Category: Summer Team Impact Project

Music and Emotions

Author(s): Omar Ahmed Alsuhaibani, Pamela Benitez, Cynthia Love

Mentor(s): Nathalia Peixoto, Bioengineering

https://youtu.be/oK0Bic28M-k

This study explored the efficacy of computer-generated music in comparison to human-composed (commercial) music among students and examined how music tempo and style related to changes in physiological signals such as heart rate and EEG signals from the brain. Participants were 6 students ranging from ages 15 to 22 years old, both male and female. During the time of testing, participants were given ten six-minute tests: one as a baseline reading of physiological signals, three tests involving commercial or human-generated music of varying tempos, and 6 tests involving the computer-generated music at different speeds and styles. Results suggested that computer-composed music is (a) positively correlated with a change in physiological signals, (b) more effective than human-composed music, and lastly that music tempo had a (c) positive correlation with gamma and beta brain waves. Results are discussed in terms of implications for students’ changes in heart rate and EEG/brain wave activity.

Hello, we are one of the groups taking part in the STIP program for the summer of 2022.
Our group consists of three members that worked on this research project over the summer. My name is Cynthia Love, serving as the project manager. I am also working alongside Omar Alsuhaibani and Pamela Benitez. The purpose of our group’s project was to explore how different styles, sources, and speeds of music affect physiological signals such as EEG signals from the brain or heart rate.
Some real life applications are to gain the ability to measure emotions, something that has not yet been easily quantifiable. Another application is to optimize an up and coming field of therapy known as music therapy, especially for those with auditory triggers. While music therapy has grown in popularity for its ability to effectively reduce feelings of anxiety and stress, it was unknown if computer-generated music was as effective as music composed by humans. This was another research question we had in mind during our experiments.
Our group hypothesized that there would be a positive correlation between music tempo and beta and gamma brain waves. We hypothesized that fast music would enhance beta and gamma brain waves, while slow music would suppress them. Our last hypothesis is that computer-generated music would be less effective at influencing physiological signals.
Materials – The equipment used for our experiments is listed on the screen. The muse headband was used to take EEG brainwaves, while the empatica wristband was used to record heart rate. The mind monitor app allowed us to process the raw EEG data gathered from the experiments. The E4 connect software was used to process the heart rate data. The Interamusi Software is where we derived most of our music from. It is a website that composes custom music and allows it to be played on a loop.
Testing – this is the type of software that we used for each data processing. The one on the left is the empatica real-time app, which was used to process heart rate data. The app on the right is the mind monitor app, which was used to process EEG data.
Methods – For this experiment, we first started with a baseline test. In this portion, participants were instructed to sit in an empty room, with their eyes closed. Lights were turned off in the room to prevent any electrical interference with the equipment used. The Muse 2 headband was placed onto the participant, as well as the E4 on the participant’s wrist. Both devices were turned on and captured the participant’s readings for 6 minutes. The next portion involved the music tests. Participants listened to each of the 9 computer-generated songs for 6 minutes each. These songs were split into two categories, computer-generated classic music, and computer-generated modern music. There were then three subcategories, known as calm, stimulus, and focused. The order in which the music was played was randomized for every single participant.
For each stimulus, the Muse 2 headband and E4 were activated at the start of each 6-minute trial. Each six-minute session was recorded through the Mind Monitor app and labeled correspondingly to the stimuli played.
For the baseline test, the four different channels both dropped leading to a decrease in Power Spectral Density with no brainwaves appearing for delta from 0.5 to 3 Hz and theta frequencies from 3 Hz to 8 Hz. This confirms the accuracy of the results interpreted as delta waves are generated from the deepest form of meditation and dreamless sleep, while theta waves are generated from learning and intuition and can be thought of as generated from beyond a conscious level. We can see an increase in activity from 8-12 Hz which indicates that there is minimal alpha waves activity occurring. Alpha waves are the present resting state of the brain where wave presence indicates relaxation and mental coordination. Frequencies of 12 Hz to 32 Hz are considered beta waves which are indicators of normal brain activity such as having our eyes open and processing information on our surroundings. In regards to our baseline testing, there are some beta waves present in the different channels which can indicate that the test subject was processing the experiment and the environment during testing. The highest activity present in the baseline testing were gamma waves. Gamma waves occur at 32 Hz and above and are considered the fastest waves out of all the aforementioned waves. Gamma waves are associated, and mainly occur, with high levels of thought and focus when conscious. In this baseline test, there were high spikes of gamma waves which indicate most likely that the subject was focused on staying still and clear headed.
With an overlook of the slow commercial music channel spectra, we can see that there is much more activity in PSD and rise in waves for different frequencies. During 3 – 8 Hz, there is no noteworthy activity for theta waves from all channels. This indicates that there was little to no learning or intuitive activity beyond a conscious level during the listening to slow commercial music test. In the 8 – 12 Hz range, there is minimal activity for alpha waves for all channels. This indicates that there was minimal mental coordination and relaxation during testing. For the 12 – 32 Hz frequency range, there is significantly higher PSD and waves from one channel than the others for beta waves. This could indicate that one area of the brain picked up more beta waves than the other channels, which is an indication that there was higher level of concentration on processing the environment they were in. In the 32 Hz and above range, there is significantly higher activity for all channels in regards to gamma waves. This indicates that the slow commercial music resulted in a high level of conscious thought and focus.
At a first look at the classic calm computer-generated music channel spectra, we can see that there is a higher activity in all the channels throughout the frequency range and PSD. During 3 – 8 Hz, there is little activity for theta waves from half of the channels but the PSD does start at a lower value than the commercial music test for all channels. This could indicate that there little learning or intuitive activity beyond a conscious level during the listening to classic calm computer-generated test. In the 8 – 12 Hz range, there is the activity for alpha waves for all channels with some channels slightly dropping in PSD while other channels have alpha waves forming. This indicates that there was minimal mental coordination and relaxation during testing. For the 12 – 32 Hz frequency range, there is a rise in PSD and waves from two channels for the beta waves. The two areas of the brain picked up more beta waves than the other channels.
Specifically, for one of the channels at low Beta (at 12-15 Hz) there is a slight formation of low Beta waves that remained consistent until it reached Beta (15-22 Hz) and higher activity was detected through the appearance of multiple Beta waves. The same can’t be said for the other two channels, where when it reached Beta, there was no increase of beta activity due to no waves appearing and the PSD slowly decaying. This can be interpreted that there was thinking occurring during testing with an awareness of self and focus on surroundings during Beta frequency picked up in two areas of the brain [NHAHealth]. In the 32 Hz and above frequency range, there is significantly higher PSD and wave activity for half the channels that proceeded to all channels when it reached above 50 Hz, in regards to gamma waves. This indicates that the classic calm computer-generated test resulted in a high level of conscious thinking and a high level of processing that is more consistent and organized, due to the firing rate of gamma waves, in all areas of the brain during testing.
In conclusion, this project achieved to explore the efficacy of computer-generated music in comparison to human-composed music among students and examined how different music styles and tempos can relate to changes in physiological signals such as EEG brainwave signals. The computer-generated music had a random pattern of behavior on the brain with the four areas of the brain. With the computer-generated music tests, after analyzing the data acquired, the four different channels were picking up the same signal and matching each other in regards to activity. This happened with human-composed music, as well, but not as often or frequently as during computer- generated music. This gave more solidity to the findings and the interpretation of the graphs. It proved that there was higher and stronger activity during particular frequencies picked up from all areas of the brain that can determine how strongly the subject was feeling. The purpose of this project was to examine if human-composed music and computer-generated music can have the same implications on human emotions and physiological signals. The results proved that there is a difference based on signal activity, PSD, and frequency of waves but more testing and analysis are required to reach a fully-formed opinion.

College of Engineering and Computing College of Public Health Summer Team Impact Project Undergraduate Research Scholars Program (URSP) - OSCAR

Personalized Intervention Using Personalized VR Recovery Rooms and Avatars

Author(s): Omar Alsuhaibani, Taylor Dinh, Elizabeth Kwari, Sara Razavi

Mentor(s): Nathalia Peixoto, Bioengineering

This project focuses on creating a personalized virtual reality (VR) safe space and avatar for individuals that are recovering from substance use disorder. HTC Vive (VR headset), Unity (Game engine), VRoid (Avatar assembly and characterization), and Blender (3D-design software) are used to create the VR safe space and avatar. The HTC Vive is calibrated and linked to Unity, and eye tracking scripts are implemented in Unity. To test the effectiveness of the VR safe space and avatar, an E4 is connected to the computer and to Unity to record the heartbeat in real-time. The heartbeat is then translated into the avatar’s animation using VRoid to simulate the emotion during particular heart rates as an indication of stress, anger, or fear.

(Slide 1)
Hello we are one of the groups taking part in the STIP program for the summer of 2022. The topic of our group’s project focused on creating a personalized VR recovery room and avatar for individuals that are recovering from substance use disorder (SUD).

(Slide 2)
And this brings us to our group’s overall goal statement, a reiteration of what was previously stated. Our group wanted to create a personalized VR safe space and avatar for individuals recovering from substance-use disorder.

(Slide 3)
The reason we decided to do more research pertaining to the topic of VR and personalized intervention has to do with the various forms of literature that are in support of VR technology as a useful method for recovery therapy.

For example, there was one study where VR was used on patients & healthcare providers in COVID-19 rehabilitation units. They found that VR helped them to cope with the isolation and loneliness they felt while quarantining during the beginning of the COVID-19 outbreak.

Another example can be seen when patients in mental health care faculties used VR technology. It helped individuals that experienced high levels of stress reduce their stress levels and maintain a calmer attitude in their daily life.

Finally, in a study that was conducted on drug abusers in a drug rehabilitation center in China, researchers found that after VR treatment, the participant’s craving for drugs was severely reduced.

For this reason, our group felt that looking more into and trying to personalize VR safe spaces and avatars would be a viable and useful method to assist individuals recovering from substance use disorder.

(Slide 4)
The design of this project was created in a temporary manner by using HTC Vive, Unity, and Blender for the development of the personalized recovery room.

(Slide 5)
Hi, my name is Taylor Dinh, I was the programmer on this project. The video you can see here is a recording of the viewport and project windows within Unity. I programmed in Unity, a game engine which runs off of C#, in order to create a VR room that can be viewed and interacted with through the HTC Vive and controllers. Our original intention was to make a therapeutic room where people with substance use disorder could go into the room and see an avatar that would mimic their heart rate using the animation controller and an Empatica. Unfortunately, we could not get this to work, however, we did make a VR room that the participant can interact with.

(Slide 6)
For the Empatica set-up, E4 streaming desktop app was used in order to keep track of the real time data streaming from the device attached to the participant using a bluetooth dongle connected to the computer
The BLE github code to connect Unity to the E4 was used in order to attempt to connect the two so that the player could see their metrics in real time

(Slide 7)
In terms of the personalized avatar, our team had initially started constructing the avatar using Blender. However, it proved to be much more challenging than what had initially been expected. While it is a viable solution, it would take time and a lot of effort to construct a model personalized to an individual’s preference.
Instead, our group looked into personalizing avatars using VRoid. This method proved to be much easier in terms of personalizing the avatar compared to building it from scratch in Blender. Creating the avatar proved to be a lot easier in VRoid as it includes several type of customizable features and can be easily imported into Blender and then Unity which is very helpful. If you are interested in learned more about creating your own personalized avatar and importing it into Blender, you can check out the full version of this video on our teams website.

(Slide 8)
In conclusion, the VR personalized intervention room and avatar that this paper has attempted to develop went successfully in building a minimal demonstration of the capabilities VR technology can have. The VR personalized intervention room model developed here integrates highly important aspects, such as head tracking and eye-tracking, to ensure that the user focuses on the intractable objects around them. In addition, the model developed had the ability to present generic soothing images in an act to calm down the users.
The purpose of this paper was to demonstrate an inexpensive method to develop a personalized recovery room and avatar with the use of VR based on heart rate.
The results were relatively similar where the personalized recovery room was able to become developed, albeit not personalized to particular users and their needs. However, the results prove that the ability to develop personalized recovery rooms that implement important personal variables (such as the users’ dogs, family homes, or family pictures) is a lot more difficult and requires a lot more time by trained professionals.

College of Humanities and Social Science College of Science Summer Team Impact Project Undergraduate Research Scholars Program (URSP) - OSCAR

Building Capacity for Indigenous Mapping & Data Sovereignty: In collaboration with the Chickahominy Indian Tribe and the Upper Mattaponi Indian Tribe

Author(s): James Condo, Adam Edwards, Domi Hannon, Sara Jefferson, Brian Jimenez, Paloma Jimenez, Maiya Justice, Guadelupe Meza-Negrete, Jasmine Okidi, Patricia Troup

Mentor(s): Jeremy Campbell, Institute for Sustainable Earth; Tom Wood, School of Integrative Studies

The Indigenous Environmental Mapping and Resilience Planning Project has worked over the summer with two of the federally recognized Native American tribes of Virginia, the Chickahominy Indian Tribe and the Upper Mattaponi Indian Tribe. As mentioned in our first abstract, Western academia has participated in an exploitative, extractive relationship with Indigenous tribes. Our project challenges the structures of settler society by aligning our research objectives with goals and values provided by the Chickahominy and Upper Mattaponi. These objectives include building tribal capacity in mapping and data sovereignty to help inform tribal environmental decision making. Honoring Indigenous knowledge is key to our project’s overall goal of Indigenizing academia, including honoring Indigenous data sovereignty. Thus not all aspects or results of our project are shown in this video, in accordance with the wishes of the Chickahominy and Upper Mattaponi. Project deliverables include a GIS training workshop for Chickahominy tribal environmental managers, a preliminary GIS database of Chickahominy and Upper Mattaponi tribal land and bioculturally significant sites, a visualization of landscape change within Upper Mattaponi tribal lands, a literature review on Indigenous data sovereignty, and a preliminary data analysis of demographic and environmental questions from the Upper Mattaponi Tribal Wellness survey

[Two undergraduate researchers stand against a white wall, and assorted photos and video from Chickahominy and Upper Mattaponi fieldwork]
Maiya: Our project, the Indigenous Environmental Mapping and Resilience Planning Project, has been working over the summer with two of the federally recognized Native American tribes of Virginia – the Chickahominy and Upper Mattaponi Indian Nation. Our work with these two tribes has been to establish environmental data baselines, using specific guidelines given to us by the tribe’s environmental offices.
Paloma: As we’ve mentioned in our previous video, maintaining tribal sovereignty over their respective data and knowledge is key to our work. This video will focus primarily on Indigenous Data Sovereignty, the GIS Map training and work we’ve been conducting over the summer, and our time with the Upper Mattaponi Indian tribe.

[Footage and images from the Upper Mattaponi PowWow interspersed with images and footage from Upper Mattaponi lands as well as GIS footage]
James: Indigenous data are defined as any piece of information that impacts Indigenous people as a group or as individuals, Indigenous lives, Indigenous cultures, and Indigenous languages (NCEAS, 2021). For example, Indigenous data may include environmental data such as geospatial or watershed information, data regarding Indigenous nations as a polity, and data about individuals such as demographic or epidemiological data (ibid.). Indigenous data are especially relevant regarding Indigenous sciences that leave further room for subjectivity and qualitative data, as well as the dissemination of Indigenous knowledge through oral histories. Sovereignty is broadly understood as rights of self-determination and self-governance without outside interference. In the context of sovereign Indigenous nations, Indigenous Data Sovereignty refers to Indigenous ownership and control of data, rather than data being owned by an outside university or government. Indigenous Data Sovereignty is critically important to enabling Indigenous environmental stewardship, collective knowledge ownership, and non-extractive data collection (NCEAS, 2021). There are many methods through which Indigenous leaders and collaborating researchers have worked to ensure long-term Indigenous data sovereignty and governance of information about Indigenous lives. Our project this summer has worked to establish and maintain IDS by deferring to leadership for the Upper Mattaponi and Chickahominy tribes in regards to data that is confidential. This has involved flexibility with internal use and delivery of data in which control lies in the hands of tribal leadership. While our team has worked to collect data, tribal leaders are able to govern the data that is relevant to the lifeways of tribal citizens.

[Footage of the Chickahominy GIS training session interspersed images and footage from Upper Mattaponi fieldwork and GIS footage]

Adam: Using GIS can be difficult to use, because GIS software has many capabilities and numerous ways to navigate the many menu options. GIS is Important to learn, because so much data today has a spatial component, and spatial problem-solving can be used in nearly every field. GIS is an important tool for analyzing important issues facing the earth today. For the GIS training with the Chickahominy Tribal members, ArcGIS Pro was used. ArcGIS Pro is a GIS software that allows you to access, explore, visualize, create maps, and analyze data geospatially. The most enjoyable part of the GIS training is showing how maps can be used to explain spatial relationships.

[Leigh stands in front of a wooded ponded area interspersed with GIS time series analysis]
Leigh: My name is Leigh Mitchell. I’m the environmental and cultural protection director with the Upper Mattaponi Indian Tribe, citizen of the Cherokee Nation. It is up to the tribe, uh, themselves to to lead, um, lead a project in a way where again their control and their use of their identity is up up to them, um, to describe, disseminate, research, um, look into, um, so while an individual tribal citizen can, um, of course you know interact and create data and kind of the the tribal sovereignty aspect is a whole new layer, um, that’s really important. Like I said kinda every step of the way we are considering,um, you know are we putting information at risk are we ensuring that, um, the information we want to be out there is out there are we sharing too much, um, how do we keep, um, internal knowledge internal, um, so when looking at environmental restoration I can I can say you know this is a sacred site or um or I can say there’s a sacred relative or a species there that is important, um, but any more you know that’s really up to the tribe to say what they want to say, um.

[Footage from the Upper Mattaponi PowWow]

Sara: At the beginning of the summer, some of our members had the chance to attend Upper Mattaponi’s yearly powwow, which had not been held since 2019. Here are some of the team members pictured with Upper Mattaponi Chief Frank Adams, enjoying the Powwow.

[Footage of Upper Mattaponi lands interspersed with footage and photos of some of the team meeting with Upper Mattaponi members]

Domi: While we weren’t able to conduct a wildlife survey in a similar vein to the one done on Chickahominy lands, we were able to meet with Leigh Mitchell, Environmental and Cultural Protection Director of the Upper Mattaponi Indian Tribe, and Reggie Tupponce, Tribal Administrator of the Upper Mattaponi. We were able to view some of the Upper Mattaponi lands, as well as the environmental risks in the area, such as a kitty litter factor and extensive sand and gravel operations. Some of our members worked on doing a preliminary data analysis using data from a Tribal Wellness Survey conducted by the tribe in 2021; data from this survey contains valuable insights into Tribal Members’ observations regarding the increased intensity of storms and droughts in the region. Our team has also partnered with Upper Mattaponi on important mapping projects, including the documentation of recently reacquired tribal properties using ArcGIS and Google Maps. Some of the featured data that we’ve developed include images of landscape and watershed change over time, as well as changes in air quality from 1998-2020. We’ve worked with Tribal Members and administrators to integrate this information into an accessible and secure database that can inform tribal decision-making for years to come.

[Three undergraduate researchers stand against a white background, interspersed with footage from Chickahominy fieldwork]

Domi: The work we’ve been doing this summer is a part of efforts to decolonize our work here at George Mason. What we’re doing isn’t about controlling research objectives, data, or access to data, but capacity building with Native tribes of Virginia to assist them in managing their own lands and cultures.
Guadelupe: We hope you’ve gained an understanding on valuing Indigenous Sovereignty and knowledge in academia and building and maintaining a reciprocal, additive relationship with Native peoples across Turtle Island and the world.
Patti: The relationships we’ve been building with the tribes are just as important as the data we’ve been working on. We would love to expand our work to other tribes in the Chesapeake Bay area, and that this sets the foundation for future work between George Mason and the Native folk whose land we continue to occupy.

[Thanks and credits set against PowWow footage as well as team photos and footage of both Chickahominy and Upper Mattaponi lands as music from the Upper Mattaponi PowWow plays]

College of Humanities and Social Science College of Science Summer Team Impact Project Undergraduate Research Scholars Program (URSP) - OSCAR

Supporting Tribal Sovereignty & Environmental Governance: In collaboration with the Chickahominy Indian Tribe and the Upper Mattaponi Indian Tribe

Author(s): James Condo, Adam Edwards, Domi Hannon, Sara Jefferson, Brian Jimenez, Paloma Jimenez, Maiya Justice, Guadelupe Meza-Negrete, Jasmine Okidi, Patricia Troup

Mentor(s): Jeremy Campbell, Institute for Sustainable Earth; Tom Wood, School of Integrative Studies

The Indigenous Environmental Mapping and Resilience Planning Project has worked over the summer with two of the federally recognized Native American tribes of Virginia, the Chickahominy Indian Tribe and the Upper Mattaponi Indian Tribe. Historically, Western academia has participated in an exploitative, extractive relationship with Indigenous tribes. Conversely, our project challenges the structures of settler society by aligning our research objectives with goals and values provided by the Chickahominy and Upper Mattaponi. These objectives include establishing environmental data baselines for their ancestral lands, such as recognition of Indigenous food sovereignty and biodiversity mapping. The Indigenous Environmental Mapping and Resilience Planning Project is grounded in collaborative practices and support for Indigenous environmental stewardship while upholding Indigenous data sovereignty. This project is the first step in establishing long-term reciprocal relationships between George Mason University and the Chickahominy and Upper Mattaponi. Project deliverables for the food sovereignty portion include an Indigenous gardening booklet, a presentation on composting, and signage for a community garden. Project deliverables for the wildlife assessment portion include an ethnobiology booklet covering bioculturally significant species and a breeding bird survey and an invasive species survey conducted on recently repatriated Chickahominy ancestral lands.

Dana: Okay the tribe is headquartered in Charles City County, Virginia at an area known as Chickahominy Ridge. My name’s Dana Adkins, I’m a Chickahominy Tribal Citizen and the tribe’s environmental director since 2019.

Sara: Soo my name’s Sara Jefferson, I’m a member of the Chickahominy Indian Tribe, and I’m also a student here at Mason studying psychology, and I’m also a team member of this STIP project going on here down in Charles City.

Dana: Some of the deliverables we are hoping to get from this project are maybe introducing us to some methods that can help us to see what lands we have, understand what’s there, what’s not there, and how to best utilize those lands so that as we grow, we can have growth that’s environmentally friendly.

Sara: To me, sovereignty means kind of taking back what was already ours, if that makes sense, being that we were one of the first tribes to have direct contact with the colonizers back in 1607, and so much of our culture was lost and we weren’t able to get all of that back, but now that we are a sovereign nation, we are able to get these tools that we once lost back in an easier manner, if that makes sense.

[A group of four undergraduate researchers are standing against a white wall]

Domi: Our project, the Indigenous Environmental Mapping and Resilience Planning Project, has been working over the summer with two of the federally recognized Native American tribes of Virginia – the Chickahominy Tribe and Upper Mattaponi Indian Tribe. Our overall goal has been to establish environmental data baselines for the two tribes, using specific guidelines given to us by the tribe’s environmental offices.

Guadelupe: Since the beginning of our relationships with the tribes, we’ve focused on maintaining tribal sovereignty over data and knowledge, as historically, Western academia has participated in an exploitative, extractive one-sided relationship with Indigenous tribes and their knowledge across North America, also known as Turtle Island.

Patti: Having a reciprocal relationship built on trust and honoring Indigenous knowledge is key to our project – we want our work with the Chickahominy and Upper Mattaponi to be additive and beneficial to the tribes. By participating in this work, we are attempting to decolonize academia and environmental studies.

Adam: An important part of our research has been honoring Indigenous data sovereignty – that means not everything we’ve worked on over the summer we can show, as the tribes do not want all data available for access to non-tribal members.

[Footage of the Powhatan River]

Dana: The site we’re standing on now is a piece of property that we have named “Chickahominy on the Powhatan.” Uh, this would have been a traditional village site, uh, the rivers uh in the state served as great village sites for the tribes. Uh, the tribes in this state are actually known as tributary tribes because of our villages being along our namesake rivers. Uh, this particular river would have been called the Powhatan River, pre-contact. Uh, post-contact it was renamed the James River.

[A single undergraduate researcher stands with a glass door behind her, with different screenshots interlayed over her at different points in time]

Jasmine: Indigenous and settler scholars Eve Tuck and K. Wayne Yang explore how the metaphorization of the term “decolonization” has strayed the operational form of the word away from the objectives of Indigenous rights and sovereignty. This metaphorization may look like conflating decolonization with other social justice movements. This metaphorization may also manifest in “settler moves to innocence,” or methods of alleviating settler guilt from Indigenous erasure and forced assimilation. This is achieved by creating distance from the settler to the system of settler colonialism without actually absolving themselves of oppressive power or returning stolen land. Rather than falling into these inappropriate comparisons or settler moves to innocence, it is important that we recognize decolonization as a distinct project on the repatriation of Indigenous land and lifeways. For our project, that means challenging the structures of settler society by supporting Indigenous environmental stewardship and ensuring Indigenous sovereignty over data and, crucially, guiding our work with the stated goals and values of the respective tribes with which we are working.

[Three different voice overs over footage of Chickahominy lands as well as the Potomac Heights Community Garden]

Paloma: Over the summer, we have been working closely with Dana Adkins, the Tribal Environmental Director of the Chickahominy. He’s been working on establishing community gardens for the tribe to ensure healthy living for tribal citizens. With Dana, we’ve been working on establishing food sovereignty for the tribe through these community gardens as well as creating a curriculum on how to teach food sovereignty and the issues of food deserts and accessibility.

Patti: Food sovereignty is the right for peoples, especially native peoples, to have access to healthy, environmentally sound, traditional food. This includes both the ability to cultivate, harvest, and consume those foodways. Food sovereignty does not listen to market demands, but instead, focuses on the need of the people of the respective culture. Food sovereignty is being used across Turtle Island and worldwide to combat food deserts. Food deserts are when an area has a rate of poverty exceeding 20%, with rural folks being unable to access a large grocery store within ten miles of their home. The area in which the majority of the Chickahominy people live in is considered a food desert. The community garden and eventual future gardens established by Dana will help combat the lack of access to healthy, in-season food.

Maiya: One of the ways our team has worked on the community gardens is through research at George Mason University’s own community gardens, maintained by the Office of Sustainability. Guided by Doni Nolan, the Sustainability Program Manager for the Gardens and Greenhouse, we gained insight and ideas to present to Dana on working on the garden to best benefit the citizens of the Chickahominy nation.

Dana: Well we received a grant from the EPA this year to start a community garden. We as a tribe have gotten away from gardening, as a general rule. Like most of society we live fast paced lives and we rely more on fast food and prepared food, rather than stuff than we would have cultivated ourselves out of our gardens and cooked at home. The thought behind having the community garden was to kind of reintroduce some of those gardening techniques to our youth before it’s lost. We also have, like other communities, we have seen an increase in diseases such as diabetes and we feel this is probably attributed, again, to too much fast food, not enough of the foods that we grow ourselves, that are generally healthy, uh foods that you’d want to consume.

[An undergraduate researcher walks along a green path and his voice is overlaid with footage from Chickahominy lands]

For the Chickahominy, we also began collecting data on the wildlife on the lands that currently have regained ownership over. The two lands, Chickahominy on the Powhatan, or James River, and Mamanahunt on the Chickahominy River, were both recently (re)acquired through purchase by the Chickahominy. We participated in a bird survey, led by Dr. Wood, and collected data on the birds, other animals, insects, as well as the flora growing on the Chickahominy properties. We also conducted basic water monitoring from the Powhatan, or James River, and the Chickahominy River as well. One of our deliverables will be an ethnobiology booklet focusing on a select number of native flora and fauna.

[Footage from the Upper Mattaponi Powwow of Women’s Fancy Shawl Dancing]

Domi: Our work with the Chickahominy tribe of Charles City, Virginia in food sovereignty and wildlife data collection is only part of what we’ve been doing this summer – check out our second video to learn more about our mapping efforts with both the Chickahominy and the Upper Mattaponi Indian Tribe!

College of Humanities and Social Science College of Science Summer Team Impact Project

Role of the Urban Built Environment in Breast Cancer Mortality Health Disparities

Author(s): Kai Barner, Ha Dao, Abigail Kokkinakis, Alexandra Merida, Amanda Webber

Mentor(s): Taylor Anderson, Geography & Geoinformation Science; Travis Gallo, Environmental Science and Policy; Mariaelena Pierobon, Biohealth

Breast cancer is the second-leading cause of cancer-related death among women in the United States, but mortality rates vary across the population. Previous studies use individual and county level data to examine the association between breast cancer mortality and various socio-demographic and environmental variables. However, global regression models assume spatial stationarity meaning that associations between explanatory variables and breast cancer mortality are the same across geographic space and scales. Therefore, the objective of the study is to use county-level data that describes the social and built environment across the contiguous United States to explain breast cancer mortality rates, as reported in the Surveillance, Epidemiology, and End Results (SEER) database, using a multi-scale geographically weighted regression model (MGWR) that accounts for spatial heterogeneity. We compare our MGWR model with a baseline global linear regression model (OLS). The MGWR outperformed the global linear regression model with an adjusted R2 of 0.91, explaining about 43% more variance than the OLS. The R2 for each county is high in the western part of the US and decreases towards the east. In comparison to global linear regression, MGWR had the same overall trends in terms of relationships between explanatory variables and mortality. For example, as mammogram screenings, health food index, and primary healthcare physician ratio increased, breast cancer mortality decreased. However, MGWR reveals spatial heterogeneity associated with the magnitude and direction of each relationship across counties. Such an approach allows for greater consideration of where certain variables are most influential in breast cancer mortality allowing for location-specific interventions.

Top of Form

Abigail G Kokkinakis0:09

Hello everyone. The objective of this study is to examine the association between the urban built environment and sociodemographic variables and breast cancer mortality using an approach that will capture spatial heterogeneity and those associations. According to the American Cancer Society, breast cancer is most common cancer in American females. One in eight females will be diagnosed with breast cancer and their lifetime, and one in 39 females will die from breast cancer. A lot of studies look at individual level, social demographic data, and regression techniques. However, these regression techniques assume spatial stationarity which ignores local heterogeneities and associations between variables and breast cancer mortality.

Ours created a US model focusing on urban built environment variables without the limitations of most global regression techniques.

We collected Seer data from the most recent five-year interval, which was 2015 to 2019. Note the white space in the map shown. These are the counties that did not have data provided, which was roughly 1/3 of the data.

Ha Dao1:10

Due to privacy reasons, counties with the count of deaths less than 10 were omitted by SEER. Therefore, we decided to assume these missing counts as 9 and calculate the crude rate based on this. Moving forward with data collection and preprocessing. In the beginning of our research, 65 urban-built and social-demographic breast cancer determinants were obtained. Throughout the process of data cleaning and data wrangling, log transformation and scaling were applied on selected variables to reduce skewness and normalize the range of independent variables. Upon running Pearson correlation and Variance Inflation Factor, we removed variables that were highly correlated so we can avoid multicollinearity in the regression model. Lastly, the leap and bounds algorithm was used to find the variables that explain the models best which you can see on the screen here

Amanda A Webber1:59

Our model contained 18 variables. For the sake of time, we’re showing the three most significant variables as well as the variables that have no significance. The three most significant variables are access to mammograms, access to primary care doctors, and percent of population that is uninsured. The variables that are not significant based on their P value are textile manufacturing, commute, radiance, and unemployment.

You can see from this map of our county residuals that predictive accuracy varied across counties and appears spatially correlated. The R square value reflects the amount of variation in the data set that is explained by our model. On a scale from 0 to 1, this models R Square explains roughly 50% of the variation within the data.

Kai Barner2:47

Big picture, this project wanted to account for spatial heterogeneity. A primary limitation of OLS regression is that it assumes that each explanatory variable has the same relationship with the dependent variable across the entire study area. So when one unit change of an explanatory variable occurs, that results in the same change to the dependent variable, no matter the location. Multiscale Geographically Weighted Regression – MGWR – takes location into account by taking the concept of neighborhoods from standard Geographically Weighted Regression and allowing their size, or bandwidth, to vary between different variables in the data set. Multiple scales reflect diversity in distribution and extent of influence.

This project examined data at the county level, so when analyzing the influence of a variable on an individual county, the bandwidth took into account that many neighbors at the same time. There are just over 3100 counties in the continental US, and we can see that some of the neighborhoods actually encompassed all of them. But at the other end of the spectrum, a small bandwidth indicates that the spatial process changes quickly from location to neighboring locations. And we see this with food index, exercise, access to primary health care, and mammograms.

Where our OLS model had an adjusted R-squared of 0.478, our MGWR model explained about 44 percentage points more variance, with an adjusted R-squared of 0.918.

Here we have our MGWR results for mammograms. The mean coefficient is negative 0.14, so the standard behavior nationwide is an inverse relationship. As the number of mammograms increase, breast cancer mortality decreases and we see this norm in cornflower blue. As we move through the purples into magenta, this negative correlation becomes even stronger. But on the other hand, areas in the cyan extend from having a weaker negative correlation to actually having a positive coefficient. In these areas as mammograms increase, breast cancer mortality also increases.

These local differences warrant further examination of other variables and other factors of underlying influence, alluding to the local complexities behind trying to improve breast cancer health outcomes. There is no one-size-fits-all solution.

Alexandra Diaz Merida5:05

Another variable that we looked at was access to exercise, which measures how close an individual is to a location for physical activity. This can be either parks or recreation centers in this map we see that in general there is a negative correlation, which means that the higher the access to exercise opportunities, there is a decrease in breast cancer mortality. Looking at these graphs, we can see which counties need more access to exercise in order to decrease breast cancer mortality.

Similar to access to exercise, we also looked at food index, which is described as how close an individual is to healthy foods and the and the ability to access due to cost barriers. It is also negatively correlated, which again means that the higher the food index, there’s a decrease in mortality. These maps will help inform local governments about breast cancer mortality rates and how improving these variables can decrease mortality.

In summary, MGWR explains the results better than OLS for spatial heterogeneity, providing better explanations for how urban built environment variables affect breast cancer mortality. Thank you.

Kai Barner6:12

Thank you.

Abigail G Kokkinakis6:13

Thank you.

College of Education and Human Development College of Engineering and Computing College of Humanities and Social Science Summer Team Impact Project

Physiological Data Associated with Linguistic Racism

Author(s): Pamela Benitez, Elizabeth Kwari, McKenna Olsen, Susan Pham, Sara Razavi

Mentor(s): Nathalia Peixoto, Volgenau School of Engineering

This project focuses on testing the correlation between linguistic racism and anxiety using physiological data. In this experiment, participants consisted of two groups, native English speakers (NES) and non-native English speakers (NNES). Each group had 5 participants, with a total of 10 participants. In the pre-experiment phase, participants were required to fill out two surveys. The first survey was on social anxiety and the second survey was on their emotional state. In addition, participants were exposed to a 25-minute video. The video was organized into 4 sections: (1) listening to 10 min. of relaxing music, (2) 10 min. Relating to linguistic racism, (3) second time taking their emotional state survey, and (4) listening to 5 min. of relaxing music. The hypothesis is that when NNES are exposed to linguistic racism, anxiety-like symptoms will occur such as an increase in heart rate and body temperature. Another part of our hypothesis is that non-native English speakers will be more stressed or sad after watching the linguistic racism video. To find this correlation, quantitative data was collected using E4s and Muse 1. Other data analysis will include participants’ survey results before and after watching the linguistic racism video. The qualitative data results that will be more focused on is comparing the participants’ results in their emotional state, specifically their changes before and after watching linguistic racism.

Hello everyone, we are one of the groups taking part in the STIP (Summer Team Impact Project) program for the summer of 2022. Our group consists of five members that helped with research and running experiments throughout the entirety of the summer. These members include Susan Pham, Elizabeth Kwari, Sara Razavi, Pamela Benitez, & McKenna Olsen.

While there are many that view being different as something good, being unique, there are just as many people that also degrade others for being different too. Words like foreigner, second class, alien, etc. are just some examples of direct forms of the prejudice that many had experience in today’s society. However, it does not just stop at direct comments. There are numerous ways that society subtly expresses racism whether it be certain words and actions, but not enough research has been done to show that this an actual problem. Our group’s project hopes to bring more awareness to the topic of linguistic racism and show that such subtle forms of racist words and actions do have a significant impact on an individual’s psyche, shown through their physiological signals.

Before we begin an overview of our group’s research, it is important to first understand what exactly linguistic racism is.

Linguistic racism is a type of prejudice individuals may have towards others based on the way they speak, accent or dialect they have, or their repertoire. Linguistic racism can be expressed in a variety of ways but is often done through the form of explicit verbal attacks such as mocks, slurs, and name-calling.

Not only does linguistic racism affect an individual by making them feel embarrassed because of their way of speaking, but it can also affect them on a deeper level, going as far as affecting the individual’s mentally. There are studies to suggest it may lead to increases in anxiety, depression, and insecurity about their language skills. Because of this, victims of linguistic racism may feel that they have reduced opportunities for self-representation and require increased effort to try and mask their accents.

And this brings us back to our group’s overall objective and research question relating to this project. Our objective is to investigate experimental methods to capture physiological data related to anxiety and linguistic racism. Our research question is how did our team test the correlation between linguistic racism and anxiety?

Based on some previous studies that have researched on detecting social anxiety with E4 data. E4 Empatica watches were used to collect physiological data and only heart rate, electrodermal activity, and skin temperature can be used as social anxiety indicators.

Our first trial procedure consists of a few steps. First, participants wore E4 watches while watching a video. Second, the video itself had three sections, the first section of listening to soothing music for 10-minute, second session of watching a 3-minute video on racism, then listening to soothing music again for 10-minute. Lastly, E4 data was synched to the E4 connect website.

The results shown from the first trial did not capture what we were looking for. Our hypothesis is that heart rate will increase when the participant is exposed to linguistic racism. In both of these graphs, both the participants from the non-native English speaker group showed a consistent heart rate pattern the whole time watching the video and listening to relaxing music. It is important to note that the decrease in skin temperature was due to the room temperature the participants were in.

Based on the first trial methods errors, our final trial procedure is much more structured and thorough. First, participants wore E4 watches and the muse 1 headband. Next, participants took 2 surveys on personal anxiety and emotional level. The second step included the participant listening to 10 minutes of soothing music, then watching a 6-minute video on linguistic racism, and then listening to 5 minutes of soothing music once again. Participants pressed the E4 button to mark their event of transitioning between listening and watching the video. Afterwards participants retook the emotional level survey.

Results from the second trial show that when the participant is watching the 6-minute video about linguistic racism, there is a slight increase in heart rate.

Tying it back to our hypothesis, it is predicted that participants would show an increase of heart rate when exposed to linguistic racism. What the researchers are looking for is a correlation between an increase in heart rate and frontal cortex activity. In the E4 graph, during the linguistic racism video section, there was an increase in heart rate. With the muse graph, we can associate not only that the heart rate was increasing, but the participant’s frontal cortex was active when watching the linguistic racism video. This correlation can show that the participant was either angry, anxious, or stressed.

Some issues we have encountered were technical related problems of data collection. Some participants showed weaker physiological responses and that the Muse 1 headband had issues streaming onto the muse app. Another issue is that the first trial setting had a lot of background noises and the room temperature would be on the cold side. Lastly there was the issue of lack of relatability. Some participants in both the native English-speaking group and non-native English-speaking group showed no reaction when watching the linguistic racism video.

Here is our bibliography

A Special thanks to George Mason University and the OSCAR Office for this research opportunity. Thank you to all the team members who have worked hard in this project, and Susan, who is the project manager of this group. Special thanks to Dr. Zhang and Dr. Park for supervising us and thank you to Venkat and Nathalia for supporting us.

College of Humanities and Social Science College of Science Summer Team Impact Project

Investigating the Reliability of an Empatica E4 for In-the-Field Experiments

Author(s): Pamela Benitez, Susan Pham

Mentor(s): Nathalia Peixoto, Electrical and Computer Engineering Department

E4 Empatica watches are a helpful tool that allows researchers to capture several data points with real-time streaming and visualization graphs. These devices are currently validated under non-movement conditions, however, few studies note that there is little data on how accurate an E4 watch is when participants are not sitting still or are in ambulatory settings. As a result, our team decided to test the accuracy of Empatica (E4) watches over long periods, and when performing hard physical activity. Participants wore the watch for 8 – 12 hours daily, for 5 days. During these 5 days, participants followed their routine and performed tasks such as carrying groceries, eating, and exercising. Once five days had passed, researchers pulled HRV data points consisting of 5-minute sessions from each day-to-day trial. By looking at the data points in Microsoft Excel, and comparing them to the premade graphs the E4 Connect website offered, researchers were able to quantitatively count the number of times the E4 did not accurately read the HRV data. Out of 300 data points, the E4 stopped working approximately 146 times. The results of this experiment ultimately indicate that the E4 is not a practical tool to use underneath motion conditions.

Hi everyone, welcome to our STIP project for summer 2022. This summer, Susan Pham and I worked together to determine the reliability of an E4 for in-field experiments. We hope you enjoy our presentation.

So our project centered around determining the accuracy of an E4 watch under strong motion conditions. We know that an E4 is quite reliable when a participant is staying still, however when we were conducting our literature review for other projects, we noted that many studies suggest that there is not enough research to prove that the E4 is reliable for experiments where the participants move around, or perform strenuous activities. So we asked the question, how reliable is it when a participant is in motion? This summer, our to see if the E4 still maintains its gold standard status for experiments that involve strong movements, such as exercising or simply wearing an E4 in day-to-day activities.

So to start off, what is an E4? An E4 is a wristband that measures several physiological indicators of stress in real-time, including electrodermal activity, which is related to sweat gland activity. Blood volume pulse, which helps the watch derive the heart rate variability (which is basically just a measure in variation between each heartbeat), and your external skin temperature. An increasement of any of these signals would give an indication that a person is stressed, or emotionally aroused. Here’s a quick demonstration on what the E4 looks like, and how it works.

This is the E4. As you can see, it’s quite a bulky watch. To use it, first, you remove the back charging panel. When you want to turn it on, you press the small circular button that’s on the front portion of the watch. This should cause the watch to emit blue blinking light. This means that the E4 is in discovery mode, and it’s ready to be paired to the streaming app. To put it on, you just wrap the band around your arm and make sure the sliver nodes press against your wrist firmly. Let’s see exactly how the watch connects to the streaming app.

Let’s check out the streaming app for the E4. As you can SEE, the app is called E4 realtime. To connect to the watch, we click on connect e3 and start streaming. Once we do that, we confirm which watch we want to use. The app will then show its acquisition of the physiological signals. As you can see, it takes a few seconds to get the app properly connected to the watch, and for it to begin working. It takes a bit longer, around 7 seconds or so, for the watch to accurately determine the Heart rate variability. The e4 can be a little finicky in determining this, which is why it does take a few seconds. However, if we press on the back button, we’ll be able to see other physiological indicators that we talked about previously, such as eda, temperature. We can even see the duration of the session, and how much battery life the watch itself has. If we want to stop streaming, we simply click on the stop streaming button. We click on yes, and as you can see in the sync section, the app states that the data is uploading. Once it reaches 100 percent and states the data was uploaded, this means that we’ll be able to see our data on the E4 Connect website later.

So now that you have a good idea of what the E4 looks like, and how it works, it’s important to know the mythology we used for our experiment. we decided to see if we could quantitatively determine the accuracy for the E4, when a participant is in movement. To do this, we compared the data gathered from an experiment where participants are sitting still and watch a video while wearing the e4, to an experiment where the participants are actively moving in day-to-day life and are wearing the e4 for a period of 8 to 12 hours .

Here’s a data set from both of our experiments. On the right is the experiment where the participants were sitting sill, whereas on the left is when participants are in motion. As you can see, there is a clear difference. The data graph from when the participant is sitting still, which is on the right, is consistent the heart rate is clear and steady.
In contrast, the heart rate when the participant is going about their day-to-day life is shaky and inconsistent. The long flatline indicates that the E4 was not able to read the HRV correctly, and the sudden decreases in heart rate are the result of the E4 not being able to connect to the streaming app, most likely due to motion. Just by looking at this, it’s clear, that the E4 is not very reliable when the participant is moving.

To quantitatively determine how accurate the E4 was for our motion experiments, we looked at the raw HRV data for the day-to-day experiments. The total session lasted an hour, but we randomly chose a section of 5 minutes from the total hours. From those 5 minutes, we looked at the raw HRV, and the E4 graph, and counted how many flat lines and zeroes there were in that duration of 5 minutes. We counted these flatline and zeroes as these would have given indications that the E4 had stopped accurately recording. We then added the total number of flatlines and zeroes and divided that by the total number of data points, which was 300. Performing these calculations gave us an accuracy rate of 46%, which means that this isn’t a device you want to use if you’re going to perform any experiments with hard physical activity.

So undeniably, E4 watches are useful research tools that allow researchers to capture physiological signals accurately. However, based on our test results, the watch gives too many unreliable readings when the participant is in motion. This has allowed us to conclude that the E4 is not the ideal equipment to use in experiments that involve long durations of wear, or that involve hard movement.

Here is the bibliography for this project on the reliability on empatica E4 watches for in-the-field experiment

Lastly, Thank you to George Mason’s OSCAR office for allowing us to work in this research opportunity. Thank you to Dr. Chaplin for supervising us. And Thank you to Venkat and Nathalia for supporting and guiding us throughout this summer’s program.

College of Engineering and Computing Honors College Summer Team Impact Project

Robotic Mirror Therapy Exoskeleton

Author(s): Omar Ahmed Alsuhaibani, Franklin Farrel, German Kuznetsov, Elizabeth Kwari, Justin Matthews, Sara Razavi

Mentor(s): Nathalia Peixoto, Engineering

Hemiplegic cerebral palsy (CP) is a type of unilateral CP that causes paralysis on only one side of an individual’s body. While there is no cure for a disorder such as hemiplegic CP, there are several technological solutions available that can assist these children in maintaining balance and improving muscle movement. However, such methods do come at a cost, one that not all people are able to afford. Our team’s project for the Summer 2022 STIP program is focused on creating a robotic mirror therapy-based exoskeleton for children with hemiplegic cerebral palsy – the second most common form of CP that affects the sensorimotor function of the limbs on one side of the body. The overall design of the exoskeleton includes one arm that is motor-based and the other arm that is sensor-based. Our team chose robotic mirror therapy as the subject of our exoskeleton because it shows promise in assisting individuals with hemiplegic cerebral palsy, helping to improve the motor functions pertaining to the impaired side of their body. For this project, our team utilized cost-effective materials, including 3D printing parts, to create our robotic mirror therapy exoskeleton. This is to ensure that all people in need of such an exoskeleton would easily be able to obtain and afford one without struggle.

Cerebral palsy is the most common motor disability to occur in childhood often characterized with poor muscle movement and balance.

According to US studies, about two to three children out of every 1,000 are born with cerebral palsy averaging to about 10,000 babies born each year that will develop CP.

One type of unilateral CP is known as hemiplegia which is caused due to damage of the spinal cord or brain. Hemiplegic CP is usually characterized by paralysis on only one side of an individual’s body.

While there is no cure for a disorder such as hemiplegic CP, there are several ways that individuals can work to strengthen and gain better control of their affected muscles.

One way involves mirror therapy, a commonly used form of physical therapy utilized by recovering stroke patients.

While on a fundamental level some may consider mirror therapy to be meant for different types of physical therapy, it does show promise in helping individuals with hemiplegic cerebral palsy as it helps to improve the motor functions pertaining to the impaired side of their body.

However, simply simulating the motor function movement of an individual’s impaired side can only do so much. What CP patients need is a way to successfully train their impaired muscles through actual action, using repetitive, passive rehabilitation exercise.

Our team’s project for the STIP program involves creating a robotic mirror therapy exoskeleton that will drive the impaired arm of children with hemiplegic CP in response to the movement of their unimpaired arm.

The arm that our team has created allows for the planar flexion and extension of the participant’s elbow. The left arm is the “sensorized” arm that is being driven by the participant’s good arm, while the right arm is the motorized arm that mirrors the “sensorized” arm and moves the impaired arm with the help of a motor.

Both devices have potentiometers to sense the arm position, and a single microcontroller that bridges them together. The design of the device primarily consists of an armrest to hold the participant’s arm that would rotate at the elbow to extend the arm away from the user’s body. A hub under the elbow includes the necessary components to accomplish the rotation – bearings to provide smooth rotation, a potentiometer to sense the current arm position, as well as a motor in the slave device to acuate the motion.

An atmega328p based Arduino microcontroller was used to control the system of the exoskeleton. The motors were driven by a L298N H-bridge motor driver that has a limit of 48V with a peak current of 4A, and can be controlled by a 5V input signal. The motor driver also allows for current sensing, which can be utilized for overcurrent protection.

To assemble the arms together, our team used V-slot linear rails to allow for a flexible system where components such as forearm rests, handles, or other supports can be mounted to the arm assembly without having to redesign the entire assembly.

The control code for the microcontroller was written in C++. Running in a loop, the code would check the position of the potentiometers in both devices, check the current drawn to the motor, then calculate the desired input to the motor. If the desired position was outside of reasonable bounds, or the current to the motor was too high for too long, the motor would simply be stopped to prevent damage to the system.

Overall, the final design of the exoskeleton did function as was expected, successfully being able to move both arms together, mirroring the movements of each other. Our group had hoped to be able to add a wrist supination and pronation movement to the exoskeleton, but due to time constraints we were only able achieve the planar flexion and extension during the course of the summer. However, we do have CAD models demonstrating our group’s design for the wrist movement, showing that it is a viable option for future research, easily being able to be attached to our group’s current prototype of the exoskeleton thanks to the flexibility of the V-slot linear rails used for the arms design.

In the end, while our exoskeleton does successfully work and did have minimal costs, there is much that can be improved for future research. Both through design and types of materials, we can continue to improve and create more efficient exoskeletons for children with hemiplegic CP.

College of Education and Human Development Summer Team Impact Project

The Effect of Age and Years of Service on Firefighter Fitness

Author(s): Arasta Wahab

Mentor(s): Joel Martin, Marcie Fyock-Martin; Kinesiology

C:UsersarastOneDriveDocuments9. Summer 2022ResearchPoster Presentation.mp4

The firefighter (FF) profession is a demanding job that requires cardiorespiratory fitness, muscular strength, and endurance. Evidence indicates that muscular fitness and body composition are related to the ability to perform their occupational duties. The purpose of this study is to assess the relationship between age and years of service on FF fitness. 96 professional firefighters volunteered for the study. Body composition was assessed via bioelectrical impedance analysis. The fitness assessment was 30 minutes and consisted of pull-ups, push-ups, curl-ups, and 3-minute step test. In the regression model, examining influence of age and years of service on pull-ups was statistically significant (R2 = 0.04, F(2, 269) = 6.104, p < 0.01). The regression model for pull-ups reveals that years of service was a significant predictor (β = -0.08, p < 0.001). The model investigating the influence of age and years of service on curl-ups was statistically significant (R2 = 0.03, F(2, 269) = 3.625, p < 0.03). The regression model for curl-ups shows that age was a significant predictor (β = -0.35, p < 0.03). For push-ups, the regression model was not significant for age and years of service (R2 = 0.01, F(2, 269) = 1.605, p < 0.20). The regression model for step-ups was not significant (R2 = 0.004, F(2, 269) = 0.556, p < 0.57). The model for body fat percentage was statistically significant (R2 = 0.04, F(2, 269) = 5.489, p < 0.01). The regression model for body fat percentage reveals that age was a significant predictor (β = 0.77, p < 0.001). There is a decline in the number of pull-ups, curl-ups, and increase in body fat percentage. These findings suggest that promoting FF fitness may help address the decline in body composition, cardiorespiratory fitness, and muscular fitness that comes with age and years of service.[/expand] [expand title="Audio Transcript"]Hello, my name is Arasta Wahab, and I am a senior at George Mason University majoring in Kinesiology. This summer 2022 I was involved in OSCAR's Impact Project where my supervisors, peers, and I conducted research on firefighters. My poster is about ‘The Effect of Age and Years of Service on Firefighter Fitness.' To introduce my topic, the firefighter profession is a physiologically demanding job that requires cardiorespiratory fitness, muscular strength, and endurance. Due to the high physical nature of this occupation, it may be advantageous to have greater levels of fitness. Consequently, the National Fire Protection Association has placed a minimum standard of 42 ml/kg/min for cardiorespiratory fitness. Despite these recommendations, about 70% of fire departments do not require firefighters to meet this level of aerobic capacity. Furthermore, current evidence indicates that upper body fitness, and body composition are all related to the ability of firefighters to perform their occupational duties. Better understanding the effects of age and years of service on firefighters may lead to improved fitness and occupational performance. The purpose of this study is to assess the relationship between age and years of service on firefighter fitness. For methods of retrospective studies, the total sample of participants were 96 firefighters. The demographics consisted of all males, a median age of 52 years old, years of service of 9 years, and a body fat of 26%. For fitness parameters, body composition was assessed via bioelectrical impedance analysis. The duration of the fitness assessment was 30 minutes and consisted of pull-ups, push-ups, curl-ups, and a 3-minute step test. For statistical purposes, linear regression models were used to examine the influence of age and years of service on fitness measures. Looking at the results table, it displays the effect of age and years of service on firefighter fitness. The variables consisted of body fat percentage, pull-ups, push-ups, curl-ups, and step-ups. Below are the models of age and years of service. In the regression model, examining influence of age and years of service on pull-ups, curl-ups, and body fat percentage was statistically significant. The regression model for push-ups and step-ups were not statistically significant. In conclusion, as these individuals progress into their career, there is a decline in the number of pull-ups, number of curl-ups, and increase in body fat percentage. However, due to the low variance there may be other variables that may influence firefighter performance. These findings suggest that the promotion of firefighter fitness may help address the decline in body composition, cardiorespiratory fitness, and muscular fitness that comes with age and years of service. With further research, the effects of age and years of service on firefighter fitness can be used to target their specific needs. Guidance and implementing health programs may provide a call to action for engagement in physical activity. And here are my references for the articles I have used. Thank you and I hope you enjoyed my presentation.[/expand]

College of Visual and Performing Arts Summer Team Impact Project

Recovery Room

Author(s): Taylor Dinh, McKenna Olsen

Mentor(s): Nathalia Peixoto, College of Science

https://www.youtube.com/watch?v=G5AGI_-VDfw

Recovery for patients with substance use disorders (SUD) is a long and difficult path. Treatment, and encouragement to stay committed to recovery is also difficult to find and afford. When there are so few low-cost treatments and coping mechanisms for recovery, the road to recovery can be bleak, especially when the possibility of recovery seems like a pipe dream. We, the researchers, wish to create a virtual space where people suffering from SUD can be reassured of their doubts in themself and in the concept of recovery. The “room” for support that we created is a virtual space that is a visual novel. A visual novel is traditionally a term used in the video game industry, used to describe a video game that is mostly text based with user input in order to change the story. We used a visual novel engine called Renpy in order to make this recovery room. Renpy uses Python as its coding language, and it simplifies text and string display inputs so that the player can simply click on the window to read forward or move backwards in the text. We utilized the button system to be able to add reassuring quotes to each option of distress (categorized by the type of problem). We can categorize this visual novel we created as a “serious game.” Serious games are games meant for educational purposes, or with the intent of being used in the field.

Hello, my name is Taylor Dinh, I’m a part of the George Mason Summer Team Impact Project for 2022. The project that I chose to work on this summer is a serious game in Renpy. I worked on the programming aspect of this project while my partner, McKenna Olsen, worked on the website and literature review. Our project was created in order to try to create personalized digital spaces that can give participants words of encouragement in order to reaffirm their commitment to recovery from substance use disorder.

Now as you can see here, we have our website for our project, and in which you can see there is a brief explanation of our project as well as explanations as to how Renpy works. You can even download our serious game through the builds. We have builds for both Mac and Windows computers. Explanations as to what Renpy is and common misunderstandings as to what each function means and how to fix them. Then, we have our game itself, which you can access through the Renpy launcher. All you have to do is select your game, and then hit “launch project;” however, if you were to download the game through the builds tab, you do not need Renpy in order to access this.

So, what is our project? Well, we used Renpy, a visual novel engine that runs using Python, in order to create a program that we call The Recovery Room. This room has a user interface that allows the participant to interact, as you can see here you can press the start button and click to move forward and you can receive encouragement based on what problem you are facing that day. The long-term possibilities for this project are that professionals in the industry can learn to create their own recovery rooms for their patients, together with their patients in order to create a therapeutic and personalized app that runs with minimal coding that way the patient can access it on their phone if they need it or if they need a reminder of their goal of recovery. The audio, the images and the text can all be altered to better suit the patient’s needs.

Thank you for listening to my presentation on our recovery room and listening to our hard work this summer.

College of Humanities and Social Science Summer Team Impact Project

“The mental health services in here are poor”: Resident Perceptions on Mental Health Services Provided in Restricted Housing

Author(s): Shelby Rothberg, Kristina Wheeler

Mentor(s): Taylor Hartwell, Criminology, Law, and Society

Many residents experience symptoms of mental illness while being housed in solitary confinement units, yet little is known about the mental health services provided to them, and how effective the residents find these services to be. This research investigates how the residents placed in solitary confinement units perceive the mental health services provided to them while they are housed in these units. Using 106 interviews collected from six different prisons, our findings suggest that most residents view these mental health services as being an overall negative experience, with residents citing a lack of treatment or ineffective treatment. These findings show that mental health services in solitary confinement units are lacking and more effective treatment is needed.

Title Slide:
-Hi, I am Shelby Rothberg
-I am Kristina Wheeler
-And we are Criminology, Law, and Society Majors at George Mason University. For our Summer Team Impact Project, we worked with the Center for Advancing Correctional Excellence!, also known as ACE!, on their Solitary Confinement Project. This project has been ongoing for the past five years, with two of those including teams of undergraduate and graduate research assistants visiting seven different prisons and solitary confinement units where they conducted interviews with residents and staff. For our project this summer, Kristina and I investigated residents’ perceptions of the mental health services provided to them in solitary confinement.
Literature Review
-In the prisons included in this study, solitary confinement units are called RHUs, or restricted housing units. RHUs are commonly referred to as a “prison within a prison”. A resident may be placed in an RHU as either a voluntary or involuntary removal from the general population. While in the RHU, residents are placed in a locked cell, either alone or with another prisoner, and are unable to leave their cell for a majority of the day. We began our project by diving into the previous literature regarding the mental health of residents in solitary confinement units. Much of the previous literature on this subject can be summarized by the following key findings. First, in 2005 Rhodes found that 20-25% of residents in solitary confinement units included in their study showed strong evidence of having a mental illness. Next, Haney concluded in 2006 that prisoners held in solitary confinement for long periods of time are at an increased risk of mental illness symptoms, especially long-term impulse-control disorder and clinical depression. In 2020 Dellazizzo et al., concluded that mental illness is a contributing factor to a resident’s placement in solitary confinement. Finally, in their 2008 study, Lovell found that 45% of residents included in their study that were being housed in solitary confinement units suffered from psychological breakdowns, marked psychological symptoms, brain damage, or serious mental illness. These findings all suggest that long-term solitary confinement may lead to declining mental health and symptoms of mental illness. They also suggest that many residents in these solitary confinement units have a preexisting mental illness before they were placed within these units, and their placement may lead to worsening symptoms of their mental illness. With this understanding that solitary confinement leads to declining mental health and that many residents in these units have symptoms of mental illness also comes a gap in knowledge regarding the mental health services provided to these residents, and more importantly the residents’ perceptions of the mental health services that are provided to them while they are in the solitary confinement units.
Research Question
-This gap in knowledge leads to our main research question for our summer project: What are residents’ in solitary confinement units’ perceptions of the mental health services provided to them while they are being housed in the RHU? This then leads into another question we investigated which was: Do residents feel that the mental health services provided to them in the RHU are effective or ineffective? Another question we would like to explore in the future is: what are RHU residents” thoughts about psychological staff in RHUs?
Data Collection/Methods
-To answer these questions, we coded and analyzed resident interviews that had been previously collected by the ACE! team. Our study site included six different male state prisons that each had their own RHU. The data collection process took place over two years beginning in 2017. In the first year, the team spent two days in each of the three prisons. In the second year, the team spent four days in each of the four prisons. The team consisted of 6 undergraduate research assistants, 3 masters students, 2 PhD students, and 3 faculty members. The data collected included two hundred and nine total resident interviews, one hundred and six of which were included in our current project. 103 interviews were excluded as they did not reference mental health services. Our research process began by closely reading through all of the resident interviews. We then used the coding software program Atlas.ti to code the interviews for mental health and mental health services. From here, interviews that included any mention of mental health services in RHUs were used in this study. Preliminary analysis was then conducted to determine residents’ perceptions of the mental health services that were provided to them.
Findings
-Here are our findings. Overall in our research, there were more negative perceptions of mental health, rather than positive perceptions of service. In total, there were 274 instances where the resident stated their negative perception, and 36 instances where the resident said something positive about the mental health services provided. There were two major recurring themes in the data such as the 76 residents who said that they believed there were not enough services or a lack thereof, and the 71 residents who stated that the psych services provided were ineffective and not beneficial. We have also provided quotes from the data to give examples of the residents perceptions such as, “There are no checkups to even see if anyone needs to get any medication provided to them”, “They (CO’S) do not talk about what is actually happening”, and “The mental health services in here are poor. There is really no one for you to talk to. They just give you medication when you just need to talk to someone.”
Discussion
-For theoretical implications we wanted to expand the literature on mental health services in RHU’s using the data provided. Practical implications include residents that may have useful suggestions for improving the effectiveness of treatment for themselves or in group therapy settings. Correctional institutions should consider adding more psych services for residents as well. Some RHU’s may be lacking the employees that are needed. For our future research we have three things we would like to research. The first research question is “What are RHU residents’ perceptions of psychological staff in RHUs?”. The second thing we would like to inquire in-depth is into mental health services (e.g.,how correctional officers/psych staff implement the psych programs, how correctional staff enforce attendance, etc.). Lastly a more in-depth inquiry into psych programming (e.g., examine perceptions of psych programs among program participants; conduct ethnographic field observations of psych programs).
Final Slide
-Thank you for listening to our presentation

College of Public Health Summer Team Impact Project

Role of Urban Built Environment of Breast Cancer Mortality Health Disparities

Author(s): Kai Barner, Ha Dao, Abigail Kokkinakis, Alexandra Diaz Merida, Amanda Webber

Mentor(s): Taylor Anderson, Geography & Geoinformation Science; Travis Gallo, Environmental Science and Policy; Mariaelena Pierobon, Biohealth

Top of Form

Abigail G Kokkinakis0:09

Ours created a US model focusing on urban built environment variables without the limitations of most global regression techniques.

Ha Dao1:10

Amanda A Webber1:59

Kai Barner2:47

Where our OLS model had an adjusted R-squared of 0.478, our MGWR model explained about 44 percentage points more variance, with an adjusted R-squared of 0.918.

Alexandra Diaz Merida5:05

In summary, MGWR explains the results better than OLS for spatial heterogeneity, providing better explanations for how urban built environment variables affect breast cancer mortality. Thank you.

Kai Barner6:12

Thank you.

Abigail G Kokkinakis6:13

Thank you.