I completed my Bachelor’s degree in Engineering from Netaji Subhas Institue of Technology, New Delhi. I majored in Mechanical Engineering; however, I found my true calling in working with software. Currently, I am a master’s student at the University of Washington majoring in Information Management.
Many individuals with disabilities and/or chronic conditions experience symptoms that may require intermittent or on-going medical care. However, healthcare is often overlooked as an area where accessibility needs to be addressed to improve physical and digital interactions between patients and healthcare providers. We discuss the challenges faced by individuals with disabilities and chronic conditions in accessing physical therapy and how technology can help improve access. We interviewed 15 people and found both social (e.g. financial constraints, lack of accessible transportation) and physiological (e.g. chronic pain) barriers to accessing physical therapy. Our study suggests that technology interventions that are adaptable, support movement tracking, and community building may support access to physical therapy. Rethinking access to physical therapy for people with disabilities or chronic conditions from a lens that includes social and physiological barriers presents opportunities to integrate accessibility and adaptability into physical therapy technology.
“I’m Just Overwhelmed”: Investigating Physical Therapy Accessibility and Technology Interventions for People with Disabilities and/or Chronic Conditions. Momona Yamagami, Kelly Mack, Jennifer Mankoff, and Katherine M. Steele. ACM Transactions on Accessible Computing 15, no. 4 (2022): 1-22.
Speechreading is the art of using visual and contextual cues in the environment to support listening. Often used by d/Deaf and Hard-of-Hearing (d/DHH) individuals, it highlights nuances of rich communication. However, lived experiences of speechreaders are underdocumented in the literature, and the impact of online environment and interaction of captioning with speechreading has not been explored. To bridge these gaps, we conducted a three-part study consisting of formative interviews, design probes and design sessions with 12 d/DHH individuals who speechread.
Megan Hofmann, Udaya Lakshmi, Kelly Mack, Rosa I. Arriaga, Scott E. Hudson, and Jennifer Mankoff. Making a Medical Maker’s Playbook: An Ethnographic Study of Safety-Critical Collective Design by Makers in Response to COVID-19. Proc. ACM Hum. Comput. Interact. 6(CSCW1): 101:1-101:26 (2022).
We present an ethnographic study of a maker community that conducted safety-driven medical making to deliver over 80,000 devices for use at medical facilities in response to the COVID-19 pandemic. To achieve this, the community had to balance their clinical value of safety with the maker value of broadened participation in design and production. We analyse their struggles and achievement through the artifacts they produced and the labors of key facilitators between diverse community members. Based on this analysis we provide insights into how medical maker communities, which are necessarily risk-averse and safety-oriented, can still support makers’ grassroots efforts to care for their communities. Based on these findings, we recommend that design tools enable adaptation to a wider set of domains, rather than exclusively presenting information relevant to manufacturing. Further, we call for future work on the portability of designs across different types of printers which could enable broader participation in future maker efforts at this scale.
There is a growing body of research revealing that longitudinal passive sensing data from smartphones and wearable devices can capture daily behavior signals for human behavior modeling, such as depression detection. Most prior studies build and evaluate machine learning models using data collected from a single population. However, to ensure that a behavior model can work for a larger group of users, its generalizability needs to be verified on multiple datasets from different populations. We present the first work evaluating cross-dataset generalizability of longitudinal behavior models, using depression detection as an application. We collect multiple longitudinal passive mobile sensing datasets with over 500 users from two institutes over a two-year span, leading to four institute-year datasets. Using the datasets, we closely re-implement and evaluated nine prior depression detection algorithms. Our experiment reveals the lack of model generalizability of these methods. We also implement eight recently popular domain generalization algorithms from the machine learning community. Our results indicate that these methods also do not generalize well on our datasets, with barely any advantage over the naive baseline of guessing the majority. We then present two new algorithms with better generalizability. Our new algorithm, Reorder, significantly and consistently outperforms existing methods on most cross-dataset generalization setups. However, the overall advantage is incremental and still has great room for improvement. Our analysis reveals that the individual differences (both within and between populations) may play the most important role in the cross-dataset generalization challenge. Finally, we provide an open-source benchmark platform GLOBEM – short for Generalization of LOngitudinal BEhavior Modeling – to consolidate all 19 algorithms. GLOBEM can support researchers in using, developing, and evaluating different longitudinal behavior modeling methods. We call for researchers’ attention to model generalizability evaluation for future longitudinal human behavior modeling studies.
Xuhai Xu, Xin Liu, Han Zhang, Weichen Wang, Subigya Nepal, Yasaman S. Sefidgar, Woosuk Seo, Kevin S. Kuehn, Jeremy F. Huckins, Margaret E. Morris, Paula S. Nurius, Eve A. Riskin, Shwetak N. Patel, Tim Althoff, Andrew Campbell, Anind K. Dey, and Jennifer Mankoff. GlOBEM: Cross-Dataset Generalization of Longitudinal Human Behavior Modeling. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6(4): 190:1-190:34 (2022).
Xuhai Xu, Han Zhang, Yasaman S. Sefidgar, Yiyi Ren, Xin Liu, Woosuk Seo, Jennifer Brown, Kevin S. Kuehn, Mike A. Merrill, Paula S. Nurius, Shwetak N. Patel, Tim Althoff, Margaret Morris, Eve A. Riskin, Jennifer Mankoff, Anind K. Dey:
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization. NeurIPS 2022
Dhruv is a freshman at the University of Washington studying Computer Science. In the past, he has began to build a coding expertise in full-stack development and data analytics. He hopes to delve more in the latter through strengthening his Machine Learning skills and utilizing them in meaningful real-world applications, with an emphasis on tackling accessibility. Outside of Computer Science, he maintains an active lifestyle – enjoying weight lifting, playing soccer, and running. Currently, he is working on the Academic Performance Prediction project here at the Make4All Lab.
Srihari Krishnaswamy is a first-year undergraduate student studying Computer Science at the University of Washington. His programming experience in the past consists of work on VR simulations and cross-platform apps. He’s looking to learn more about Machine Learning, AI and Signal Processing in the near future, and looks forward to working on more projects. In his free time, he enjoys music production and playing sports.
Hello! My name is Emma and I’m a third year computer science major at UW. My hobbies include crocheting and trying out new foods. I love talking to other people so feel free to reach out anytime!
Yusuf Shahpurwala is a freshman studying computer science at the University of Washington. Most of his coding experience so far has been through making web applications. He is hoping to learn more about human-computer interaction, machine learning, and embedded systems over the next couple of years. To relax he enjoys playing sports and reading. He is excited to currently be working on the Accessible Knitting project with Make4all.
Zelin is a first-year Master’s student majoring in Mechanical Engineering with Data Science at the University of Washington. He completed his bachelor’s degree in Mechanical Engineering at Colorado State University where he also engaged in research in the field of manufacturing high-performance polymers by 3D printing and robotics.
His current research, led by Momona, focuses on modeling and enhancing biosignals-based human-machine interaction to support accessibility and health. He presently works on data collection and analysis of biosignals.
