Medical Making During COVID

The onset of COVID-19 led many makers to dive deeply into the potential applications of their work to help with the pandemic. Our group’s efforts on this front, all of which were collaborations with a variety of people from multiple universities, led me to this reflective talk about the additional work that is needed for us to take the next step towards democratizing fabrication.

https://www.youtube.com/watch?v=xfz7JDSyWXI&ab_channel=stanfordonline

This talk is based on a series of papers studying and working with people who make, including the following recent COVID-related papers:

Navigating Illness, Finding Place

Sylvia JanickiMatt Ziegler, Jennifer Mankoff:
Navigating Illness, Finding Place: Enhancing the Experience of Place for People Living with Chronic Illness. COMPASS 2021: 173-187

When chronic illness, such as Lyme disease, is viewed through a disability lens, equitable access to public spaces becomes an important area for consideration. Yet chronic illness is often viewed solely through an individualistic, medical model lens. We contribute to this field of study in four consecutive steps using Lyme disease as a case study: (1) we highlight urban design and planning literature to make the case for its relevance to chronic illness; (2) we explore the place-related impacts of living with chronic illness through an analysis of interviews with fourteen individuals living with Lyme disease; (3) we derive a set of design guidelines from our literature review and interviews that serve to support populations living with chronic illness; and (4) we present an interactive mapping prototype that applies our design guidelines to support individuals living with chronic illness in experiencing and navigating public and outdoor spaces.

https://youtu.be/mo8ZIeDXyGE?t=1297
(Left) Users can select accessibility qualities to filter the places shown on the map. (Right) Clicking on a place opens a popup window to see all of that place's coded accessibility qualities, pictures, reviews, and additional textual descriptions.

BLV Understanding of Visual Semantics


Venkatesh Potluri
Tadashi E. GrindelandJon E. Froehlich, Jennifer Mankoff: Examining Visual Semantic Understanding in Blind and Low-Vision Technology Users. CHI 2021: 35:1-35:14

Visual semantics provide spatial information like size, shape, and position, which are necessary to understand and efficiently use interfaces and documents. Yet little is known about whether blind and low-vision (BLV) technology users want to interact with visual affordances, and, if so, for which task scenarios. In this work, through semi-structured and task-based interviews, we explore preferences, interest levels, and use of visual semantics among BLV technology users across two device platforms (smartphones and laptops), and information seeking and interactions common in apps and web browsing. Findings show that participants could benefit from access to visual semantics for collaboration, navigation, and design. To learn this information, our participants used trial and error, sighted assistance, and features in existing screen reading technology like touch exploration. Finally, we found that missing information and inconsistent screen reader representations of user interfaces hinder learning. We discuss potential applications and future work to equip BLV users with necessary information to engage with visual semantics.

Interaction via Wireless Earbuds

Xuhai XuHaitian ShiXin YiWenjia LiuYukang YanYuanchun ShiAlex Mariakakis, Jennifer Mankoff, Anind K. Dey:
EarBuddy: Enabling On-Face Interaction via Wireless Earbuds. CHI 2020: 1-14

Past research regarding on-body interaction typically requires custom sensors, limiting their scalability and generalizability. We propose EarBuddy, a real-time system that leverages the microphone in commercial wireless earbuds to detect tapping and sliding gestures near the face and ears. We develop a design space to generate 27 valid gestures and conducted a user study (N=16) to select the eight gestures that were optimal for both human preference and microphone detectability. We collected a dataset on those eight gestures (N=20) and trained deep learning models for gesture detection and classification. Our optimized classifier achieved an accuracy of 95.3%. Finally, we conducted a user study (N=12) to evaluate EarBuddy’s usability. Our results show that EarBuddy can facilitate novel interaction and that users feel very positively about the system. EarBuddy provides a new eyes-free, socially acceptable input method that is compatible with commercial wireless earbuds and has the potential for scalability and generalizability

HulaMove: Waist Interaction

Xuhai XuJiahao LiTianyi YuanLiang HeXin LiuYukang YanYuntao WangYuanchun Shi, Jennifer Mankoff, Anind K. Dey:
HulaMove: Using Commodity IMU for Waist Interaction. CHI 2021: 503:1-503:16

We present HulaMove, a novel interaction technique that leverages the movement of the waist as a new eyes-free and hands-free input method for both the physical world and the virtual world. We first conducted a user study (N=12) to understand users’ ability to control their waist. We found that users could easily discriminate eight shifting directions and two rotating orientations, and quickly confirm actions by returning to the original position (quick return). We developed a design space with eight gestures for waist interaction based on the results and implemented an IMU-based real-time system. Using a hierarchical machine learning model, our system could recognize waist gestures at an accuracy of 97.5%. Finally, we conducted a second user study (N=12) for usability testing in both real-world scenarios and virtual reality settings. Our usability study indicated that HulaMove significantly reduced interaction time by 41.8% compared to a touch screen method, and greatly improved users’ sense of presence in the virtual world. This novel technique provides an additional input method when users’ eyes or hands are busy, accelerates users’ daily operations, and augments their immersive experience in the virtual world.

Understanding Disabled Knitters


Taylor Gotfrid
Kelly MackKathryn J. LumEvelyn YangJessica K. HodginsScott E. Hudson, Jennifer Mankoff: Stitching Together the Experiences of Disabled Knitters. CHI 2021: 488:1-488:14

Knitting is a popular craft that can be used to create customized fabric objects such as household items, clothing and toys. Additionally, many knitters find knitting to be a relaxing and calming exercise. Little is known about how disabled knitters use and benefit from knitting, and what accessibility solutions and challenges they create and encounter. We conducted interviews with 16 experienced, disabled knitters and analyzed 20 threads from six forums that discussed accessible knitting to identify how and why disabled knitters knit, and what accessibility concerns remain. We additionally conducted an iterative design case study developing knitting tools for a knitter who found existing solutions insufficient. Our innovations improved the range of stitches she could produce. We conclude by arguing for the importance of improving tools for both pattern generation and modification as well as adaptations or modifications to existing tools such as looms to make it easier to track progress

KnitGIST: Generative Texture Design

Hofmann, M., Mankoff, J., & Hudson, S. E. (2020, October). KnitGIST: A Programming Synthesis Toolkit for Generating Functional Machine-Knitting Textures. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (pp. 1234-1247).

Automatic knitting machines are robust, digital fabrication devices that enable rapid and reliable production of attractive, functional objects by combining stitches to produce unique physical properties. However, no existing design tools support optimization for desirable physical and aesthetic knitted properties. We present KnitGIST (Generative Instantiation Synthesis Toolkit for knitting), a program synthesis pipeline and library for generating hand- and machine-knitting patterns by intuitively mapping objectives to tactics for texture design. KnitGIST generates a machine-knittable program in a domain-specific programming language.

Detecting Depression △

A series of research projects based on the UWEXP study have focused on detecting depression in various ways. Three such papers are listed below.

Xuhai XuPrerna ChikersalJanine M. DutcherYasaman S. SefidgarWoosuk SeoMichael J. TumminiaDaniella K. VillalbaSheldon CohenKasey G. CreswellJ. David CreswellAfsaneh DoryabPaula S. NuriusEve A. RiskinAnind K. Dey, Jennifer Mankoff:
Leveraging Collaborative-Filtering for Personalized Behavior Modeling: A Case Study of Depression Detection among College Students. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 5(1): 41:1-41:27 (2021)

The prevalence of mobile phones and wearable devices enables the passive capturing and modeling of human behavior at an unprecedented resolution and scale. Past research has demonstrated the capability of mobile sensing to model aspects of physical health, mental health, education, and work performance, etc. However, most of the algorithms and models proposed in previous work follow a one-size-fits-all (i.e., population modeling) approach that looks for common behaviors amongst all users, disregarding the fact that individuals can behave very differently, resulting in reduced model performance. Further, black-box models are often used that do not allow for interpretability and human behavior understanding. We present a new method to address the problems of personalized behavior classification and interpretability, and apply it to depression detection among college students. Inspired by the idea of collaborative-filtering, our method is a type of memory-based learning algorithm. It leverages the relevance of mobile-sensed behavior features among individuals to calculate personalized relevance weights, which are used to impute missing data and select features according to a specific modeling goal (e.g., whether the student has depressive symptoms) in different time epochs, i.e., times of the day and days of the week. It then compiles features from epochs using majority voting to obtain the final prediction. We apply our algorithm on a depression detection dataset collected from first-year college students with low data-missing rates and show that our method outperforms the state-of-the-art machine learning model by 5.1% in accuracy and 5.5% in F1 score. We further verify the pipeline-level generalizability of our approach by achieving similar results on a second dataset, with an average improvement of 3.4% across performance metrics. Beyond achieving better classification performance, our novel approach is further able to generate personalized interpretations of the models for each individual. These interpretations are supported by existing depression-related literature and can potentially inspire automated and personalized depression intervention design in the future.The prevalence of mobile phones and wearable devices enables the passive capturing and modeling of human behavior at an unprecedented resolution and scale. Past research has demonstrated the capability of mobile sensing to model aspects of physical health, mental health, education, and work performance, etc. However, most of the algorithms and models proposed in previous work follow a one-size-fits-all (i.e., population modeling) approach that looks for common behaviors amongst all users, disregarding the fact that individuals can behave very differently, resulting in reduced model performance. Further, black-box models are often used that do not allow for interpretability and human behavior understanding. We present a new method to address the problems of personalized behavior classification and interpretability, and apply it to depression detection among college students. Inspired by the idea of collaborative-filtering, our method is a type of memory-based learning algorithm. It leverages the relevance of mobile-sensed behavior features among individuals to calculate personalized relevance weights, which are used to impute missing data and select features according to a specific modeling goal (e.g., whether the student has depressive symptoms) in different time epochs, i.e., times of the day and days of the week. It then compiles features from epochs using majority voting to obtain the final prediction. We apply our algorithm on a depression detection dataset collected from first-year college students with low data-missing rates and show that our method outperforms the state-of-the-art machine learning model by 5.1% in accuracy and 5.5% in F1 score. We further verify the pipeline-level generalizability of our approach by achieving similar results on a second dataset, with an average improvement of 3.4% across performance metrics. Beyond achieving better classification performance, our novel approach is further able to generate personalized interpretations of the models for each individual. These interpretations are supported by existing depression-related literature and can potentially inspire automated and personalized depression intervention design in the future.

Leveraging Routine Behavior and Contextually-Filtered Features for Depression Detection among College Students. Xuhai Xu, Prerna Chikersal, Afsaneh Doryab, Daniella Villaalba, Janine M. Dutcher, Michael J. Tumminia, Tim Althoff, Sheldon Cohen, Kasey Creswell, David Creswell, Jennifer Mankoff and Anind K. Dey. IMWUT, Article No 116. 10.1145/3351274

The rate of depression in college students is rising, which is known to increase suicide risk, lower academic performance and double the likelihood of dropping out. Researchers have used passive mobile sensing technology to assess mental health. Existing work on finding relationships between mobile sensing and depression, as well as identifying depression via sensing features, mainly utilize single data channels or simply concatenate multiple channels. There is an opportunity to identify better features by reasoning about co-occurrence across multiple sensing channels. We present a new method to extract contextually filtered features on passively collected, time-series data from mobile devices via rule mining algorithms. We first employ association rule mining algorithms on two different user groups (e.g., depression vs. non-depression). We then introduce a new metric to select a subset of rules that identifies distinguishing behavior patterns between the two groups. Finally, we consider co-occurrence across the features that comprise the rules in a feature extraction stage to obtain contextually filtered features with which to train classifiers. Our results reveal that the best model with these features significantly outperforms a standard model that uses unimodal features by an average of 9.7% across a variety of metrics. We further verified the generalizability of our approach on a second dataset, and achieved very similar results.

Chikersal, P., Doryab, A., Tumminia, M., Villalba, D., Dutcher, J., Liu, X., Cohen, S., Creswell, K., Mankoff, J., Creswell, D., Goel, M., & Dey, A. “Detecting Depression and Predicting its Onset Using Longitudinal Symptoms Captured by Passive Sensing: A Machine Learning Approach With Robust Feature Selection.” ACM Transactions on Computer-Human Interaction (TOCHI), 2020.

We present a machine learning approach that uses data from smartphones and ftness trackers of 138 college students to identify students that experienced depressive symptoms at the end of the semester and students whose depressive symptoms worsened over the semester. Our novel approach is a feature extraction technique that allows us to select meaningful features indicative of depressive symptoms from longitudinal data. It allows us to detect the presence of post-semester depressive symptoms with an accuracy of 85.7% and change in symptom severity with an accuracy of 85.4%. It also predicts these outcomes with an accuracy of >80%, 11-15 weeks before the end of the semester, allowing ample time for preemptive interventions. Our work has signifcant implications for the detection of health outcomes using longitudinal behavioral data and limited ground truth. By detecting change and predicting symptoms several weeks before their onset, our work also has implications for preventing depression.

Shows barchart of import of different features onetecting change in depression
Bar chart shows value of baseline, bluetooth, calls, campus map, location, phone usage, sleep and step features on detecting change in depression. the best set leads to 85.4% accuracy; all features except bluetooth and calls improve on baseline accuracy of 65.9%

Gender in Online Doctor Reviews

Dunivin Z, Zadunayski L, Baskota U, Siek K, Mankoff J. Gender, Soft Skills, and Patient Experience in Online Physician Reviews: A Large-Scale Text Analysis. Journal of Medical Internet Research. 2020;22(7):e14455.

This study examines 154,305 Google reviews from across the United States for all medical specialties. Many patients use online physician reviews but we need to understand effects of gender on review content. Reviewer gender was inferred from names.

Reviews were coded for overall patient experience (negative or positive) by collapsing a 5-star scale and for general categories (process, positive/negative soft skills). We estimated binary regression models to examine relationships between physician rating, patient experience themes, physician gender, and reviewer gender.

We found considerable bias against female physicians: Reviews of female physicians were considerably more negative than those of male physicians (OR 1.99; P<.001). Critiques of female physicians more often focused on soft skills such as amicability, disrespect and candor. Negative reviews typically have words such as “rude, arrogant, and condescending”

Reviews written by female patients were also more likely to mention disrespect (OR 1.27, P<.001), but female patients were less likely to report disrespect from female doctors than expected.

Finally, patient experiences with the bureaucratic process also impacted reviews. This includes issues like cost of care. Overall, lower patient satisfaction is correlated with high physician dominance (e.g., poor information sharing or using medical jargon)

Limitations of our work include the lack of definitive (or non-binary) information about gender; and the fact that we do not know about the actual outcomes of treatment for reviewers.

Even so, it seems critical that readers attend to the who the reviewers are when reading online reviews. Review sites may also want to provide information about gender differences, control for gender when presenting composite ratings for physicians, and helping users write less biased reviews. Reviewers should be aware of their own gender biases and assess reviews for this (http://slowe.github.io/genderbias/).

Living Disability Theory

A picture of a carved wooden cane in greens and blues

It was my honor this year to participate in an auto-ethnographic effort to explore accessibility research from a combination of personal and theoretical perspectives. In the process, and thanks to my amazing co-authors, I learned so much about myself, disability studies, ableism and accessibility.

Best Paper Award Hoffman, M., Kasnitz, D., Mankoff, J. and Bennett, C. l. (2020) Living Disability Theory: Reflections on Access, Research, and Design. In Proceedings of ASSETS 2020, 4:1-4:13

Abstract: Accessibility research and disability studies are intertwined fields focused on, respectively, building a world more inclusive of people with disability and understanding and elevating the lived experiences of disabled people. Accessibility research tends to focus on creating technology related to impairment, while disability studies focuses on understanding disability and advocating against ableist systems. Our paper presents a reflexive analysis of the experiences of three accessibility researchers and one disability studies scholar. We focus on moments when our disability was misunderstood and causes such as expecting clearly defined impairments. We derive three themes: ableism in research, oversimplification of disability, and human relationships around disability. From these themes, we suggest paths toward more strongly integrating disability studies perspectives and disabled people into accessibility research.