arXiv subjects

Jennifer Mankoff

Publications and source records attributed to Jennifer Mankoff.

26 records · Page 2Linked to original sources

arXiv2022

"I'm Just Overwhelmed": Investigating Physical Therapy Accessibility and Technology Interventions for People with Disabilities and/or Chronic Conditions

Many individuals with disabilities and/or chronic conditions (da/cc) experience symptoms that may require intermittent or on-going medical care. However, healthcare is an often-overlooked domain for accessibility work, where access needs associated with temporary and long-term disability must be addressed to increase the utility of physical and digital interactions with healthcare workers and spaces. Our work focuses on a specific domain of healthcare often used by individuals with da/cc: physical therapy (PT). Through a twelve-person interview study, we examined how people's access to PT for their da/cc is hampered by social (e.g., physically visiting a PT clinic) and physiological (e.g., chronic pain) barriers, and how technology could improve PT access. In-person PT is often inaccessible to our participants due to lack of transportation and insufficient insurance coverage. As such, many of our participants relied on at-home PT to manage their da/cc symptoms and work towards PT goals. Participants felt that PT barriers, such as having particularly bad symptoms or feeling short on time, could be addressed with well-designed technology that flexibly adapts to the person's dynamically changing needs while supporting their PT goals. We introduce core design principles (adaptability, movement tracking, community building) and tensions (insurance) to consider when developing technology to support PT access. Rethinking da/cc access to PT from a lens that includes social and physiological barriers presents opportunities to integrate accessibility and adaptability into PT technology.

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arXiv2022

Areas of Strategic Visibility: Disability Bias in Biometrics

This response to the RFI considers the potential for biometrics to help or harm disabled people2. Biometrics are already integrated into many aspects of daily life, from airport travel to mobile phone use. Yet many of these systems are not accessible to people who experience different kinds of disability exclusion . Different personal characteristics may impact any or all of the physical (DNA, fingerprints, face or retina) and behavioral (gesture, gait, voice) characteristics listed in the RFI as examples of biometric signals.

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arXiv2021

Examining Needs and Opportunities for Supporting Students Who Experience Discrimination

Perceived discrimination is common and consequential. Yet, little support is available to ease handling of these experiences. Addressing this gap, we report on a need-finding study to guide us in identifying relevant technologies and their requirements. Specifically, we examined unfolding experiences of perceived discrimination among college students and found factors to address in providing meaningful support. We used semi-structured retrospective interviews with 14 students to understand their perceptions, emotions, and coping in response to discriminatory behaviors within the prior ten-week period. These 14 students were among 90 who provided experience sampling reports of unfair treatment over the same ten-week period. We found that discrimination is more distressing if students face related academic and social struggles or when the incident triggers beliefs of inefficacy. We additionally identified patterns of effective coping. By grounding the findings in an extended stress processing framework, we offer a principled approach to intervention design, which we illustrate through incident-specific and proactive intervention paradigms.

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arXiv2021

Understanding health and behavioral trends of successful students through machine learning models

This study analyzes patterns of physical, mental, lifestyle, and personality factors in college students in different periods over the course of a semester and models their relationships with students' academic performance. The data analyzed was collected through smartphones and Fitbit. The use of machine learning models derived from the gathered data was employed to observe the extent of students' behavior associated with their GPA, lifestyle, physical health, mental health, and personality attributes. A mutual agreement method was used in which rather than looking at the accuracy of results, the model parameters and weights of features were used to find common behavioral trends. From the results of the model creation, it was determined that the most significant indicator of academic success defined as a higher GPA, was the places a student spent their time. Lifestyle and personality factors were deemed more significant than mental and physical factors. This study will provide insight into the impact of different factors and the timing of those factors on students' academic performance.

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arXiv2021

Rapid Convergence: The Outcomes of Making PPE during a Healthcare Crisis

The NIH 3D Print Exchange is a public and open source repository for primarily 3D printable medical device designs with contributions from expert-amateur makers, engineers from industry and academia, and clinicians. In response to the COVID-19 pandemic, a collection was formed to foster submissions of low-cost, local manufacture of personal protective equipment (Personal Protective Equipment (PPE)). We systematically evaluated the 623 submissions in this collection to understand: what makers contributed, how they were made, who made them, and key characteristics of their designs. Our analysis reveals an immediate design convergence to derivatives of a few initial designs affiliated with NIH partners (e.g., universities, the Veteran's Health Administration, America Makes) and major for-profit groups (e.g., Prusa). The NIH worked to review safe and effective designs but was quickly overloaded by derivative works. We found that the vast majority were never reviewed (81.3%) while 10.4% of those reviewed were deemed safe for clinical (5.6%) or community use (4.8%). Our work contributes insights into: the outcomes of distributed, community-based, medical making; features the community accepted as "safe" making; and how platforms can support regulated maker activities in high-risk domains (e.g., healthcare).

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arXiv2017

A Systematic Approach for Exploring Tradeoffs in Predictive HVAC Control Systems for Buildings

Heating, Ventilation, and Cooling (HVAC) systems are often the most significant contributor to the energy usage, and the operational cost, of large office buildings. Therefore, to understand the various factors affecting the energy usage, and to optimize the operational efficiency of building HVAC systems, energy analysts and architects often create simulations (e.g., EnergyPlus or DOE-2), of buildings prior to construction or renovation to determine energy savings and quantify the Return-on-Investment (ROI). While useful, these simulations usually use static HVAC control strategies such as lowering room temperature at night, or reactive control based on simulated room occupancy. Recently, advances have been made in HVAC control algorithms that predict room occupancy. However, these algorithms depend on costly sensor installations and the tradeoffs between predictive accuracy, energy savings, comfort and expenses are not well understood. Current simulation frameworks do not support easy analysis of these tradeoffs. Our contribution is a simulation framework that can be used to explore this design space by generating objective estimates of the energy savings and occupant comfort for different levels of HVAC prediction and control performance. We validate our framework on a real-world occupancy dataset spanning 6 months for 235 rooms in a large university office building. Using the gold standard of energy use modeling and simulation (Revit and Energy Plus), we compare the energy consumption and occupant comfort in 29 independent simulations that explore our parameter space. Our results highlight a number of potentially useful tradeoffs with respect to energy savings, comfort, and algorithmic performance among predictive, reactive, and static schedules, for a stakeholder of our building.

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arXiv2016

Dynamic Question Ordering in Online Surveys

Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach, uniformly across all respondents. We envision a richer interpretation of adaptive questions, which we call dynamic question ordering (DQO), where question order is personalized. Such an approach could increase engagement, and therefore response rate, as well as imputation quality. We present a DQO framework to improve survey completion and imputation. In the general survey-taking setting, we want to maximize survey completion, and so we focus on ordering questions to engage the respondent and collect hopefully all information, or at least the information that most characterizes the respondent, for accurate imputations. In another scenario, our goal is to provide a personalized prediction. Since it is possible to give reasonable predictions with only a subset of questions, we are not concerned with motivating users to answer all questions. Instead, we want to order questions to get information that reduces prediction uncertainty, while not being too burdensome. We illustrate this framework with an example of providing energy estimates to prospective tenants. We also discuss DQO for national surveys and consider connections between our statistics-based question-ordering approach and cognitive survey methodology.

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arXiv2016

Keyboard Surface Interaction: Making the keyboard into a pointing device

Pointing devices that reside on the keyboard can reduce the overall time needed to perform mixed pointing and typing tasks, since the hand of the user does not have to reach for the pointing device. However, previous implementations of this kind of device have a higher movement time compared to the mouse and trackpad due to large error rate, low speed and spatial resolution. In this paper we introduce Keyboard Surface Interaction (KSI), an interaction approach that turns the surface of a keyboard into an interaction surface and allows users to rest their hands on the keyboard at all times to minimize fatigue. We developed a proof-of-concept implementation, Fingers, which we optimized over a series of studies. Finally, we evaluated Fingers against the mouse and trackpad in a user study with 25 participants on a Fitts law test style, mixed typing and pointing task. Results showed that for users with more exposure to KSI, our KSI device had better performance (reduced movement and homing time) and reduced discomfort compared to the trackpad. When compared to the mouse, KSI had reduced homing time and reduced discomfort, but increased movement time. This interaction approach is not only a new way to capitalize on the space on top of the keyboard, but also a call to innovate and think beyond the touchscreen, touchpad, and mouse as our main pointing devices. The results of our studies serve as a specification for future KSI devices.

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