Andrew Raij

AutoSense: unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field

The effect of psychosocial stress on health has been a central focus area of public health research. However, progress has been limited due a to lack of wearable sensors that can provide robust measures of stress in the field. In this paper, we present a wireless sensor suite called AutoSense that collects and processes cardiovascular, respiratory, and thermoregularity measurements that can inform about the general stress state of test subjects in their natural environment. AutoSense overcomes several challenges in the design of wearable sensor systems for use in the field. First, it is unobtrusively wearable because it integrates six sensors in a small form factor. Second, it demonstrates a low power design; with a lifetime exceeding ten days while continuously sampling and transmitting sensor measurements. Third, sensor measurements are robust to several sources of errors and confounds inherent in field usage. Fourth, it integrates an ANT radio for low power and integrated quality of service guarantees, even in crowded environments. The AutoSense suite is complemented with a software framework on a smart phone that processes sensor measurements received from AutoSense to infer stress and other rich human behaviors. AutoSense was used in a 20+ subject real-life scientific study on stress in both the lab and field, which resulted in the first model of stress that provides 90% accuracy.

Privacy risks emerging from the adoption of innocuous wearable sensors in the mobile environment

Wearable sensors are revolutionizing healthcare and science by enabling capture of physiological, psychological, and behavioral measurements in natural environments. However, these seemingly innocuous measurements can be used to infer potentially private behaviors such as stress, conversation, smoking, drinking, illicit drug usage, and others. We conducted a study to assess how concerned people are about disclosure of a variety of behaviors and contexts that are embedded in wearable sensor data. Our results show participants are most concerned about disclosures of conversation episodes and stress - inferences that are not yet widely publicized. These concerns are mediated by temporal and physical context associated with the data and the participants personal stake in the data. Our results provide key guidance on the extent to which people understand the potential for harm and data characteristics researchers should focus on to reduce the perceived harm from such datasets.

A Survey of Incentive Techniques for Mobile Crowd Sensing

Crowd sensing (CS) is an approach to collecting many samples of a phenomena of interest by distributing the sampling across a large number of individuals. While any one individual may not provide sufficient samples, aggregating samples across many individuals provides high-quality, high-coverage measurements of the phenomena. Thus, for participatory sensing to be successful, one must motivate a large number of individuals to participate. In this work, we review a variety of incentive mechanisms that motivate people to contribute to a CS effort. We then establish a set of design constraints or minimum requirements that any incentive mechanism for CS must have. These design constrains are then used as metrics to evaluate those approaches and determine their advantages and disadvantages. We also contribute a taxonomy of CS incentive mechanisms and show how current systems fit within this taxonomy. We conclude with the identification of new types of incentive mechanisms that require further investigation.

Exploring micro-incentive strategies for participant compensation in high-burden studies

Micro-incentives represent a new but little-studied trend in participant compensation for user studies. In this paper, we use a combination of statistical analysis and models from labor economics to evaluate three canonical micro-payment schemes in the context of high-burden user studies, where participants wear sensors for extended durations. We look at how these strategies affect compliance, data quality, and retention, and show that when used carefully, micro-payments can be highly beneficial. We find that data quality is different across the micro-incentive schemes we experimented with, and therefore the incentive strategy should be chosen with care. We think that adaptive micro-payment based incentives can be used to successfully incentivize future studies at much lower cost to the study designer, while ensuring high compliance, good data quality, and lower retention issues.

Are we there yet?: feasibility of continuous stress assessment via wireless physiological sensors

Stress can lead to headaches and fatigue, precipitate addictive behaviors (e.g., smoking, alcohol and drug use), and lead to cardiovascular diseases and cancer. Continuous assessment of stress from sensors can be used for timely delivery of a variety of interventions to reduce or avoid stress. We investigate the feasibility of continuous stress measurement via two field studies using wireless physiological sensors --- a four-week study with illicit drug users (n = 40), and a one-week study with daily smokers and social drinkers (n = 30). We find that 11+ hours/day of usable data can be obtained in a 4-week study. Significant learning effect is observed after the first week and data yield is seen to be increasing over time even in the fourth week. We propose a framework to analyze sensor data yield and find that losses in wireless channel is negligible; the main hurdle in further improving data yield is the attachment constraint. We show the feasibility of measuring stress minutes preceding events of interest and observe the sensor-derived stress to be rising prior to self-reported stress and smoking events.

Immersive 3DUI on one dollar a day

A convergence between consumer electronics and virtual reality is occurring. We present an immersive head-mounted-display-based, wearable 3D user interface that is inexpensive (less than

Visualization of time-series sensor data to inform the design of just-in-time adaptive stress interventions

900 USD), robust (sourceless tracking), and portable (lightweight and untethered). While the current display has known deficiencies, the user tracking quality is within the constraints of many existing applications, while the portability and cost offers opportunities for innovative applications that are not currently feasible.

Trends in Mobile Cyber-Physical Systems for health Just-in time interventions

We investigate needs, challenges, and opportunities in visualizing time-series sensor data on stress to inform the design of just-in-time adaptive interventions (JITAIs). We identify seven key challenges: massive volume and variety of data, complexity in identifying stressors, scalability of space, multifaceted relationship between stress and time, a need for representation at multiple granularities, inter-person variability, and limited understanding of JITAI design requirements due to its novelty. We propose four new visualizations based on one million minutes of sensor data (n=70). We evaluate our visualizations with stress researchers (n=6) to gain first insights into its usability and usefulness in JITAI design. Our results indicate that spatio-temporal visualizations help identify and explain between- and within-person variability in stress patterns and contextual visualizations enable decisions regarding the timing, content, and modality of intervention. Interestingly, a granular representation is considered informative but noise-prone; an abstract representation is the preferred starting point for designing JITAIs.

VR4VR: vocational rehabilitation of individuals with disabilities in immersive virtual reality environments

Advances in pervasive computing, machine learning, and human activity recognition are changing preventive health care. Emerging paradigms, such as Mobile Cyber-Physical System (MCPS) and Just-in-time interventions (JITI), allow patients to take health monitoring, diagnosis, therapy and treatments beyond traditional medical settings. These paradigms empower patients by delivering health care at any place and at any time. MCPS provides the necessary engineering support to enable JITI systems to work in an autonomous way. In this work, we review the recent trends in the design of Mobile Cyber-Physical systems for Just-in-time interventions (MCP-JITI), and the different engineering concepts behind this paradigm. Finally, we discuss a set of necessary requirements or design issues to successfully deploy in real world scenarios. This discussion is driven by the description of the MCP-JITI architecture and the interconnections among its components.

The wobbly table: Increased social presence via subtle incidental movement of a real-virtual table

This paper presents a virtual reality for vocational rehabilitation system (VR4VR) that is currently in development at the University of South Floridas Center for Assistive, Rehabilitation, and Robotics Technologies (CARRT). VR4VR utilizes virtual reality to assess and train individuals with severe cognitive and physical disabilities. Using virtual reality offers several advantages such as being inexpensive, safer and easily adjustable to different user needs through customization of environments, content and real time interventions. The system is composed of the following components: a virtual reality training area surrounded by an optical motion tracking system, a curved screen with two projectors, a server computer, a remote control interface on a tablet computer for job coaches, and a virtual assistive robot. This paper focuses on virtual reality training for underserved individuals with cognitive disabilities, such as autism spectrum disorder (ASD) and traumatic brain injury (TBI). We describe six transferrable skill modules and corresponding design considerations. Future work focuses on people with severe mobility impairment, such as spinal cord injury (SCI).