Jason Nawyn

Using a live-in laboratory for ubiquitous computing research

Ubiquitous computing researchers are increasingly turning to sensor-enabled “living laboratories” for the study of people and technologies in settings more natural than a typical laboratory. We describe the design and operation of the PlaceLab, a new live-in laboratory for the study of ubiquitous technologies in home settings. Volunteer research participants individually live in the PlaceLab for days or weeks at a time, treating it as a temporary home. Meanwhile, sensing devices integrated into the fabric of the architecture record a detailed description of their activities. The facility generates sensor and observational datasets that can be used for research in ubiquitous computing and other fields where domestic contexts impact behavior. We describe some of our experiences constructing and operating the living laboratory, and we detail a recently generated sample dataset, available online to researchers.

Estimating activity and sedentary behavior from an accelerometer on the hip or wrist.

PURPOSE Previously, the National Health and Examination Survey measured physical activity with an accelerometer worn on the hip for 7 d but recently changed the location of the monitor to the wrist. This study compared estimates of physical activity intensity and type with an accelerometer on the hip versus the wrist. METHODS Healthy adults (n = 37) wore triaxial accelerometers (Wockets) on the hip and dominant wrist along with a portable metabolic unit to measure energy expenditure during 20 activities. Motion summary counts were created, and receiver operating characteristic (ROC) curves were then used to determine sedentary and activity intensity thresholds. Ambulatory activities were separated from other activities using the coefficient of variation of the counts. Mixed-model predictions were used to estimate activity intensity. RESULTS The ROC for determining sedentary behavior had greater sensitivity and specificity (71% and 96%) at the hip than at the wrist (53% and 76%), as did the ROC for moderate- to vigorous-intensity physical activity on the hip (70% and 83%) versus the wrist (30% and 69%). The ROC for the coefficient of variation associated with ambulation had a larger AUC at the hip compared to the wrist (0.83 and 0.74). The prediction model for activity energy expenditure resulted in an average difference of 0.55 ± 0.55 METs on the hip and 0.82 ± 0.93 METs on the wrist. CONCLUSIONS Methods frequently used for estimating activity energy expenditure and identifying activity intensity thresholds from an accelerometer on the hip generally do better than similar data from an accelerometer on the wrist. Accurately identifying sedentary behavior from a lack of wrist motion presents significant challenges.

Embedding behavior modification strategies into a consumer electronic device: a case study

Ubiquitous computing technologies create new opportunities for preventive healthcare researchers to deploy behavior modification strategies outside of clinical settings. In this paper, we describe how strategies for motivating behavior change might be embedded within usage patterns of a typical electronic device. This interaction model differs substantially from prior approaches to behavioral modification such as CD-ROMs: sensor-enabled technology can drive interventions that are timelier, tailored, subtle, and even fun. To explore these ideas, we developed a prototype system namedViTo. On one level, ViTo functions as a universal remote control for a home entertainment system. The interface of this device, however, is designed in such a way that it may unobtrusively promote a reduction in the users television viewing while encouraging an increase in the frequency and quantity of non-sedentary activities. The design of ViTo demonstrates how a variety of behavioral science strategies for motivating behavior change can be carefully woven into the operation of a common consumer electronic device. Results of an exploratory evaluation of a single participant using the system in an instrumented home facility are presented.

Context-aware tunable office lighting application and user response

LED light sources having multiple independently controllable color channels allow tuning of both the intensity and color output. Consequently, highly tailored lighting can be applied according to instantaneous user needs and preferences. Besides improving lighting performance, energy use can also be reduced since the brightest illumination is applied only when necessary. In an example application, low activity or vacant areas of a multi-zone office are lit by low power illumination, including colored light options, which can reduce energy consumption to 20-45% of typical full-time, fullbrightness, office-wide illumination. The availability of color also allows communication functions and additional aesthetic design possibilities. To reduce user burden in frequent switching between various illumination settings, an activity recognition sensor network is used to identify selected office activities. The illumination is then adjusted automatically to satisfy the needs of the occupants. A handheld mobile device provides an interactive interface for gathering user feedback regarding impressions and illumination preferences. The activity-triggered queries collect contemporaneous feedback that reduces reliance on memory; immediate previews of illumination options are also provided. Through mobile queries and post-experience interviews, user feedback was gathered regarding automation, colored lighting, and illumination preferences. Overall reaction was indicated by a range of response words such as fun, stimulating, very cool, very pleasant, enjoyed, good, comfortable, satisfactory, fine, energy saving, interesting, curious, dim, cave, isolated, distracting, and unfamiliar. Positive reaction from a meaningful, though not universal, fraction of users indicates reasonable application potential, particularly as personal preferences and control are accommodated.

A Closed-Loop Feedback System for a Context-Aware Tunable Architectural Lighting Application

In this demonstration, we present a closed-loop feedback system for evaluating and improving the human factors performance of a lighting system based on tunable LED technology. We investigate the ways in which closed-loop feedback can enhance the ability for lighting to automatically respond to changes in the user’s ongoing activities. A sensing platform uses multimodal wireless sensors and computer vision to detect an individual’s presence and uses computational reasoning to make inferences about his or her activities. A “recognition engine” provides access to the inferred activities, which the LED system uses to make contextually relevant lighting changes according to the various operational states within the space. A human factors experiment makes use of a mobile phone based context-aware experience sampling application that responds to changes in the activities, delivering questions to the user to help improve the activity classifier and refine the lighting application. During the demonstration, participants will experience lighting changes automatically applied to a workspace in order to fulfill the visual requirements of the detected activities and to maximize energy savings.

Development of an eHealth System to Capture and Analyze Patient Sensor and Self-Report Data: Mixed-Methods Assessment of Potential Applications to Improve Cancer Care Delivery

Background Capturing and Analyzing Sensor and Self-Report Data for Clinicians and Researchers (COMPASS) is an electronic health (eHealth) platform designed to improve cancer care delivery through passive monitoring of patients’ health status and delivering customizable reports to clinicians. Based on data from sensors and context-driven administration of patient-reported outcome (PRO) measures, key indices of patients’ functional status can be collected between regular clinic visits, supporting clinicians in the delivery of patient care. Objective The first phase of this project aimed to systematically collect input from oncology providers and patients on potential clinical applications for COMPASS to refine the system. Methods Ten clinicians representing various oncology specialties and disciplines completed semi-structured interviews designed to solicit clinician input on how COMPASS can best support clinical care delivery. Three cancer patients tested a prototype of COMPASS for 7 days and provided feedback. Interview data were tabulated using thematic content analysis to identify the most clinically relevant objective and PRO domains. Results Thematic content analysis revealed that clinicians were most interested in monitoring vital statistics, symptoms, and functional status, including the physical activity level (n=9), weight (n=5), fatigue (n=9), sleep quality (n=8), and anxiety (n=7). Patients (2 in active treatment and 1 in remission) reported that they would use such a device, were enthusiastic about their clinicians monitoring their health status, especially the tracking of symptoms, and felt knowing their clinicians were monitoring and reviewing their health status provided valuable reassurance. Patients would, however, like to provide some context to their data. Conclusions Clinicians and patients both articulated potential benefits of the COMPASS system in improving cancer care. From a clinician standpoint, data need to be easily interpretable and actionable. The fact that patients and clinicians both see potential value in eHealth systems suggests wider adoption and utilization could prove to be a useful tool for improving care delivery.

A visualization tool for reconstructing behavior patterns in built spaces

We demonstrate how sensor data recorded from a home sensing network can be replayed and explored from a first-person perspective using a novel input device and from a birds eye perspective using an augmented reality scanner. Data previously recorded from a living lab apartment is reconstructed, revealing patterns of movement and occupancy. A sample use-case is presented wherein the individuals data is mapped onto a new home in order to reveal how existing behavioral patterns might be impeded or facilitated by differences in the built environment.