Pallavi Kaushik

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.

User-adaptive Reminders for Home-based Medical Tasks - A Case Study

OBJECTIVES We present a prototype adaptive reminder system for home-based medical tasks. The system consists of a mobile device for reminder presentation and ambient sensors to determine opportune moments for reminder delivery. Our objective was to study interaction with the prototype under naturalistic living conditions and gain insight into factors affecting the long-term acceptability of context-sensitive reminder systems for the home setting. METHODS A volunteer participant used the prototype in a residential research facility while adhering to a regimen of simulated medical tasks for ten days. Some reminders were scheduled at fixed times during the day and some were automatically time-shifted based on sensor data. We made a complete video and sensor record of the stay. Finally, the participant commented about his experiences with the system in a debriefing interview. RESULTS Based on this case study, including direct observation of individual alert-action sequences, we make four recommendations for designers of context-sensitive adaptive reminder systems. Captured metrics suggest that adaptive reminders led to faster reaction times and were perceived by the participant as being more useful. CONCLUSIONS The evaluation of context-sensitive systems that overlap into domestic lives is challenging. We believe that the ideal experiment is to deploy such systems in real homes and assess performance longitudinally. This case study in an instrumented live-in facility is a step toward that long-term goal.

Investigating the potential of in-home devices for improving medication adherence

We conducted five focus groups with seniors and middle-aged participants who live independently in their own homes to assess the potential value of a home-centered medication reminder system concept. The medication reminder system was conceptualized as a system that uses a television and set-top box, mobile phones and other in-home accessories as a means to set and deliver medication reminders. We found that the main value perceived by participants in the medication reminder system was its ability to provide multiple channels for them to be reminded of medications. The mobile phone, due to its advantages in portability and privacy, was considered to be the most useful device on which to receive reminders. Most participants saw value in receiving secondary reminders on other devices in their home such as the TV, PC, and other in-home accessories. Design implications along with other findings about the challenges faced by participants in managing their medications are discussed.

Deploying Context-Aware Health Technology at Home: Human-Centric Challenges

Publisher Summary The medical systems in many industrialized countries around the world face a problem of paying for the care of an aging population. Recent work has demonstrated that sensors placed in the home environment can be used to develop algorithms that infer context, such as activities of daily living. Context-aware applications can then be created that may help people stay healthy, active, and safe in their homes as they age. Although automatic detection of context from home sensors is an active research area, some critical aspects of the “human-centric” side of creating and deploying these home health systems related to sensor installation, algorithm training, and error recovery have been largely ignored by the research community. Using an example of a medication adherence system motivated by some pilot experiments in which there were subjects self-install sensors in a home, twelve questions were set out, that encouraged context-aware application developers (as well as machine learning researchers providing the context detection algorithms) to ask themselves as they go forward with their research. This chapter argues that these human-centric questions directly impact not only important aspects of human–computer interface design but also the appropriateness of the selection of specific context detection algorithms related to sensor installation, algorithm training, and error recovery have been largely ignored by the research community.Publisher Summary The medical systems in many industrialized countries around the world face a problem of paying for the care of an aging population. Recent work has demonstrated that sensors placed in the home environment can be used to develop algorithms that infer context, such as activities of daily living. Context-aware applications can then be created that may help people stay healthy, active, and safe in their homes as they age. Although automatic detection of context from home sensors is an active research area, some critical aspects of the “human-centric” side of creating and deploying these home health systems related to sensor installation, algorithm training, and error recovery have been largely ignored by the research community. Using an example of a medication adherence system motivated by some pilot experiments in which there were subjects self-install sensors in a home, twelve questions were set out, that encouraged context-aware application developers (as well as machine learning researchers providing the context detection algorithms) to ask themselves as they go forward with their research. This chapter argues that these human-centric questions directly impact not only important aspects of human–computer interface design but also the appropriateness of the selection of specific context detection algorithms related to sensor installation, algorithm training, and error recovery have been largely ignored by the research community.

Chapter 19 – Deploying Context-Aware Health Technology at Home: Human-Centric Challenges

Publisher Summary The medical systems in many industrialized countries around the world face a problem of paying for the care of an aging population. Recent work has demonstrated that sensors placed in the home environment can be used to develop algorithms that infer context, such as activities of daily living. Context-aware applications can then be created that may help people stay healthy, active, and safe in their homes as they age. Although automatic detection of context from home sensors is an active research area, some critical aspects of the “human-centric” side of creating and deploying these home health systems related to sensor installation, algorithm training, and error recovery have been largely ignored by the research community. Using an example of a medication adherence system motivated by some pilot experiments in which there were subjects self-install sensors in a home, twelve questions were set out, that encouraged context-aware application developers (as well as machine learning researchers providing the context detection algorithms) to ask themselves as they go forward with their research. This chapter argues that these human-centric questions directly impact not only important aspects of human–computer interface design but also the appropriateness of the selection of specific context detection algorithms related to sensor installation, algorithm training, and error recovery have been largely ignored by the research community.Publisher Summary The medical systems in many industrialized countries around the world face a problem of paying for the care of an aging population. Recent work has demonstrated that sensors placed in the home environment can be used to develop algorithms that infer context, such as activities of daily living. Context-aware applications can then be created that may help people stay healthy, active, and safe in their homes as they age. Although automatic detection of context from home sensors is an active research area, some critical aspects of the “human-centric” side of creating and deploying these home health systems related to sensor installation, algorithm training, and error recovery have been largely ignored by the research community. Using an example of a medication adherence system motivated by some pilot experiments in which there were subjects self-install sensors in a home, twelve questions were set out, that encouraged context-aware application developers (as well as machine learning researchers providing the context detection algorithms) to ask themselves as they go forward with their research. This chapter argues that these human-centric questions directly impact not only important aspects of human–computer interface design but also the appropriateness of the selection of specific context detection algorithms related to sensor installation, algorithm training, and error recovery have been largely ignored by the research community.

Deploying Context-Aware Health Technology at Home

Publisher Summary The medical systems in many industrialized countries around the world face a problem of paying for the care of an aging population. Recent work has demonstrated that sensors placed in the home environment can be used to develop algorithms that infer context, such as activities of daily living. Context-aware applications can then be created that may help people stay healthy, active, and safe in their homes as they age. Although automatic detection of context from home sensors is an active research area, some critical aspects of the “human-centric” side of creating and deploying these home health systems related to sensor installation, algorithm training, and error recovery have been largely ignored by the research community. Using an example of a medication adherence system motivated by some pilot experiments in which there were subjects self-install sensors in a home, twelve questions were set out, that encouraged context-aware application developers (as well as machine learning researchers providing the context detection algorithms) to ask themselves as they go forward with their research. This chapter argues that these human-centric questions directly impact not only important aspects of human–computer interface design but also the appropriateness of the selection of specific context detection algorithms related to sensor installation, algorithm training, and error recovery have been largely ignored by the research community.Publisher Summary The medical systems in many industrialized countries around the world face a problem of paying for the care of an aging population. Recent work has demonstrated that sensors placed in the home environment can be used to develop algorithms that infer context, such as activities of daily living. Context-aware applications can then be created that may help people stay healthy, active, and safe in their homes as they age. Although automatic detection of context from home sensors is an active research area, some critical aspects of the “human-centric” side of creating and deploying these home health systems related to sensor installation, algorithm training, and error recovery have been largely ignored by the research community. Using an example of a medication adherence system motivated by some pilot experiments in which there were subjects self-install sensors in a home, twelve questions were set out, that encouraged context-aware application developers (as well as machine learning researchers providing the context detection algorithms) to ask themselves as they go forward with their research. This chapter argues that these human-centric questions directly impact not only important aspects of human–computer interface design but also the appropriateness of the selection of specific context detection algorithms related to sensor installation, algorithm training, and error recovery have been largely ignored by the research community.