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To address an aging population, we have been investigating sensor networks for monitoring older adults in their homes. In this paper, we report ongoing work in which passive sensor networks have been installed in 17 apartments in an aging in place eldercare facility. The network under development includes simple motion sensors, video sensors, and a bed sensor that captures sleep restlessness and pulse and respiration levels. Data collection has been ongoing for over two years in some apartments. This longevity in sensor data collection is allowing us to study the data and develop algorithms for identifying alert conditions such as falls, as well as extracting typical daily activity patterns for an individual. The goal is to capture patterns representing physical and cognitive health conditions and then recognize when activity patterns begin to deviate from the norm. In doing so, we strive to provide early detection of potential problems which may lead to serious health events if left unattended. We describe the components of the network and show examples of logged sensor data with correlated references to health events. A summary is also included on the challenges encountered and the lessons learned as a result of our experiences in monitoring aging adults in their homes. [ABSTRACT FROM AUTHOR]

This study aims to explore older adults' privacy considerations for technology based monitoring applications in eldercare that use video systems. It specifically aims to introduce alternative vision based tools and identify whether distorting or "anonymizing" captured images affect older adults' privacy concerns and willingness to allow such an application to be installed in their residence. Ten residents of an independent retirement community were recruited to participate in a series of scenarios. Each scenario involved a daily activity such as sitting in the living room and having a visitor, or preparing a snack. These sessions were video-recorded using different image processing and extraction approaches. Follow-up in-depth interviews with participants were conducted after a demonstration of the captured images. Findings indicate that shape extraction can alleviate privacy concerns associated with the use of cameras. Participants expressed no privacy concerns with silhouette images and emphasized the importance of anonymity in the video sequences. They furthermore expressed the desire to control system operation by being able to turn a vision-based system off and on, and also determine who has access to the collected information. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a participatory evaluation of an actual "smart home" project implemented in an independent retirement facility. Using the participatory evaluation process, residents guided the research team through development and implementation of the initial phase of a smart home project designed to assist residents to remain functionally independent and age in place. We recruited nine residents who provided permission to install the technology in their apartments. We conducted a total of 75 interviews and three observational sessions. Residents expressed overall positive perceptions of the sensor technologies and did not feel that these interfered with their daily activities. The process of adoption and acceptance of the sensors included three phases, familiarization, adjustment and curiosity, and full integration. Residents did not express privacy concerns. They provided detailed feedback and suggestions that were integrated into the redesign of the system. They also reported a sense of control resulting from their active involvement in the evaluation process. Observational sessions confirmed that the sensors were not noticeable and residents did not change their routines. The participatory evaluation approach not only empowers end-users but it also allows for the implementation of smart home systems that address residents' needs. [ABSTRACT FROM AUTHOR]