Perspectives of human factors in designing elderly monitoring system

A Framework for Detecting and Analyzing Behavior Changes of Elderly People over Time Using Learning Techniques

A sensor-rich environment can be exploited for elder healthcare applications. In this work, our objective was to conduct a continuous and long-term analysis of elderly's behavior for detecting changes. We indeed did not study snapshots of the behavior but, rather, analyzed the overall behavior evolution over long periods of time in order to detect anomalies. Therefore, we proposed a learning method and formalize a normal behavior pattern for elderly people related to her/his Activities of Daily Living (ADL). We also defined a temporal similarity score between activities that allows detecting behavior changes over time. During the periods of time when behavior changes occurred, we then focused on each activity to identify anomalies. Finally, when a behavior change occurred, it was also necessary to help caregivers and/or family members understand the possible pathology detected in order for them to react accordingly. Therefore, the framework presented in this article includes a fuzzy logic-based decision support system that provides information about the suspected disease and its severity.

Design of a Predictive Scheduling System to Improve Assisted Living Services for Elders

As the number of older adults increases, and with it the demand for dedicated care, geriatric residences face a shortage of caregivers, who themselves experience work overload, stress, and burden. We conducted a long-term field study in three geriatric residences to understand the work conditions of caregivers with the aim of developing technologies to assist them in their work and help them deal with their burdens. From this study, we obtained relevant requirements and insights to design, implement, and evaluate two prototypes for supporting caregivers’ tasks (e.g., electronic recording and automatic notifications) in order to validate the feasibility of their implementation in situ and their technical requirements. The evaluation in situ of the prototypes was conducted for a period of 4 weeks. The results of the evaluation, together with the data collected from 6 months of use, motivated the design of a predictive schedule, which was iteratively improved and evaluated in participative sessions with caregivers. PRESENCE, the predictive schedule we propose, triggers real-time alerts of risky situations (e.g., falls, entering off-limits areas such as the infirmary or the kitchen) and informs caregivers of routine tasks that need to be performed (e.g., medication administration, diaper change, etc.). Moreover, PRESENCE helps caregivers to record caring tasks (such as diaper changes or medication) and well-being assessments (such as the mood) that are difficult to automate. This facilitates caregiver's shift handover and can help to train new caregivers by suggesting routine tasks and by sending reminders and timely information about residents. It can be seen as a tool to reduce the workload of caregivers and medical staff. Instead of trying to substitute the caregiver with an automatic caring system, as proposed by others, we propose our predictive schedule system that blends caregiver assessments and measurements from sensors. We show the feasibility of predicting caregiver tasks and a formative evaluation with caregivers that provides preliminary evidence of its utility.