ArticleSensors (Basel, Switzerland)2020
A Framework for Detecting and Analyzing Behavior Changes of Elderly People over Time Using Learning Techniques.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Behavioral patterns in elderly single-person households.Heliyon · 2024Article
- IoT in medical diagnosis: detecting excretory functional disorders for Older adults via bathroom activity change using unobtrusive IoT technology.Frontiers in public health · 2023Article
- Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns.Sensors (Basel, Switzerland) · 2022Article
- Identifying and Monitoring the Daily Routine of Seniors Living at Home.Sensors (Basel, Switzerland) · 2022Article
- Spatial-temporal analysis of urban-rural differences in the development of elderly care institutions in China.Frontiers in public health · 2022Article
- Discovering Daily Activity Patterns from Sensor Data Sequences and Activity Sequences.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
5 authors.
Funding
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Abstract
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.
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Registered trials
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