Non-Intrusive Monitoring and Detection of Mobility Loss in Older Adults Using Binary Sensors DOI Creative Commons
Ioan Șușnea, Emilia Pecheanu, Adina Cocu

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(9), P. 2755 - 2755

Published: April 26, 2025

(1) Background and objective: Mobility is crucial for healthy aging, its loss significantly impacts the quality of life, healthcare costs, mortality among older adults. Clinical mobility assessment methods, though precise, are resource-intensive economically impractical, most existing solutions automatic detection anomalies either obtrusive or improper long time monitoring. This study explores feasibility using non-intrusive, low-cost binary sensors continuous, remote in adults, aiming to identify both sudden events gradual loss. (2) Method: The utilized publicly available datasets (CASAS Aruba HH120) containing annotated activity data recorded from installed residential environments. After preprocessing—including filtering irrelevant sensor aggregation into behaviorally meaningful places (BMPs)—a series forecasting model (Prophet) was used predict normal patterns. A fuzzy inference module analyzed deviations between observed predicted determine probability anomalies. (3) Results: system effectively identified periods prolonged inactivity indicative potential falls other disruptions. Preliminary evaluation indicated a rate approximately 77–81% point anomalies, with false positive ranging 12 16%. Additionally, approach successfully detected simulated declines (1% per day reduction), evidenced by statistically significant regression trends levels over time. (4) Conclusions: argues that non-intrusive sensors, combined lightweight models inference, may provide practical scalable solution detecting Although performance can be further enhanced through improved preprocessing, predictive modeling, anomaly threshold tuning, proposed addresses key limitations approaches.

Language: Английский

Non-Intrusive Monitoring and Detection of Mobility Loss in Older Adults Using Binary Sensors DOI Creative Commons
Ioan Șușnea, Emilia Pecheanu, Adina Cocu

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(9), P. 2755 - 2755

Published: April 26, 2025

(1) Background and objective: Mobility is crucial for healthy aging, its loss significantly impacts the quality of life, healthcare costs, mortality among older adults. Clinical mobility assessment methods, though precise, are resource-intensive economically impractical, most existing solutions automatic detection anomalies either obtrusive or improper long time monitoring. This study explores feasibility using non-intrusive, low-cost binary sensors continuous, remote in adults, aiming to identify both sudden events gradual loss. (2) Method: The utilized publicly available datasets (CASAS Aruba HH120) containing annotated activity data recorded from installed residential environments. After preprocessing—including filtering irrelevant sensor aggregation into behaviorally meaningful places (BMPs)—a series forecasting model (Prophet) was used predict normal patterns. A fuzzy inference module analyzed deviations between observed predicted determine probability anomalies. (3) Results: system effectively identified periods prolonged inactivity indicative potential falls other disruptions. Preliminary evaluation indicated a rate approximately 77–81% point anomalies, with false positive ranging 12 16%. Additionally, approach successfully detected simulated declines (1% per day reduction), evidenced by statistically significant regression trends levels over time. (4) Conclusions: argues that non-intrusive sensors, combined lightweight models inference, may provide practical scalable solution detecting Although performance can be further enhanced through improved preprocessing, predictive modeling, anomaly threshold tuning, proposed addresses key limitations approaches.

Language: Английский

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