An Integrated ADL Anomaly Detection Method Based on Sleep and Occupancy Patterns Using Non-intrusive Indoor Environmental Sensors for Single-person Elderly Households

Jun-Sub Kim; Soo-Hyun Cho; Juno Lee; Hyeun Jun Moon

doi:10.21086/ksles.2026.8.33.4.393

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2026 Vol.33, Issue 4 Next Page

Research Article

An Integrated ADL Anomaly Detection Method Based on Sleep and Occupancy Patterns Using Non-intrusive Indoor Environmental Sensors for Single-person Elderly Households 독거 노인 세대 대상 실내 환경 센서 기반 수면 ∙ 재실 패턴 통합 일상생활 이상 탐지 방법

31 August 2026. pp. 393-404

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Abstract

With population aging and the increase in single-person elderly households, continuous monitoring of daily living patterns has become increasingly important for supporting older adults and vulnerable populations. This study proposes a non-intrusive Activities of Daily Living (ADL)-related anomaly detection framework using indoor environmental sensor data. The proposed framework estimates sleep patterns and room-stay patterns from temperature, humidity, CO₂, particulate matters (PM_2.5, PM₁₀), VOCs, illuminance, and PIR (Passive Infrared) data collected by IoT sensors, and determines ADL-related anomalies by comparing daily patterns with individual baseline distributions. A sleep detection model was developed using 15-min sensor features, temporal contextual variables, and manually recorded sleep labels. The model was implemented as an ensemble classifier combining LightGBM and ExtraTrees, and achieved an F1-score of 0.93 in sleep/non-sleep classification. For behavior pattern analysis, room-level occupancy outputs were converted into daily behavior sequences consisting of room-stay tokens, and deviations from individual baseline routines were quantified using normalized edit distance. Sleep anomalies and behavior sequence anomalies were then integrated using a rule-based decision structure. The proposed framework was further applied to 100 single-person elderly households that was not included in the model training process. The proposed method was able to detect a case of a behavior pattern anomaly, which include no valid sleep episode within the sleep evaluation window, and missing behavior sequences in the selected household. The observed event was later confirmed as hospitalization of the resident. These findings suggest that non-intrusive indoor environmental sensors may support the early detection of ADL-related changes without requiring wearable devices or camera-based monitoring.

Keywords

Activities of Daily Living

Anomaly Detection

Sleep Pattern

Occupancy Detection

Non-intrusive Sensor

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Information

Publisher :The Korean Society of Living Environmental System
Publisher(Ko) :한국생활환경학회
Journal Title :Journal of The Korean Society of Living Environmental System
Journal Title(Ko) :한국생활환경학회
Volume : 33
No :4
Pages :393-404
Received Date : 2026-06-28
Revised Date : 2026-07-03
Accepted Date : 2026-07-09
DOI :https://doi.org/10.21086/ksles.2026.8.33.4.393

Journal of The Korean Society of Living Environmental System ISSN:1226-1289(Print) 한국생활환경학회

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