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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

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Indoor Abnormal Behavior Detection for the Elderly: A Review.

Tianxiao Gu¹, Min Tang¹

¹School of Information Science and Technology, Nantong University, Nantong 226019, China.

Sensors (Basel, Switzerland)

|September 19, 2025

Summary

This summary is machine-generated.

Elderly safety is a growing concern. This review explores indoor abnormal behavior detection technologies, including sensors and AI, to ensure timely treatment for seniors living alone.

Keywords:

abnormal detection fall detection human activity recognition sensors video

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Area of Science:

Gerontology and Computer Science
Focus on aging population and technology applications

Background:

Rising elderly population globally necessitates enhanced safety measures.
Undetected falls and illnesses in seniors living alone pose significant life risks.
Indoor abnormal behavior detection technology is crucial for timely intervention.

Purpose of the Study:

Systematically review indoor abnormal behavior detection methods.
Analyze technical principles, advantages, and limitations of various detection techniques.
Provide a reference for academic research and practical applications.

Main Methods:

Comprehensive literature review of detection methods.
Analysis of sensor-based, video, infrared, WIFI, radar, depth, and multimodal fusion techniques.
Exploration of relevant datasets, applicable scenarios, and current research challenges.

Main Results:

Various detection methods (sensors, video, WIFI, radar, etc.) were analyzed for their principles, pros, and cons.
Key challenges identified include data scarcity, privacy risks, environmental adaptability, and user adoption.
Future directions include generative models, federated learning, multi-sensor fusion, and IoT integration.

Conclusions:

Indoor abnormal behavior detection is vital for elderly safety.
Addressing challenges like data scarcity and privacy is key for effective solutions.
Future research should focus on advanced AI and multi-sensor fusion for robust and privacy-preserving systems.