Indoor Abnormal Behavior Detection for the Elderly: A Review.

Due to the increased age of the global population, the proportion of the elderly population continues to rise. The safety of the elderly living alone is becoming an increasingly prominent area of concern. They often miss timely treatment due to undetected falls or illnesses, which pose risks to their lives. In order to address this challenge, the technology of indoor abnormal behavior detection has become a research hotspot. This paper systematically reviews detection methods based on sensors, video, infrared, WIFI, radar, depth, and multimodal fusion. It analyzes the technical principles, advantages, and limitations of various methods. This paper further explores the characteristics of relevant datasets and their applicable scenarios and summarizes the challenges facing current research, including multimodal data scarcity, risk of privacy leakage, insufficient adaptability of complex environments, and human adoption of wearable devices. Finally, this paper proposes future research directions, such as combining generative models, federated learning to protect privacy, multi-sensor fusion for robustness, and abnormal behavior detection on the Internet of Things environment. This paper aims to provide a systematic reference for academic research and practical application in the field of indoor abnormal behavior detection.