Research on Deep Learning-Based Sensor Technology for Falls in Elderly Communities

Abstract

Purpose: Artificial intelligence (AI) is being increasingly explored for its potential applications in disease prevention and clinical medicine. This article studies the characteristics of falls in the elderly, summarizes the research on non-contact sensor-based machine learning, and discusses issues and suggestions related to fall prevention. It evaluates the living conditions of the elderly and provides a theoretical basis for preventing fall risks. The article also discusses the advantages, disadvantages, and prospects of applying AI in mental health, aiming to provide a reference for future research. Results: A systematic introduction to applying sensor technology in fall prevention equipment, such as millimeter-wave radar, inertial, and MEMS sensors.Conclusion: AI is developing rapidly and can complement manual diagnosis, but it also has limitations like algorithm bias and ethical issues. Mental health practitioners should actively adapt to and promote the further development of AI in this field.