Artificial Intelligence and Home Fall Detection: Challenges and Prospects

As the world population is aging, fall detection has emerged a major technology in ensuring the safety of the elderly and the provision of rehabilitative services to them at home. Nevertheless, the current studies continue to encounter difficulties regarding the implementation of algorithms, data privacy, and the lack of data. To address this gap, this paper uses an exploratory review method to describe in a systematic manner the status quo in the research concerning artificial intelligence as applied in the area of home fall detection. The process of analyzing 11 chosen core articles demonstrates that the use of AI technology in the area of fall detection is typically represented in: posture estimation-driven visual recognition, wearable sensor-driven time-series analysis, and multimodal monitoring systems in the Internet of Things. It has been found that the objective of AI application in research is to enhance the accuracy and openness of clinical judgment using interpretable AI and advanced filtering procedures. The paper discusses the need to create low cost, high precision, and privacy-sensitive AI systems in the home care setting as well as present a broad view of the system design in the intelligent elder care services in the future.