Machine Learning-Based Techniques for Fall Detection in Geriatric Healthcare Systems

Intelligent IoT-based ambient assisted living systems (AALS) have been a major research focus area in recent times. According to the studies conducted by National Program for Health Care of the Elderly (NPHCE), elderly population in India will increase to 12% of the national population by 2025 with 8%-10% requiring utmost care. Application of machine learning in areas of AALS such as fall detection, therefore, has the potential to have huge public impact. In this paper, we propose a fall detection system that takes into account not only various wearable sensor node parameter readings for a subject, but also his biological and physiological profile. The profile is used to determine a fall risk category for the subject. We performed machine learning experiments using public datasets for fall detection which included wearable sensor node readings. The algorithms were then retrained by feeding in the risk categorization of the subject, and results from this analyses are presented. The objective of the experiments was to find out the impact of a subject's risk categorization on the accuracy of fall detection. The algorithms presented here form part of a comprehensive geriatric healthcare system under development, which comprises wearable sensor nodes, co-ordinator nodes, an indoor localization framework and cloud-hosted application servers. A brief overview of the system capabilities is also presented.