Fall detection in assisted living using wearable sensors in the context of Internet of Health Things (IoHT)

Abstract

Falling is one of the major health concerns faced by elderly people globally. Accurate and timely prediction or detection of falls could result in a significant reduction in injuries and associated costs. In this study, we have presented a deep learning model inspired by InceptionResNet which can detect a fall with an inference time of 52 milliseconds in the pre-fall phase. We conducted 3-fold testing by extracting temporal features of a falling signal from three different phases: pre-impact, post-impact, and the fall-cycle phase. The system is capable of predicting and detecting 15 types of fall activities alongside 21 different activities of daily living (ADL). The classifier has achieved outstanding results in detecting falls in all phases, with an average sensitivity and F1 score of 98%. Achieving a sensitivity of 98% implies that the model has effectively minimized the occurrence of false negatives, which is vital for an accurate and reliable fall detection system. Furthermore, the proposed model has been evaluated using three different types of input features with varying dimensions, ranging from 1D to 6D. These features include the "1D magnitude of Accelerations", "3D Accelerations", and "6D Accelerations & Angular Velocities", which were used to examine the impact of input size on the model’s complexity.