A hybrid CNN-LSTM model for involuntary fall detection using wrist-worn sensors

Abstract

Falls have become a significant global safety and health concern, which may lead to physical injuries as well as declined mental health, reduced mobility, and deteriorated quality of life. Wearable sensor-based fall detection has emerged as a promising solution for preventing fall-related injuries. However, existing solutions are hindered by user compliance related to sensor placement locations, overall model accuracy, and dependence on simulated voluntary falls. To overcome these limitations, this study aimed to propose a novel involuntary fall detection solution by using wearable sensors and deep learning algorithms. Forty-nine participants were involved in an experimental study, in which activities of daily living and involuntary falls were simulated and kinematic data from these activities were collected using wrist-worn sensors. A novel hybrid model which integrates a convolutional neural network (CNN) and a long short-term memory (LSTM) model was proposed and its performance was compared with the CNN-alone model and LSTM-alone model. The results showed that the proposed hybrid CNN-LSTM model could effectively detect involuntary falls with 96.94% detection accuracy, 98.33% sensitivity, and 96.67% specificity, superior to the CNN-alone model and LSTM-alone model. These results highlight the effectiveness of our proposed approach in significantly improving fall detection accuracy, providing a more reliable and less intrusive solution for preventing fall-related injuries.