Elderly Fall Detection using Deep Learning Techniques
Joseph Samuel Philip, M. Shilpa Aarthi, G. Jaspher W. Kathrine, Stewart Kirubakaran S, G. Matthew Palmer · 2023
The elderly should be especially cautious about falls because old age people frequently cause fatalities and serious injuries. As a result, there has been an increase in interest in creating fall detection systems that can instantly identify falls and notify caregivers or emergency services. Deep learning approaches have demonstrated considerable promise in solving this issue. For the elderly to remain safe and healthy, fall detection is a crucial responsibility. Because deep learning approaches can automatically identify specific elements from data, these methodologies have recently demonstrated promising results in the detection of falls. In this research, a deep learning-based method for fall detection makes use of an biLSTM network and Convolutional Neural Network (CNN). The suggested method employs a CNN to identify features in the raw sensor data from a wearable device, such as an accelerometer. Then, based on the order of inputs, predictions are made using the biLSTM network to decide whether the incident has actually occurred.