A robust deep learning model for fall action detection using healthcare wearable sensors

The proposed technique begins with Butterworth’s sixth-order filtering of the data followed by segmentation through Hamming window application. The identification of essential patterns is achieved through the utilization of feature extraction methods which include State Space Correlation Entropy (SSCE) coefficients together with Mel Frequency Cepstral Coefficients (MFCC), Linear Predictive Cepstral Coefficients (LPCC), Parseval’s energy and Auto-Regressive (AR) coefficients. The features are selected through Particle Swarm Optimization (PSO) optimization then Long Short-Term Memory (LSTM) networks execute the classification process. The method received experimental assessment using three publicly available datasets named UP-Fall, HealthLINK_Falls, and UR-Fall. The proposed method achieves a substantial improvement in classification results compared to traditional approaches while demonstrating enhanced accuracy outcomes in experiments. This methodology demonstrates exceptional performance for fall action detection systems because it combines advanced preprocessing techniques with robust feature extraction methods and LSTM networks optimized through PSO.