Sport-Related Activity Recognition from Wearable Sensors Using Bidirectional GRU Network

Numerous learning-based techniques for effective human activity recognition (HAR) have recently been developed. Wearable inertial sensors are critical for HAR studies to characterize sport-related activities. Smart wearables are now ubiquitous and can benefit people of all ages. HAR investigations typically involve sensor-based evaluation. Sport-related activities are unpredictable and have historically been classified as complex, with conventional machine learning (ML) algorithms applied to resolve HAR issues. The efficiency of machine learning techniques in categorizing data is limited by the human-crafted feature extraction procedure. A deep learning model named MBiGRU (multimodal bidirectional gated recurrent unit) neural network was proposed to recognize everyday sport-related actions, with the publicly accessible UCI-DSADS dataset utilized as a benchmark to compare the effectiveness of the proposed deep learning network against other deep learning architectures (CNNs and GRUs). Experiments were performed to quantify four evaluation criteria as accuracy, precision, recall and F1-score. Following a 10-fold cross-validation approach, the experimental findings indicated that the MBiGRU model presented superior accuracy of 99.55% against other benchmark deep learning networks. The available evidence was also evaluated to explore ways to enhance the proposed model and training procedure.