Recently, the multimoal and high dimensional sensor data are prone to problems such as artificial error and time-consuming acquisition processes, especially in supervised human activity recognition. Therefore, this study proposes an activity recognition framework called compositional Bidir-LSTM-CNN Networks, which automatically extracts features from raw data using the optimized Convolutional Neural Network and further capture dynamic temporal features through the Bidirectional Lone Short Term Memory Network. Finally, this study paves the way for accurate recognition of human activities using the proposed framework with significantly improve 8% recognition accuracy along with additional features such as robustness and generalization.
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