Efficient segmentation and 1D-CNN model optimization for recognizing human actions with mobile sensors

This research proposes a unique approach to human action recognition using mobile sensor data and a computationally efficient 1D Convolutional Neural Network (1D-CNN). In this research, a 1D-CNN model is constructed to recognize human actions using data from an accelerometer sensor. In order to increase model performance, the study looks into the ideal number of layers and epochs. The proposed method also automates the data annotation to simplify the training process. This research highlights the significance of the chosen model and the size of segments in action recognition. This paper investigates the ideal segmentation size for precisely identifying actions. Experimental analysis confirms the effectiveness of the segmentation length in recognizing human actions by reducing false alarms. This paper demonstrates that increasing the number of fully connected layers does not increase precision or accuracy. The paper concludes by proposing subject-independent methods for action recognition and optimizing power consumption for wearable devices. It highlights the potential of using mobile sensor data and 1D-CNNs for future research in human action recognition. The method and model presented in this paper achieve 95% of recognition accuracy.