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Efficiency Optimization of Deep Workout Recognition with Accelerometer Sensor for a Mobile Environment

Recent advancements in deep learning have created numerous possibilities for real-world application, among which the recognition of human motions with sensors. We employed a convolutional neural network (CNN) to process workout motion data to solve the segmentation and recognition problem. We focused on deploying the network architecture in a mobile environment characterized by limited resources. Our experimental results were promising in terms of both segmentation and recognition. Furthermore, we analyzed the performance correlation between sampling the rate and recognition rate. This result indicated 55Hz to provide an appropriate amount of information for workout motion. Then we investigated the computational cost and memory space usage of the hyper-parameter selection. The experimental results implied that appropriate hyper-parameter selection could reduce the computational burden on the mobile environment. Subsequently, we suggest an efficiency score regarding the computational cost, memory usage, and the recognition rate. Our suggested efficiency score shows that our method could find hyper-parameters that offer minimum loss of accuracy and are computationally inexpensive with optimal utilization of memory space.