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Physical Workout Classification Using Wrist Accelerometer Data by Deep Convolutional Neural Networks
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DOI：10.1109/ACCESS.2019.2959398.png)
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Recently, the deep learning algorithm has received considerable attention and is influencing different fields including human-computer interaction (HCI). The purpose of this study is to maximize accuracy by applying deep learning to the classification of body movements. An experiment was performed to collect acceleration information on the wrist while performing seven workouts: pull up, row-barbell, bench press, dips, squat, deadlift, and military press. Participants were asked to perform each workout for ten sets repeated ten times per set. Experimental results confirm that one-dimensional convolutional neural network was the best among different algorithms including support vector machine, multi-layer perceptron, long short-term memory, and other deep convolutional neural networks. The accuracy was extremely high, 96%. The results of this experiment are applicable not only to the classification of fitness activities but also to the classification of different motions in numerous sporting events.
Keyword:
Deep convolutional neural networks
fitness workouts
physical movements
accelerometer
smartwatches
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3.6
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9.8W
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29.4W
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