Abstract
This paper proposes a systemic approach to upper arm gym-workout classification according to spatio-temporal features depicted by biopotential as well as joint kinematics. The key idea of the proposed approach is to impute a biopotential-kinematic relationship by merging the joint kinematic data into a multichannel electromyography signal and visualizing the merged biopotential-kinematic data as an image. Under this approach, the biopotential-kinematic relationship can be imputed by counting on the functionality of a convolutional neural network: an automatic feature extractor followed by a classifier. First, while a professional trainer is demonstrating upper arm gym-workouts, electromyography and joint kinematic data are measured by an armband-type surface electromyography (sEMG) sensor and a RGB-d camera, respectively. Next, the measured data are augmented by adopting the amplitude adjusted Fourier Transform. Then, the augmented electromyography and joint kinematic data are visualized as one image by merging and calculating pixel components in three different ways. Lastly, for each visualized image type, upper arm gym-workout classification is performed via the convolutional neural network. To analyze classification accuracy, two-way rANOVA is performed with two factors: the level of data augmentation and visualized image type. The classification result substantiates that a biopotential-kinematic relationship can be successfully imputed by merging joint kinematic data in-between biceps- and triceps-electromyography channels and visualizing as a time-series heatmap image.
Highlights
Nowadays, many people regularly perform gym-workouts to prevent enervation and invigorate the activities of daily living
To address the issues above, we propose a systemic approach to upper arm gymworkout classification via convolutional neural network (CNN)
We introduce a data augmentation technique for time series, present various visualization methods according to human-eye friendly image manipulation, and statistically analyze the CNN classification performance based on experimental evaluations
Summary
Many people regularly perform gym-workouts to prevent enervation and invigorate the activities of daily living. Prerequisite for maximizing the efficacy of exercise and preventing unexpected injury is that a person must work out with a correct posture as well as target muscle stimulation while performing exercises such as arm-curl, dead-lift, kettle-bell squat, and so on [1,4]. People’s interests about exercise monitoring systems have been grown as the number of both gym-goers and home trainees increase. Computer vision-based approach is one main pillar to build exercise monitoring systems. In the computer vision-based approaches, the exercise monitoring system is typically equipped with RGB-d (or RGB) cameras such as Microsoft Kinect, Intel Realsense, etc. The aforementioned studies in [1,2,3]
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