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Article

Development of AI Algorithm for Weight Training Using Inertial Measurement Units

1
Department of Electrical Engineering, National United University, Maio-Li 360, Taiwan
2
Department of Computer Science and Information Engineering, National United University, Maio-Li 360, Taiwan
3
Department of Information Management, National United University, Maio-Li 360, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Teen-Hang Meen and Chun-Yen Chang
Appl. Sci. 2022, 12(3), 1422; https://doi.org/10.3390/app12031422
Received: 27 November 2021 / Revised: 24 January 2022 / Accepted: 25 January 2022 / Published: 28 January 2022
(This article belongs to the Special Issue Human-Computer Interactions)
Thanks to the rapid development of Wearable Fitness Trackers (WFTs) and Smartphone Pedometer Apps (SPAs), people are keeping an eye on their health through fitness and heart rate tracking; therefore, home weight training exercises have received a lot of attention lately. A multi-procedure intelligent algorithm for weight training using two inertial measurement units (IMUs) is proposed in this paper. The first procedure is for motion tracking that estimates the arm orientation and calculates the positions of the wrist and elbow. The second procedure is for posture recognition based on deep learning, which identifies the type of exercise posture. The final procedure is for exercise prescription variables, which first infers the user’s exercise state based on the results of the previous two procedures, triggers the corresponding event, and calculates the key indicators of the weight training exercise (exercise prescription variables), including exercise items, repetitions, sets, training capacity, workout capacity, training period, explosive power, etc.). This study integrates the hardware and software as a complete system. The developed smartphone App is able to receive heart rate data, to analyze the user’s exercise state, and to calculate the exercise prescription variables automatically in real-time. The dashboard in the user interface of the smartphone App can display exercise information through Unity’s Animation System (avatar) and graphics, and records are stored by the SQLite database. The designed system was proven by two types of experimental verification tests. The first type is to control a stepper motor to rotate the designed IMU and to compare the rotation angle obtained from the IMU with the rotation angle of the controlled stepper motor. The average mean absolute error of estimation for 31 repeated experiments is 1.485 degrees. The second type is to use Mediapipe Pose to calculate the position of the wrist and the angles of upper arm and forearm between the Z-axis, and these calculated data are compared with the designed system. The root-mean-square (RMS) error of positions of the wrist is 2.43 cm, and the RMS errors of two angles are 5.654 and 4.385 degrees for upper arm and forearm, respectively. For posture recognition, 12 participants were divided into training group and test group. Eighty percent and 20% of 24,963 samples of 10 participants were used for the training and validation of the LSTM model, respectively. Three-thousand-three-hundred-and-fifty-nine samples of two participants were used to evaluate the performance of the trained LSTM model. The accuracy reached 99%, and F1 score was 0.99. When compared with the other LSTM-based variants, the accuracy of one-layer LSTM presented in this paper is still promising. The exercise prescription variables provided by the presented system are helpful for weight trainers/trainees to closely keep an eye on their fitness progress and for improving their health. View Full-Text
Keywords: inertial measurement unit; 9-DoF sensor; weight training; motion tracking; posture recognition; machine learning; internet of things inertial measurement unit; 9-DoF sensor; weight training; motion tracking; posture recognition; machine learning; internet of things
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MDPI and ACS Style

Wu, Y.-C.; Lin, S.-X.; Lin, J.-Y.; Han, C.-C.; Chang, C.-S.; Jiang, J.-X. Development of AI Algorithm for Weight Training Using Inertial Measurement Units. Appl. Sci. 2022, 12, 1422. https://doi.org/10.3390/app12031422

AMA Style

Wu Y-C, Lin S-X, Lin J-Y, Han C-C, Chang C-S, Jiang J-X. Development of AI Algorithm for Weight Training Using Inertial Measurement Units. Applied Sciences. 2022; 12(3):1422. https://doi.org/10.3390/app12031422

Chicago/Turabian Style

Wu, Yu-Chi, Shi-Xin Lin, Jing-Yuan Lin, Chin-Chuan Han, Chao-Shu Chang, and Jun-Xian Jiang. 2022. "Development of AI Algorithm for Weight Training Using Inertial Measurement Units" Applied Sciences 12, no. 3: 1422. https://doi.org/10.3390/app12031422

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