Application of Additive Manufacturing and Deep Learning in Exercise State Discrimination
With the rapid development of sports technology, smart wearable devices play a crucial role in athletic training and health management. Sports fatigue is a key factor affecting athletic performance. Using smart wearable devices to detect the onset of fatigue can optimize training, prevent excessive...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/389 |
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| author | Zhilong Zhao Jiaxi Yang Jiahao Liu Shijie Soong Yiming Wang Juan Zhang |
| author_facet | Zhilong Zhao Jiaxi Yang Jiahao Liu Shijie Soong Yiming Wang Juan Zhang |
| author_sort | Zhilong Zhao |
| collection | DOAJ |
| description | With the rapid development of sports technology, smart wearable devices play a crucial role in athletic training and health management. Sports fatigue is a key factor affecting athletic performance. Using smart wearable devices to detect the onset of fatigue can optimize training, prevent excessive fatigue and resultant injury, and increase efficiency and safety. However, current wearable sensing devices are often uncomfortable and imprecise. Furthermore, stable methods for fatigue detection are not yet established. To address these challenges, this paper introduces 3D printing and deep learning to design a smart wearable sensing device to detect different states of sports fatigue. First, to meet the need for comfort and improved accuracy in data collection, we utilized reverse engineering and additive manufacturing technologies. Second, we designed a prototype based on the long short-term memory (LSTM) neural network to analyze the collected bioelectrical signals for the identification of sports fatigue states and the extraction of related indicators. Finally, we conducted a large number of numerical experiments. The results demonstrated that our prototype and related equipment could collect signals and mine information as well as identify indicators associated with sports fatigue in the signals, thereby improving accuracy in the classification of fatigue states. |
| format | Article |
| id | doaj-art-37fb6fc30c8d41deb6063abe3335af65 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-37fb6fc30c8d41deb6063abe3335af652025-01-24T13:48:44ZengMDPI AGSensors1424-82202025-01-0125238910.3390/s25020389Application of Additive Manufacturing and Deep Learning in Exercise State DiscriminationZhilong Zhao0Jiaxi Yang1Jiahao Liu2Shijie Soong3Yiming Wang4Juan Zhang5Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, ChinaUSC Viterbi School of Engineering, Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angles, CA 90089, USASchool of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei 230001, ChinaDepartment of Bioengineering, Royal School of Mines, Imperial College London, London SW7 2AZ, UKBiomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, ChinaBiomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, ChinaWith the rapid development of sports technology, smart wearable devices play a crucial role in athletic training and health management. Sports fatigue is a key factor affecting athletic performance. Using smart wearable devices to detect the onset of fatigue can optimize training, prevent excessive fatigue and resultant injury, and increase efficiency and safety. However, current wearable sensing devices are often uncomfortable and imprecise. Furthermore, stable methods for fatigue detection are not yet established. To address these challenges, this paper introduces 3D printing and deep learning to design a smart wearable sensing device to detect different states of sports fatigue. First, to meet the need for comfort and improved accuracy in data collection, we utilized reverse engineering and additive manufacturing technologies. Second, we designed a prototype based on the long short-term memory (LSTM) neural network to analyze the collected bioelectrical signals for the identification of sports fatigue states and the extraction of related indicators. Finally, we conducted a large number of numerical experiments. The results demonstrated that our prototype and related equipment could collect signals and mine information as well as identify indicators associated with sports fatigue in the signals, thereby improving accuracy in the classification of fatigue states.https://www.mdpi.com/1424-8220/25/2/389bioelectrical signalreverse engineeringadditive manufacturinglong short-term memory (LSTM) neural networksports fatigue states |
| spellingShingle | Zhilong Zhao Jiaxi Yang Jiahao Liu Shijie Soong Yiming Wang Juan Zhang Application of Additive Manufacturing and Deep Learning in Exercise State Discrimination Sensors bioelectrical signal reverse engineering additive manufacturing long short-term memory (LSTM) neural network sports fatigue states |
| title | Application of Additive Manufacturing and Deep Learning in Exercise State Discrimination |
| title_full | Application of Additive Manufacturing and Deep Learning in Exercise State Discrimination |
| title_fullStr | Application of Additive Manufacturing and Deep Learning in Exercise State Discrimination |
| title_full_unstemmed | Application of Additive Manufacturing and Deep Learning in Exercise State Discrimination |
| title_short | Application of Additive Manufacturing and Deep Learning in Exercise State Discrimination |
| title_sort | application of additive manufacturing and deep learning in exercise state discrimination |
| topic | bioelectrical signal reverse engineering additive manufacturing long short-term memory (LSTM) neural network sports fatigue states |
| url | https://www.mdpi.com/1424-8220/25/2/389 |
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