Machine learning-assisted novel recyclable flexible triboelectric nanogenerators for intelligent motion
Summary: In the smart era, big data analysis based on sensor units is important in intelligent motion. In this study, a dance sports and injury monitoring system (DIMS) based on a recyclable flexible triboelectric nanogenerator (RF-TENG) sensor module, a data processing hardware module, and an upper...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-04-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S258900422400837X |
| _version_ | 1797214862619705344 |
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| author | Yuzhang Wen Fengxin Sun Zhenning Xie Mengqi Zhang Zida An Bing Liu Yuning Sun Fei Wang Yupeng Mao |
| author_facet | Yuzhang Wen Fengxin Sun Zhenning Xie Mengqi Zhang Zida An Bing Liu Yuning Sun Fei Wang Yupeng Mao |
| author_sort | Yuzhang Wen |
| collection | DOAJ |
| description | Summary: In the smart era, big data analysis based on sensor units is important in intelligent motion. In this study, a dance sports and injury monitoring system (DIMS) based on a recyclable flexible triboelectric nanogenerator (RF-TENG) sensor module, a data processing hardware module, and an upper computer intelligent analysis module are developed to promote intelligent motion. The resultant RF-TENG exhibits an ultra-fast response time of 17 ms, coupled with robust stability demonstrated over 4200 operational cycles, with 6% variation in output voltage. The DIMS enables immersive training by providing visual feedback on sports status and interacting with virtual games. Combined with machine learning (K-nearest neighbor), good classification results are achieved for ground-jumping techniques. In addition, it shows some potential in sports injury prediction (i.e., ankle sprains, knee hyperextension). Overall, the sensing system designed in this study has broad prospects for future applications in intelligent motion and healthcare. |
| first_indexed | 2024-04-24T11:20:55Z |
| format | Article |
| id | doaj.art-dd1eed7a29594e00a4fdba3f7dbcddd0 |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-04-24T11:20:55Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-dd1eed7a29594e00a4fdba3f7dbcddd02024-04-11T04:41:49ZengElsevieriScience2589-00422024-04-01274109615Machine learning-assisted novel recyclable flexible triboelectric nanogenerators for intelligent motionYuzhang Wen0Fengxin Sun1Zhenning Xie2Mengqi Zhang3Zida An4Bing Liu5Yuning Sun6Fei Wang7Yupeng Mao8Physical Education Department, Northeastern University, Shenyang 110819, ChinaPhysical Education Department, Northeastern University, Shenyang 110819, ChinaPhysical Education Department, Northeastern University, Shenyang 110819, ChinaPhysical Education Department, Northeastern University, Shenyang 110819, ChinaFaculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, ChinaCriminal Investigation Police University of China, Shenyang 110035, ChinaPhysical Education Department, Northeastern University, Shenyang 110819, China; Corresponding authorFaculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China; Corresponding authorPhysical Education Department, Northeastern University, Shenyang 110819, China; School of Strength and Conditioning Training, Beijing Sport University, Beijing 100084, China; Corresponding authorSummary: In the smart era, big data analysis based on sensor units is important in intelligent motion. In this study, a dance sports and injury monitoring system (DIMS) based on a recyclable flexible triboelectric nanogenerator (RF-TENG) sensor module, a data processing hardware module, and an upper computer intelligent analysis module are developed to promote intelligent motion. The resultant RF-TENG exhibits an ultra-fast response time of 17 ms, coupled with robust stability demonstrated over 4200 operational cycles, with 6% variation in output voltage. The DIMS enables immersive training by providing visual feedback on sports status and interacting with virtual games. Combined with machine learning (K-nearest neighbor), good classification results are achieved for ground-jumping techniques. In addition, it shows some potential in sports injury prediction (i.e., ankle sprains, knee hyperextension). Overall, the sensing system designed in this study has broad prospects for future applications in intelligent motion and healthcare.http://www.sciencedirect.com/science/article/pii/S258900422400837XHealth sciencesPhysicsComputer scienceMaterials science |
| spellingShingle | Yuzhang Wen Fengxin Sun Zhenning Xie Mengqi Zhang Zida An Bing Liu Yuning Sun Fei Wang Yupeng Mao Machine learning-assisted novel recyclable flexible triboelectric nanogenerators for intelligent motion iScience Health sciences Physics Computer science Materials science |
| title | Machine learning-assisted novel recyclable flexible triboelectric nanogenerators for intelligent motion |
| title_full | Machine learning-assisted novel recyclable flexible triboelectric nanogenerators for intelligent motion |
| title_fullStr | Machine learning-assisted novel recyclable flexible triboelectric nanogenerators for intelligent motion |
| title_full_unstemmed | Machine learning-assisted novel recyclable flexible triboelectric nanogenerators for intelligent motion |
| title_short | Machine learning-assisted novel recyclable flexible triboelectric nanogenerators for intelligent motion |
| title_sort | machine learning assisted novel recyclable flexible triboelectric nanogenerators for intelligent motion |
| topic | Health sciences Physics Computer science Materials science |
| url | http://www.sciencedirect.com/science/article/pii/S258900422400837X |
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