Machine learning-assisted novel recyclable flexible triboelectric nanogenerators for intelligent motion

doi:10.1016/j.isci.2024.109615

. 2024 Mar 29;27(4):109615.

doi: 10.1016/j.isci.2024.109615. eCollection 2024 Apr 19.

Machine learning-assisted novel recyclable flexible triboelectric nanogenerators for intelligent motion

Yuzhang Wen¹, Fengxin Sun¹, Zhenning Xie¹, Mengqi Zhang¹, Zida An², Bing Liu³, Yuning Sun¹, Fei Wang², Yupeng Mao^{1

4}

Affiliations

¹ Physical Education Department, Northeastern University, Shenyang 110819, China.
² Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China.
³ Criminal Investigation Police University of China, Shenyang 110035, China.
⁴ School of Strength and Conditioning Training, Beijing Sport University, Beijing 100084, China.

PMID: 38632997
PMCID: PMC11022051
DOI: 10.1016/j.isci.2024.109615

Machine learning-assisted novel recyclable flexible triboelectric nanogenerators for intelligent motion

Yuzhang Wen et al. iScience. 2024.

. 2024 Mar 29;27(4):109615.

doi: 10.1016/j.isci.2024.109615. eCollection 2024 Apr 19.

Authors

Yuzhang Wen¹, Fengxin Sun¹, Zhenning Xie¹, Mengqi Zhang¹, Zida An², Bing Liu³, Yuning Sun¹, Fei Wang², Yupeng Mao^{1

4}

Affiliations

¹ Physical Education Department, Northeastern University, Shenyang 110819, China.
² Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China.
³ Criminal Investigation Police University of China, Shenyang 110035, China.
⁴ School of Strength and Conditioning Training, Beijing Sport University, Beijing 100084, China.

PMID: 38632997
PMCID: PMC11022051
DOI: 10.1016/j.isci.2024.109615

Abstract

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.

Keywords: Computer science; Health sciences; Materials science; Physics.

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Conflict of interest statement

The authors declare no competing interests.

Figures

**Figure 1**
The concept, structure, and design of a dance sports and injury monitoring system (A) Concept diagram of dance sports injury monitoring system. (B) Structure diagram and practical application diagram of RF-TENG. (Ⅰ) Structure diagram of RF-TENG. (Ⅱ) Practical application diagram of RF-TENG. (C) Recycling and preparation process of disposable wood fiber tissue. (D) Optical images, surface electron micrographs (SEM) of the tissue and PTFE. (Ⅰ) Optical image of the tissue surface under bending state. (Ⅱ) SEM of the tissue surface. (Ⅲ) Optical image of PTFE film surface under bending state. (Ⅳ) SEM of the PTFE film surface. (E) Functional flow diagram of the dance sports and injury monitoring system. (F) Data processing flowchart based on KNN.

**Figure 2**
The preparation, working mechanism, simulated potential, and material selection of RF-TENG (A) The preparation process of RF-TENG. (B) The working mechanism of RF-TENG. (C) The potential distribution of RF-TENG is simulated by COMSOL Multiphysics software. (D) The output voltage is generated by the contact separation of wood fiber tissue and different negative triboelectric layers. (E) The output voltage is generated by the contact separation of PTFE film and different material composition tissues. (F) Switching polarity test of RF-TENG.

**Figure 3**
RF-TENG sensing properties testing (A) Output voltage of RF-TENG at different frequencies. (B) Output voltage and response of RF-TENG at different frequencies. (C) Output voltage of RF-TENG at different angles. (D) Linear fitting of RF-TENG output voltage at different angles. (E) Output voltage of RF-TENG at different temperatures. (F) Output voltage and response of RF-TENG at different temperatures. (G) Output voltage and current of RF-TENG at different load resistances. (H) Output power of RF-TENG at different load resistance. (I) Response and recovery time of single press RF-TENG.

**Figure 4**
RF-TENG is demonstrated as a power supply for various applications (A) Circuit diagram of RF-TENG supplying power to the device by charging capacitors. (B) Charging curves of RF-TENG for different capacitors. (C) RF-TENG utilizes the power collected by capacitors to drive small calculators and watches. (Ⅰ) Charging and discharging curves for supplying power to the device. (Ⅱ) Optical diagram of the device after driving. (D) Circuit diagram and optical image of RF-TENG lighting up 28 commercial green LEDs by tapping. (Ⅰ) Circuit diagram of RF-TENG for lighting LED. (Ⅱ) The letters “RF” are composed of 28 LEDs. (Ⅲ) Tap RF-TENG to light up the letters “RF”.

**Figure 5**
The application of DIMS (A) Schematic diagram of DIMS. (B) DIMS for dance ground-jumping techniques monitoring. (Ⅰ) The output voltage generated by the knee joint during dancers’ ground-jumping. (Ⅱ) The output voltage generated by the ankle joint during dancers’ ground-jumping. (C) DIMS for monitoring ankle sprains. (D) Output voltage of normal flexion and extension and post-injury flexion and extension of the ankle joint. (E) DIMS for monitoring knee hyperextension. (F) Flow diagram of DIMS for judging dance sports status. (G) Flow diagram of DIMS for virtual game control.

**Figure 6**
Recognition of dance ground-jumping techniques based on KNN algorithm (A) Flowchart for dance ground-jumping techniques recognition based on KNN algorithm. (B) Signal time-domain characteristic diagram. (C and D) Scatterplot of dance floor jumping techniques (small, medium, and big jumps). (E) Minimum classification error diagram. (F and G) Confusion matrix of training results and test results.

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References

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[1] Fang Y., Tang T., Li Y., Hou C., Wen F., Yang Z., Chen T., Sun L., Liu H., Lee C. A high-performance triboelectric-electromagnetic hybrid wind energy harvester based on rotational tapered rollers aiming at outdoor IoT applications. iScience. 2021;24 doi: 10.1016/j.isci.2021.102300. - DOI - PMC - PubMed

[2] Fang Y., Tang T., Li Y., Hou C., Wen F., Yang Z., Chen T., Sun L., Liu H., Lee C. A high-performance triboelectric-electromagnetic hybrid wind energy harvester based on rotational tapered rollers aiming at outdoor IoT applications. iScience. 2021;24 doi: 10.1016/j.isci.2021.102300. - DOI - PMC - PubMed

[3] Pu X., An S., Tang Q., Guo H., Hu C. Wearable triboelectric sensors for biomedical monitoring and human-machine interface. iScience. 2021;24 doi: 10.1016/j.isci.2020.102027. - DOI - PMC - PubMed

[4] Pu X., An S., Tang Q., Guo H., Hu C. Wearable triboelectric sensors for biomedical monitoring and human-machine interface. iScience. 2021;24 doi: 10.1016/j.isci.2020.102027. - DOI - PMC - PubMed

[5] Wu T., Deng H., Sun Z., Zhang X., Lee C., Zhang X. Intelligent soft robotic fingers with multi-modality perception ability. iScience. 2023;26 doi: 10.1016/j.isci.2023.107249. - DOI - PMC - PubMed

[6] Wu T., Deng H., Sun Z., Zhang X., Lee C., Zhang X. Intelligent soft robotic fingers with multi-modality perception ability. iScience. 2023;26 doi: 10.1016/j.isci.2023.107249. - DOI - PMC - PubMed

[7] Ying B., Liu X. Skin-like hydrogel devices for wearable sensing, soft robotics and beyond. iScience. 2021;24 doi: 10.1016/j.isci.2021.103174. - DOI - PMC - PubMed

[8] Ying B., Liu X. Skin-like hydrogel devices for wearable sensing, soft robotics and beyond. iScience. 2021;24 doi: 10.1016/j.isci.2021.103174. - DOI - PMC - PubMed

[9] Mao Y., Wen Y., Liu B., Sun F., Zhu Y., Wang J., Zhang R., Yu Z., Chu L., Zhou A. Flexible wearable intelligent sensing system for wheelchair sports monitoring. iScience. 2023;26 doi: 10.1016/j.isci.2023.108126. - DOI - PMC - PubMed

[10] Mao Y., Wen Y., Liu B., Sun F., Zhu Y., Wang J., Zhang R., Yu Z., Chu L., Zhou A. Flexible wearable intelligent sensing system for wheelchair sports monitoring. iScience. 2023;26 doi: 10.1016/j.isci.2023.108126. - DOI - PMC - PubMed

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Machine learning-assisted novel recyclable flexible triboelectric nanogenerators for intelligent motion

Affiliations

Machine learning-assisted novel recyclable flexible triboelectric nanogenerators for intelligent motion

Authors

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Abstract

Conflict of interest statement

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