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 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.

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Figure S1 .
Figure S1.RF-TENG output voltage at different frequencies, related to Figure 3.

Figure S2 .
Figure S2.Schematic diagram of linear motor simulation angle test, related to Figure 3.

Figure S3 .
Figure S3.RF-TENG output voltage and response at different angles, related to Figure 3.

Figure S4 .FigureFigure S5 .
Figure S4.Voltage signals of the RF-TENG generated by an elastic sphere, related toFigure

Figure S6 .
Figure S6.Output voltage of RF-TENG after 4200 cycles and its detail, related to Figure 3.

Figure S7 .
Figure S7.RF-TENG output voltage after initial, 15 days and 30 days of resting condition, related to Figure 3.

Figure S8 .
Figure S8.The optical diagram of RF-TENG, related to STAR Methods.

Figure S9 .
Figure S9.The optical diagram of oscilloscopes, related to STAR Methods.

Figure S10 .
Figure S10.Optical diagram of the data processing hardware modules, related to STAR Methods.

Figure S11 .
Figure S11.Circuit diagram of data processing hardware module,related to STAR Methods.

Figure S12 .
Figure S12.Testing the transmission distance of data processing module, related to STAR Methods.