# Self-Powered, Hybrid, Multifunctional Sensor for a Human Biomechanical Monitoring Device
## 4 Conclusions  
In conclusion, we developed a hybrid, curved spacer supported with a high-performance zigzag-shaped self-powered multifunctional sensor (SPMS) and data analytics for gait analysis. The piezoelectric actuators provided high and stable output performance with a natural curved spacer generated by the special polyimide actuator support of the SPMS's active mechanism. Open circuit voltage reached 103 V and the short circuit current reached \(\sim\)300 \(\mu\)A. The zigzag-shaped structure in the SPMS's triboelectric nanogenerator provided a greater contact area to improve voltage and current output. The hybridization of the piezoelectric actuators and zigzag-shaped triboelectric nanogenerator provided a higher, stable power generation. As demonstrated, the zigzag-shaped self-powered multifunctional sensor was adopted as the sensor in a leg-rehabilitation device. To provide effective medical diagnosis and gait monitoring, a self-powered sensor in a leg-rehabilitation device was realized. In the developed automated system, the biomechanical force that was actuated was used to identify the individual movement characteristics of human individuals using the device. The recognition rate of the personal sequences of gaits from the SPMS signal was 81.8% when using the deep learning LSTM model. This research demonstrates the applications of the SPMS, showing the future potential and great opportunity in wearable, sensor-transmitter devices for healthcare and biomedical applications.  
The following are available online at [[https://www.mdpi.com/2076-341](https://www.mdpi.com/2076-341)]([https://www.mdpi.com/2076-341](https://www.mdpi.com/2076-341)) 7/11/2/519/s1, Figure S1: The operation of the Zigzag shape electrostatic generator, the upper layer is a PTFE film containing microstructures, the lower layer is Al film. From left to right is the mode of action of TENG, Figure S2: The method of use sand papers to grind the surface of PTFE. Fix the PTFE and repeat the grinding action in parallel direction about 20 cycles, Figure S3: Long term Stability tested of PENG for three days. The output voltages of the PENG operating at 1 Hz for 10 min per day, Figure S4: Frequency test. The output of the NG with the increase in driving frequency, showing  
Figure 6: Classification of the SPMS motion identification by using machine learning via LSTM arithmetic. (**a**) The LSTM simplified architecture. (**b**) Confusion matrix for biomechanical motion-level classification corresponding to walking, running, jumping, hopping, and limping.  
its good stability, Figure S5: Validated tests of polarity. The peak voltage was obtained by (**a**) the forward connection and (**b**) reverse connection, Figure S6: Schematic diagram of the experimental method for the applied force-voltage output diagram, Figure S7: Isolated motion schematic image of five different action (walk, run, jump, imp, hop) in rehabilitation device application, Table S1: Tester Information.  
Conceptualization, Y.H.L., T.H.L. and Y.K.F.; Data curation, Y.H.L. and Y.K.F.; Formal analysis, Y.H.L.; Funding acquisition, Y.K.F.; Methodology, Y.H.L., T.H.L. and Y.K.F.; Project administration, Y.H.L.; Resources, T.H.L.; Software, Y.H.L., H.H.L. and J.W.; Supervision, T.H.L. and Y.K.F.; Validation, J.W.; Writing--original draft, Y.H.L. and J.W.; Writing--review and editing, Y.H.L. and J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Research Project of the NCU-LSH-107-B-008, a project supported by Landseed International Hospital and by the ministry of science and technology, and Taiwan under contract nos. MOST 105-2221-E-008-049-MY3 and MOST 108-2221-E-008-070-MY3, respectively.  
The authors declare no conflict of interest.