# Self-Powered, Hybrid, Multifunctional Sensor for a Human Biomechanical Monitoring Device
## 3 Results and Discussion
From the above experimental results, it can be found that the sensitivity of the SPMS was practically linear. The experimental device of the sensitivity test is illustrated in Supplementary Information Figure S6. Figure 4d presents the results when using different thicknesses of PTFE as the friction layer of the TENG. From this chart, it can be seen that when 0.3 mm thick PTFE was used, a higher voltage output appeared at about 2 V. This test was the experimental result of friction using a single layer, smooth, flat PTFE and Al with the area of 2 cm \(\times\) 2 cm. Figure 4e shows that the piezoelectric stainless steel sheet produced different voltage outputs when using different pressure methods. Figure 4e used the positive pressing method as shown in the SPMS structure diagram, while Figure 4f shows a graph of the opposite direction pressing method data. The tablet was pressed in turn, and it can be seen from the results that the positive pressing methods had a relatively high voltage output. When using the reverse pressing method, the reduction of deformation affected the piezoelectricity performance, thereby lessening the voltage output. The output energy produced per cycle was calculated by Equation (1). \[E_{Ott}=\int\,l^{2}R\,dt \tag{1}\]  
As shown in Figure 5a-c, the testers are asked to put on a special leg-rehabilitation device when performing those actions (walk, run, jump, lim, hop); assistance was provided by the Landseed International Hospital using AQUACAST 243. In this research, the tester made several sequential actions, namely, walking, running, jumping, limping, and hopping. Based on multiple consecutive tests, the tests show outstanding repeatability. The tested movements can cause the piezoelectric device to deform and cause a piezoelectric response. Features of different motions show their unique identity waveforms. While  
Figure 4: (**a**) Current and voltage output under different external load resistance. (**b**) Power output data. (**c**) The relationship between the applied force and the electrical voltage of the SPMS. (**d**) The voltage output comparison of the sensor by using different thicknesses of PTFE film. (**e**) Voltage output by pressing the front of the PENG, and (**f**) is the voltage output from pressing on the back. wearing the rehabilitation device, the subject's movement characteristics are affected by the rehabilitation device to generate different characteristic waveforms from the original walking mode. The action cycle diagram is shown in Supplementary Information Figure S7, showing the potential of in situ monitoring for rehabilitation. Figure 5d,e were generated when the rehabilitation device was used to perform the five actions: walking, running, jumping, limping, and hopping. With different voltage and current waveforms, it can be seen that the waveforms generated by the five actions are different from each other. Therefore, human biomechanical features can be identified through machine learning assisted by multiple training runs with stainless steel piezoelectric sensors such that useful waveforms are recorded from the SPMS and pattern recognition can be obtained. Figure 5: Response to different types of action modes while wearing a leg-rehabilitation device. (**a**) Picture of rehabilitation device. (**b**) Picture of a test subject’s leg while wearing the rehabilitation device. (**c**) The relative position of the wearable rehabilitation device and the SPMS. (**d**) The data on the voltage output of five different action modes (walk, run, jump, limp, hop) while wearing a rehabilitation device. (**e**) The current measurement of five different action modes. Figure 6a schematically shows a simplified LSTM memory block, comprising several multiplicative gates that control the activation and repeatedly connect hidden layers via the Softmax Regression neural network for data identification. To completely verify the capability of the SPMS, the number of actions was set to five samples.