Fatigue Detection System Based On Triboelectric Nanogenerator

The Internet of People is a new concept in which wearable device(WD)is becoming a key technology to change the daily life of human beings.In order to achieve the functions of collecting physical activity and various biological signals,WD mostly uses malleable materials.The Triboelectric Nanogenerator(TENG)is a suitable choice for the sensor.In the past decade,TENG has received extensive attention as an emerging technology to efficiently convert low-frequency mechanical energy into electrical energy.Since the TENG self-powered sensor can directly use mechanical motion to output a voltage waveform that reflects the law of motion,it is in line with the requirements of WD for low power consumption,miniaturization,and low latency.At the same time,most of the users of WD are sports enthusiasts.Along with long-term or high-intensity exercise,sports enthusiasts often cause many physical injuries due to exercise fatigue,which affects their daily life.Therefore,it is particularly important to design a low-power fatigue monitoring system.In this subject,human fatigue mainly refers to exercise fatigue.In order to predict fatigue in advance and make reminders,a fatigue detection system based on TENG is designed.Aiming at the physiological phenomenon that people will be fatigued when exercising,which will cause physical,psychological,and economic damage and potential threats,the electrical performance characteristics of TENG combined with the inertial measurement unit(IMU)are used to collect the human body.The acceleration of the ankle during walking and the method of gait division,and finally the fatigue detection model is trained through machine learning to obtain the result of fatigue judgment.Specifically,the sensor based on the TENG design is placed on the knee,and the mechanical energy generated by the bending of the knee when a person is walking is converted into a sufficiently high and regular voltage signal.The time point of the gait cycle when the user is walking is divided by intercepting the voltage cycle,so as to help the computer to divide the gait,extract the eigenvalues in each step,and finally complete the fatigue judgment.In addition,in order to calibrate the human fatigue data,the COSMED K5 metabolism tester was used.By measuring human biosignals to calculate changes in metabolic rate,it is possible to distinguish and calibrate fatigue states.The purpose of this topic is to use TENG technology for accurate gait division,and then use the support vector machine algorithm to predict the user's fatigue in advance,so as to avoid unnecessary damage caused by fatigue.In the field experimental test,the knee joint pressure sensor based on TENG showed good electrical performance,with obvious periodic regularity,which could easily complete the tester's gait division,and provided complete gait information for fatigue judgment.Foundation.The performance test experiment of the fatigue monitoring system based on TENG was completed,and the fatigue state of the tester was judged,and the fatigue state detection accuracy rate reached 79.28%.The experimental results show that the system can more accurately predict the lower limb muscle fatigue caused by walking,which meets the design indicators of this subject.