Monitoring of training, especially the use of soft devices, is difficult but has become more and more important for human health. In this paper, a self-power flexible piezoelectric sensing system for badminton training based on polyvinylidene fluoride (PVDF) piezoelectric film sensor is designed and experimentally verified. The sensing array is divided as 3 ◊ 3 chessboard-like areas, which contain thin-film transductors to determine the hitting position and force. The system owns a linear relationship between force and generated electric signal with a sensitivity of 0.377 V/N and the minimum sampling interval of 1s. Furthermore, the piezoelectric Lead Zirconium titanite (PZT) patch behind the racket is employed as a power source to support the sensing circle. This work paves a new way for the application of artificial intelligence in the human health area and the Internet of Things.

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Breathing monitoring is important in the health and sports area. In this paper, a wireless self-powered health system for breathing monitoring is designed using the piezoelectric devices and buckled n-shape and L-shape structure. A cotton belt is used to tie the system to the chest and deliver the force stimulated by the chest cavity enlargement caused by the breathing, which can be collected and measured based on the three-dimension nshape and L-shape structure. Three hard lead zirconate titanate (PZT) patches are used as the energy source to support the sensing circuit and soft piezoelectric polyvinylidene fluoride (PVDF) works as the sensor to measure the stretching force, which are placed at the gap between the 3D-printed n-shape and L-shape structure, making the measurement more precise. Breathing signal comparison between commercially used batterypowered respiration belts shows the efficiency of the piezoelectric devices on the accuracy, precision, and recall factors. The system is also used in practical experiments and it is found that the intense actions have a similar peak-peak value of breathing wavefront but a much higher frequency of respiration. This design can be used in health monitoring and sports training, which also shows the potential of data acquisition for disease diagnosis applications.