摘要
Respiratory muscle training can improve respiratory function by strengthening muscle mass,which is of great help to populations with respiratory system diseases and athletes.Existing respiratory muscle training methods rely on resistance that hinders breathing,and the resistance cannot be adjusted automatically.However,the detection of the user's current muscle fatigue state and precise adjustment of resistance during respiratory muscle training are crucial to training efficiency.Here,we have developed a hybrid sensor that combines a triboelectric nanogenerator and a piezoelectric nanogenerator.This hybrid sensor can simultaneously collect both high-frequency and lowfrequency signals generated by the Karman vortex street effect with low hysteresis.When the airway height is 30 mm,the sensor size is 52μm×40 mm×17 mm,the output performance of the sensor is optimal,and the minimum response amplitude for the sensor is approximately 3 mm.Under normal breathing conditions,the output peak voltage is 7 V,the current is 100μA,the charge transfer amount generated by one movement is 55 nC,the response time is 0.16 s,and the sensitivity is 0.07 V/m·s^(-1).With the help of the principal component analysis algorithm,features related to the fatigue state of muscles were extracted from the collected signals,and the accuracy rate can reach 94.4%.Subsequently,the stepper motor will rotate to adjust the resistance appropriately.We fused the hybrid sensor,machine learning,control circuits,and stepper motors and fabricated a resistance self-adaptation program.Our findings inspire researchers in the field of rehabilitation and sports training to evaluate training status and improve training efficiency.
基金
National Key R&D Project from the Minister of Science and Technology,Grant/Award Number:2023YFB3208101
National Natural Science Foundation of China,Grant/Award Number:T2125003
National Key R&D Program of China,Grant/Award Number:2022YFE0111700
China Postdoctoral Science Foundation,Grant/Award Number:2023M743446
Science Fund for Distinguished Young Scholars of Hubei Province,Grant/Award Number:2023AFA109
Guizhou Provincial Basic Research Program(Natural Science),Grant/Award Number:ZK2023-032
Guizhou Provincial Key Technology R&D Program,Grant/Award Number:2024161
Fundamental Research Funds for the Central Universities。
