Adaptive respiratory muscle trainer based on hybrid nanogenerator sensor and artificial intelligence

Abstract

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 low-frequency signals generated by the Karman vortex street effect with low hysteresis. When the airway height is 30 mm, the sensor size is 52 μm × 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 100 μA, 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.

Keywords

artificial intelligence / hybrid / nanogenerator sensor / respiratory sensor / self-adaptation

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