Human motion measurement methods under the background of molecular chain conformation changes.

This study proposes a human motion measurement model combining molecular chain conformation with a silicone rubber strain sensor embedded with carbon nanotubes to enhance signal response stability. An improved least mean square algorithm is used to optimize signal processing. Experimental results show the model achieves 95.12% measurement accuracy, 92.45% F1 score, 35.14 dB SNR, and 60.45 ms latency. Across different age groups and motion states such as gait, running, and jumping, the average detection error remains below 3%, and physiological monitoring errors for heart rate and oxygen saturation are as low as 0.42. The model operates stably in dynamic conditions.