Shows effective cervical posture recognition in flexible sensor systems, indicating novel health management applications.
in the low-pressure regime while maintaining robust sensing performance across an extended working range (up to 390 kPa). Furthermore, it exhibits fast response and recovery times (31/34 ms) and superior long-term durability over 3000 loading-unloading cycles. This outstanding electromechanical performance enables comprehensive multiscale physiological monitoring, capable of capturing small pressure variations down to 1 kPa including respiratory patterns and arterial pulses, as well as high-load scenarios such as joint flexion and gait monitoring. Building upon these capabilities, a real-time cervical posture recognition system was implemented as a representative paradigm of intelligent health management. By integrating a logistic regression machine learning classifier, the system attained a classification accuracy of 98.5% on the test set. This work proposes a rational structural engineering strategy, providing a versatile and dependable sensing platform for next-generation intelligent health monitoring and human-machine interfaces.
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Wu et al. (2026) studied this question.
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