A Piezoionic Hydrogel-Based Electrochemical Strain Sensor for Self-Powered Pulse Monitoring and Machine Learning-Assisted Speech Recognition

Hydrogel-based strain sensors are attractive for wearable electronics owing to their stretchability, self-healing, and biocompatibility. However, conventional hydrogel-based strain sensors relying on the piezoresistive effect suffer from limited sensitivity to small forces. Here, we present an ultrasensitive hydrogel-based strain sensor that operates on a piezoionic response mechanism for electrochemical measurement. The hydrogel exhibits a Young's modulus of ∼45 kPa, enabling high sensitivity to subtle forces, while its low residual strain (2.6%) and hysteresis (2.5%) ensure excellent elasticity and stability under cyclic loading. Moreover, 741% enhancement in the piezoionic coefficient of the hydrogel was achieved with a 10% increase in water content. Notably, the sensor demonstrates self-powered characteristics, a fast response time (40 ms), a short decay time (90 ms) at room temperature, and a high gauge factor of 1242. These features enable high-resolution dynamic strain detection, demonstrated by capturing detailed pulse waveforms across arterial sites, exercise states, and breath-holding, as well as achieving 96.3% accuracy in machine learning-assisted speech recognition through precise laryngeal monitoring. This work provides a new strategy for highly efficient dynamic strain detection and has broad potential for health monitoring and human-machine interfaces.