A wearable AI sensing system for non-invasive and experimental characterization of biological tissue hardness

• Wearable AI sensing system to monitor biological tissue hardness non-invasively. • Piezoresistive sensor, printable material, machine learning combined for precision. • Accurate detection of muscle tissue hardness in raw and baked chicken, pork, beef. • Potential for high-precision healthcare devices and individualized diagnostics. Continuous non-clinical monitoring of biological tissue hardness is crucial to prevent and diagnose internal unwellness, skin diseases, and related illnesses. However, current monitoring instruments are limited by the need for unwieldy systems or the inability to quantify hardness indices. In the present study, we report a soft, printed, and wearable monitoring system designed to overcome these limitations by combining a conformal piezoresistive touch sensor, functionalized printable materials, an active pressure adaptation device, and advanced machine learning for real-time, wearable, and continuous tissue hardness monitoring. Through optimization of device structure, data sampling strategy, and system integration, this touch sensor exhibits improved interfacial performance while identifying the hardness of muscle tissues at elevated temperatures. Accurate, automatic identification of the raw and baked states of chicken, pork, and beef muscle tissues by touch proves the utility of this device as a reliable wearable hardness monitoring system. Overall, this system can be used in high-precision touch sensors for healthcare devices and for the development of individualized diagnostic schemes.