A flexible dual-structured MXene for ultra-sensitive and ultra-wide monitoring of anatomical and physiological movements

Flexible devices for capturing anatomical and physiological movements are essential for improving the quality of life in, e.g., disease monitoring, physical rehabilitation, and assistance for people with cognitive disorders. They require high sensitivity, wide detection range, multi-functional applicability, etc. Nevertheless, the current devices and technologies face the challenge of simultaneous achievement of these features, mainly sensitivity and detection range, and thus their utility and applications are limited. Herein we report on the design and production of dual-microstructures of surface micro-bumps and internal hollow pores in a conductive material, an MXene, for obtaining a multifunctional high-performance pressure sensor. The designed sensor has ultra-high sensitivity (401.01 kPa(-1), 0-12 kPa), a wide detection range (1.96 Pa to 100 kPa), and stability in a wide range of human physiological and anatomical movement types, including wide range movement (joint movement and gesture), slight movement (muscle movement and wrist pulse), and synchronous movement (respiration, carotid artery, and head movement). With data-mining methods, we show an ultra-sensitive ability to extract gesture behavioral information and physiological information from the sensor signals, and its implications for human health. These performances could be used as a shuttling pad for motor function assessment and dexterous human-robot interaction for rehabilitation robots and intelligent prosthetics.

Automated summary

This study presents the design and production of a multifunctional high-performance pressure sensor with dual microstructures of surface micro-bumps and internal hollow pores in a conductive material, an MXene. The sensor demonstrates ultra-high sensitivity, wide detection range, and stability in various human physiological and anatomical movement types. Data mining methods are used to extract gesture behavioral information and physiological information from the sensor signals, showing potential for motor function assessment and human-robot interaction applications.