Synergy of porous and air-gap structures for pressure sensing arrays with high sensitivity and wide detection range towards machine learning-assisted gait analysis

Abstract

A smart insole based on pressure sensing arrays is a simple and effective means of gait analysis assist in the assessment of human movement and neurological health. However, these smart insoles usually fail to combine high sensitivity with a wide detection range, making them only suitable for people within a certain body weight range. Here, based on the synergy of porous and air-gap structures, we develop a high-performance and high stability smart insole, which has a sensitivity of up to 16.064 kPa−1 in a wide pressure range of 0.170 Pa to 248 kPa. After combined with Decision Tree machine learning model, gait classification and recognition can be as high as 99.96%. Based on these, a tap dance game was designed, which proves its ability to identify individual activities, and demonstrates its potential of application in the field of human-computer interaction and medical engineering.