Robust and breathable all-textile gait analysis platform based on LeNet convolutional neural networks and embroidery

technique

Repetitive Strain Injury (RSI) and its related Musculoskeletal Disorders (MSDs) symptoms not only bring pathologicalpains to people, but also limit their physical activities and work abilities.The pathological changes in the footprints and other gait features provide a new way for the real-time monitoring and nursing of the recovery degrees of MSDs symptoms. In this work, based on the

conformable,breathable, and lightweight all-fabric pressure sensing material, a novel highly robust universal platform ATPSA-LeNet,consisting of the all-textile pressure sensors array (ATPSA) and LeNet convolutional neural networks, has been proposed.

Standing posturesand authentication of volunteers have been identiffed from their gait characteristics with high accuracy. The

ATPSA-LeNet platform could directly convert foot pressure values into input data for the deep learning networs through the

ATPSA, which greatly reduces the artiffcial errors arose from the spatial arranging of the sensors array and image data processing.

Besides, ATPSA is more seamless and comfortable due to its improved compactness and breathability. Failures of sensing units also did not signiffcantly decrease the overall accuracy. The proposed ATPSA-LeNet platform would provide a great prospect for

extracting the high-dimensional spatial information contained in human gait features in many ffelds, such as clinical medicine,

authentication,and criminal investigation.

Zhao M, Xu H, Zhong W, et al. Robust and breathable all-textile gait analysis platform based on LeNet convolutional

neural networks and embroidery technique[J]. Sensors and Actuators A: Physical, 2023, 360: 114549.