Machine-learning-based children’s pathological gait classification with low-cost gait-recognition system

Abstract

Background: Pathological gaits of children may lead to terrible diseases, such as osteoarthritis or scoliosis. By monitoring the gait pattern of a child, proper therapeutic measures can be recommended to avoid the terrible consequence. However, low-cost systems for pathological gait recognition of children automatically have not been on market yet. Our goal was to design a low-cost gait-recognition system for children with only pressure information.Methods: In this study, we design a pathological gait-recognition system (PGRS) with an 8 × 8 pressure-sensor array. An intelligent gait-recognition method (IGRM) based on machine learning and pure plantar pressure information is also proposed in static and dynamic sections to realize high accuracy and good real-time performance. To verifying the recognition effect, a total of seventeen children were recruited in the experiments wearing PGRS to recognize three pathological gaits (toe in, toe out, and flat) and normal gait. Children are asked to walk naturally on level ground in the dynamic section or stand naturally and comfortably in the static section. The evaluation of the performance of recognition results included stratified 10-fold cross-validation with recall, precision, and a time cost as metrics.Results: The experimental results show that all of the IGRMs have been identified with a practically applicable degree of average accuracy either in the dynamic or static section. Experimental results indicate that the IGRM has 92.41% and 97.79% recognition accuracy respectively in the static and dynamic sections. And we find methods in the static section have less recognition accuracy due to the unnatural gesture of children when standing.Conclusions: In this study, a low-cost PGRS has been verified and realize feasibility, highly average precision, and good real-time performance of gait recognition. The experimental results reveal the potential for the computer supervision of non-pathological and pathological gaits in the plantar-pressure patterns of children and for providing feedback in the application of gait-abnormality rectification.

Current developments of robotic hip exoskeleton toward sensing, decision, and actuation: A review

“…Song and Collins (2021) used a force sensor placed at the heel to detect heel-strike events. We developed an 8 × 8 FSR array-based shoes for gait event detection and children’s pathological gait identification as well (Xu et al, 2021). The advantage of using the plantar pressure sensor to detect the segmentation point of gait is that it is simple and accurate.…”

Orthotic Prescription for Pediatric Flexible Flat Feet using Convolutional Neural Networks

“…Xu et al developed a gait recognition system using pressure sensors. While this study successfully classifies flat feet using the self-organizing-map (SOM) neural network (NN) and support vector machine (SVM), the study does not address the classification between flexible flatfoot or rigid flatfoot or the needs of orthotic prescription [12]. Li's study collected data from smart-insoles embedding baropodometry, stabilometry, and biomechanical sensors.…”