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 17 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 tenfold 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% intra-subject recognition accuracy, and 85.78% and 78.81% inter-subject 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.

[Retracted] Analysis of Gait Characteristics of Patients with Knee Arthritis Based on Human Posture Estimation

“…In recent years, with the maturity of deep learning technology, machine learning has gradually been applied in various areas of the medical field, such as precise cell classification [ 10 ] and image analysis for pathology [ 11 , 12 ]. In addition, human pose estimation has broad application prospects in computer vision, pattern recognition, video/image sequence processing, and other technologies [ 13 ]. Cao et al [ 14 ] developed the part affinity field (PAF) nonparametric representation method to learn how to associate body parts with individuals in an image to detect real-time multiperson two-dimensional poses.…”

Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction

“…Zakaria et al [ 16 ] classified Autism Spectrum Disorder (ASD) children’s gait from normal gait by a depth camera and found the accuracy of the support vector machine (SVM) classifier was 98.67% and the Naive Bayes classifier had an accuracy of 99.66%. Chen et al [ 17 ] and Xu et al [ 18 ] classified Parkinsonian gait classification using monocular video imaging techniques and kernel-based principal component analysis (PCA) [ 17 , 18 ]. These studies demonstrate the advancement of sensor technology and its capacity to collect kinematic and electrophysiological information during walking, which has greatly promoted the development of automated gait recognition technology.…”

“…Using the Kinect skeleton tracking technology, the spatial information of the key points of the human skeleton can be accurately obtained, processed through the algorithm, and converted into the joint angle during walking. Unlike other studies that only collect ankle joint information [ 12 ], footstep information [ 18 ], trunk tilt angle [ 22 ], or data from a public database [ 23 ], we collected all joint kinetic data available for ML processing. Moreover, previous studies detected the whole fall event [ 12 , 18 , 22 , 23 , 24 ], while our study focused on detecting abnormal gaits (pelvic obliquity gait and knee hyperextension gait) aiming at the prevention of falls.…”

Flexible Machine Learning Algorithms for Clinical Gait Assessment Tools

“…An alternative to laboratory-based 3D CGA are wearable sensor systems coupled with machine learning analytics [8][9][10][11][12]. These sensor systems use predictive machine learning methods to automatically partition and analyse gait (e.g., foot-contact and foot-off events) from sensor signals (e.g., inertial measurement units (IMUs)) [8][9][10][11][12][13]. These sensor systems are mobile, cost-effective and allow the automation of some tasks that currently require laboratory equipment and skilled knowledge [8][9][10][11][12][13].…”

“…These sensor systems use predictive machine learning methods to automatically partition and analyse gait (e.g., foot-contact and foot-off events) from sensor signals (e.g., inertial measurement units (IMUs)) [8][9][10][11][12][13]. These sensor systems are mobile, cost-effective and allow the automation of some tasks that currently require laboratory equipment and skilled knowledge [8][9][10][11][12][13]. For example, partitioning gait into stance and swing phases is an important step in 3D CGA.…”