Abstract
BackgroundPathological 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.MethodsIn 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.ResultsThe 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.ConclusionsIn 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.
Highlights
Pathological gaits of children may lead to terrible diseases, such as osteoarthritis or scoliosis
Regardless of its powerful ability for solving non-linear problems, neural network (NN) is a black box comparing with explainable and intuitive support vector machine (SVM). In this part, a low-cost pathological gait-recognition system (PGRS) is built to measure children’s plantar-pressure data during walking, and children’s experiments in dynamic and static sections are designed to verify the performance of recall, precision, and time cost of the intelligent gait-recognition method (IGRM)
An independent t-test shows no significant difference between linear discriminant analysis (LDA) + NN and LDA + SVMlin in terms of accuracy (P-value = 0.702 in the dynamic section, P-value = 0.765 in the static section) and time cost (P-value = 0.064 in the dynamic section, P = 0.388 in the static section)
Summary
Pathological gaits of children may lead to terrible diseases, such as osteoarthritis or scoliosis. Our goal was to design a low-cost gait-recognition system for children with only pressure information. To monitoring the pathological gait pattern of a human, various bio-signals are adopted among which kinematics information and plantar-pressure show more potential for their easy to measure and explain [1, 2]. These high-dimensional bio-signals indicate complex states of human muscles and joints [3], which cause difficulty to interpret directly by conventional kinematics or kinetics. Faragó et al [9, 10] proposed a framework for classifying normal walking, heel-walking, and toe-walking based on the cross-correlation of plantar pressures with corresponding lower-limb EMG signals
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