Deep learning approach to estimate foot pressure distribution in walking with application for a cost-effective insole system

Frederick Mun,Ahnryul Choi

doi:10.1186/s12984-022-00987-8

Abstract

BackgroundFoot pressure distribution can be used as a quantitative parameter for evaluating anatomical deformity of the foot and for diagnosing and treating pathological gait, falling, and pressure sores in diabetes. The objective of this study was to propose a deep learning model that could predict pressure distribution of the whole foot based on information obtained from a small number of pressure sensors in an insole.MethodsTwenty young and twenty older adults walked a straight pathway at a preferred speed with a Pedar-X system in anti-skid socks. A long short-term memory (LSTM) model was used to predict foot pressure distribution. Pressure values of nine major sensors and the remaining 90 sensors in a Pedar-X system were used as input and output for the model, respectively. The performance of the proposed LSTM structure was compared with that of a traditionally used adaptive neuro-fuzzy interference system (ANFIS). A low-cost insole system consisting of a small number of pressure sensors was fabricated. A gait experiment was additionally performed with five young and five older adults, excluding subjects who were used to construct models. The Pedar-X system placed parallelly on top of the insole prototype developed in this study was in anti-skid socks. Sensor values from a low-cost insole prototype were used as input of the LSTM model. The accuracy of the model was evaluated by applying a leave-one-out cross-validation.ResultsCorrelation coefficient and relative root mean square error (RMSE) of the LSTM model were 0.98 (0.92 ~ 0.99) and 7.9 ± 2.3%, respectively, higher than those of the ANFIS model. Additionally, the usefulness of the proposed LSTM model for fabricating a low-cost insole prototype with a small number of sensors was confirmed, showing a correlation coefficient of 0.63 to 0.97 and a relative RMSE of 12.7 ± 7.4%.ConclusionsThis model can be used as an algorithm to develop a low-cost portable smart insole system to monitor age-related physiological and anatomical alterations in foot. This model has the potential to evaluate clinical rehabilitation status of patients with pathological gait, falling, and various foot pathologies when more data of patients with various diseases are accumulated for training.

Highlights

The human foot constitutes a basic surface that interacts directly with the environment to facilitate bipedal locomotion
Pressure values for sensors attached to the hindfoot were relatively high in the initial phase, while pressure values for sensors attached to the forefoot (Sensors # 67, 70, 72, and 84) appeared to be high in the end phase
In the young adult group, those with Relative root mean square error (rRMSE) of 6-9% accounted for 23%

Summary

Introduction

The human foot constitutes a basic surface that interacts directly with the environment to facilitate bipedal locomotion. Sacco et al [8] have evaluated foot pressure distribution among patients with diabetic neuropathy during walking They reported that the pressure was increased throughout the foot as symptoms worsened, with the pressure being centered in the forefoot [8]. Diabetic neuropathy can lead to slow joint movement and foot deformities, causing alterations in daily movement, including walking Such movement alteration may cause excessive pressure on specific parts of the foot. Repeated load eventually can lead to skin wounds and ulcers [9] If these changes in foot pressure are detected early in diabetic patients, the development of foot disease and ulcers can be prevented in advance. Foot pressure distribution can be used as a quantitative parameter for evaluating anatomical deformity of the foot and for diagnosing and treating pathological gait, falling, and pressure sores in diabetes. The objective of this study was to propose a deep learning model that could predict pressure distribution of the whole foot based on information obtained from a small number of pressure sensors in an insole

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