Unsupervised gait retraining using a wireless pressure-detecting shoe insole

He, Jade; Lippmann, Kevin; Shakoor, Najia; Ferrigno, Christopher; Wimmer, Markus A.

doi:10.1016/j.gaitpost.2019.03.021

Cited by 26 publications

(10 citation statements)

References 31 publications

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“…More recently, the OpenGo system was used for unsupervised gait retraining [76], targeting knee osteoarthritis patients. However, the participants chosen for the study were healthy subjects, who performed firstly gait training in the lab, which was then continued in an unsupervised manner, outside the lab.…”

Section: Description Of the Pedar System Together With Validation Anmentioning

confidence: 99%

Smart Socks and In-Shoe Systems: State-of-the-Art for Two Popular Technologies for Foot Motion Analysis, Sports, and Medical Applications

Drăgulinescu

Zinca

Bucur

et al. 2020

Sensors

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The present paper reviews, for the first time, to the best of our knowledge, the most recent advances in research concerning two popular devices used for foot motion analysis and health monitoring: smart socks and in-shoe systems. The first one is representative of textile-based systems, whereas the second one is one of the most used pressure sensitive insole (PSI) systems that is used as an alternative to smart socks. The proposed methods are reviewed for smart sock use in special medical applications, for gait and foot pressure analysis. The Pedar system is also shown, together with studies of validation and repeatability for Pedar and other in-shoe systems. Then, the applications of Pedar are presented, mainly in medicine and sports. Our purpose was to offer the researchers in this field a useful means to overview and select relevant information. Moreover, our review can be a starting point for new, relevant research towards improving the design and functionality of the systems, as well as extending the research towards other areas of applications using sensors in smart textiles and in-shoe systems.

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Section: Description Of the Pedar System Together With Validation Anmentioning

confidence: 99%

Smart Socks and In-Shoe Systems: State-of-the-Art for Two Popular Technologies for Foot Motion Analysis, Sports, and Medical Applications

Drăgulinescu

Zinca

Bucur

et al. 2020

Sensors

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“…One solution presented is an insole equipped with capacitive sensors with commercial solutions made by Moticon located in Munich, Germany [ 31 ]. Furthemore, others have been created for research purposes [ 32 , 33 ] in multiple different studies for gait tracking and motion analysis [ 21 , 34 , 35 , 36 ]. Other solutions such as the pedar© system designed by Novel in St. Paul, MN, USA [ 37 ] contain more than one hundred sensors so as to detect the precise pressure distribution across the foot [ 38 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

Empirical Study on Human Movement Classification Using Insole Footwear Sensor System and Machine Learning

Anderson

Choffin

Jeong

et al. 2022

Sensors

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This paper presents a plantar pressure sensor system (P2S2) integrated in the insoles of shoes to detect thirteen commonly used human movements including walking, stooping left and right, pulling a cart backward, squatting, descending, ascending stairs, running, and falling (front, back, right, left). Six force sensitive resistors (FSR) sensors were positioned on critical pressure points on the insoles to capture the electrical signature of pressure change in the various movements. A total of 34 adult participants were tested with the P2S2. The pressure data were collected and processed using a Principal Component Analysis (PCA) for input to the multiple machine learning (ML) algorithms, including k-NN, neural network and Support-Vector Machine (SVM) algorithms. The ML models were trained using four-fold cross-validation. Each fold kept subject data independent from other folds. The model proved effective with an accuracy of 86%, showing a promising result in predicting human movements using the P2S2 integrated in shoes.

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“…In the research on motor behavior, there exist many different approaches regarding the arti cial generation of augmented auditory feedback (AAF). The following AAF methods were mainly considered: natural movement sounds [18][19][20][21], error feedback [22][23][24], rhythmic auditory stimulation [25][26][27][28], soni cation [29][30][31][32][33][34][35] and musical movement feedback [36][37][38]. It has been shown that AAF is effective in a wide variety of application areas.…”

Section: Introductionmentioning

confidence: 99%

Loudness affects motion: Asymmetric volume of auditory feedback results in asymmetric gait in healthy young adults

Reh

Schmitz

Hwang

et al. 2022

Preprint

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Background: The potential of auditory feedback for motor learning in the rehabilitation of various diseases has become apparent in recent years. However, since the volume of auditory feedback has played a minor role so far and its influence has hardly been considered, we investigate the volume effect of auditory feedback on gait pattern and gait direction and its interaction with pitch.Methods: Thirty-two healthy young participants were randomly divided into two groups: Group 1 (n = 16) received a high pitch (150-250 Hz) auditory feedback; group 2 (n = 16) received a lower pitch (95-112 Hz) auditory feedback. The feedback consisted of a real-time sonification of the right and left foot ground contact. After an initial condition (no auditory feedback and full vision), both groups realized a 30-minute habituation period followed by a 30-minute asymmetry period. At any condition, the participants were asked to walk blindfolded and with auditory feedback towards a target at 15 m distance and were stopped 5 m before the target. Three different volume conditions were applied in random order during the habituation period: loud, normal, and quiet. In the subsequent asymmetry period, the three volume conditions baseline, right quiet and left quiet were applied in random order.Results: In the habituation phase, the step width from the loud to the quiet condition showed a significant interaction of volume*pitch with a decrease at high pitch (group 1) and an increase at lower pitch (group 2) (group 1: loud 1.02 ± 0.310, quiet 0.98 ± 0.301; group 2: loud 0.95 ± 0.229, quiet 1.11 ± 0.298). In the asymmetry period, a significantly increased ground contact time on the side with reduced volume could be found (right quiet: left foot 0.988 ± 0.033, right foot 1.003 ± 0.040, left quiet: left foot 1.004 ± 0.036, right foot 1.002 ± 0.033).Conclusions: Our results suggest that modifying the volume of auditory feedback can be an effective way to improve gait symmetry. This could facilitate gait therapy and rehabilitation of hemiparetic and arthroplasty patients, in particular if gait improvement based on verbal corrections and conscious motor control is limited.

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Unsupervised gait retraining using a wireless pressure-detecting shoe insole

Cited by 26 publications

References 31 publications

Smart Socks and In-Shoe Systems: State-of-the-Art for Two Popular Technologies for Foot Motion Analysis, Sports, and Medical Applications

Smart Socks and In-Shoe Systems: State-of-the-Art for Two Popular Technologies for Foot Motion Analysis, Sports, and Medical Applications

Empirical Study on Human Movement Classification Using Insole Footwear Sensor System and Machine Learning

Loudness affects motion: Asymmetric volume of auditory feedback results in asymmetric gait in healthy young adults

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