Abstract
Abstract The global society is increasingly facing the challenges that reduce mobility, quality of life, and independence. Gait disorders are often both a result of, and predictor of further issues, tied to the 15 million stroke patients annually world-wide. These individuals face a number of gait abnormalities including drop foot that is a pathological condition, limiting patients' ability to lift the foot from the ground during the swing phase of walking. In this research work, we introduce a novel smart textile system, MagicSox that is woven with multiple sensors distributed over the surface of the foot. The overarching goal of MagicSox is to quantify the gait abnormalities in remote settings such as patients' homes so that clinicians and physical therapists can assess their patients on daily basis. The paper provides a detailed architecture of MagicSox that leverages the computing and communication capabilities of a modern Internet of Things (IoT) processor, the Intel Curie. We have developed an Android smart phone app that uses Bluetooth low energy (BLE) and automates the multi-sensor data collection from MagicSox. In terms of signal processing of wearable sensor data, we adopted multiplication of backward differences (MOBD) to analyze the multi-modal time series data to distinguish drop foot events from normal walking cycles. We pursued a usability study on 12 healthy participants who were asked to walk normally and also to simulate drop foot cycles. We developed support vector machine (SVM) classifiers to analyze the data. The classification resulted in the accuracy of drop foot detection varying from 73.38 % − 99.02 % . The promising results now encourage us to evaluate MagicSox on stroke patients in future studies.
Highlights
The global medical community is attentive of Gait since the society witnesses the increasing population with decreased mobility due to medical conditions such as stroke, Parkinson’s, arthritis, and other age-related condi5 tions [1, 2, 3]
In addition to the on-board module consisting of multiple sensors and an Intel Curie microcontroller (with Bluetooth Low Energy(BLE)), MagicSox is embedded with algorithms such as 30 multiplication of backward differences (MOBD) to accurately differentiate between a standard walking step and a drop-foot step
In this research article we introduced a novel wearable health monitoring system, MagicSox, that fuses the capabilities of smart textiles with Internetof-things to detect and communicate gait abnormalities such as drop foot
Summary
The global medical community is attentive of Gait since the society witnesses the increasing population with decreased mobility due to medical conditions such as stroke, Parkinson’s, arthritis, and other age-related condi tions [1, 2, 3]. Drop foot could be a symptom of underlying disease or an effect of neurological conditions. 15 In this paper, we examine the use of smart textiles to measure the gait movements and differentiate between a healthy movement and a drop foot movement. We have developed a smart textile called 20 “MagicSox” to collect data on various attributes of motion on an individual’s lower extremities, designed to monitor patients with neurological diseases during their time of rehabilitation. In addition to the on-board module consisting of multiple sensors and an Intel Curie microcontroller (with Bluetooth Low Energy(BLE)), MagicSox is embedded with algorithms such as 30 multiplication of backward differences (MOBD) to accurately differentiate between a standard walking step and a drop-foot step. We focus on “drop foot” ( known as foot drop) that is not a disease but a type of gait abnormality that indicates someone’s inability
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