Abstract
The quantification of gait is uniquely facilitated through the conformal wearable and wireless inertial sensor system, which consists of a profile comparable to a bandage. These attributes advance the ability to quantify hemiplegic gait in consideration of the hemiplegic affected leg and unaffected leg. The recorded inertial sensor data, which is inclusive of the gyroscope signal, can be readily transmitted by wireless means to a secure Cloud. Incorporating Python to automate the post-processing of the gyroscope signal data can enable the development of a feature set suitable for a machine learning platform, such as the Waikato Environment for Knowledge Analysis (WEKA). An assortment of machine learning algorithms, such as the multilayer perceptron neural network, J48 decision tree, random forest, K-nearest neighbors, logistic regression, and naïve Bayes, were evaluated in terms of classification accuracy and time to develop the machine learning model. The K-nearest neighbors achieved optimal performance based on classification accuracy achieved for differentiating between the hemiplegic affected leg and unaffected leg for gait and the time to establish the machine learning model. The achievements of this research endeavor demonstrate the utility of amalgamating the conformal wearable and wireless inertial sensor with machine learning algorithms for distinguishing the hemiplegic affected leg and unaffected leg during gait.
Highlights
The opportunity to quantify gait patterns is an inherent aspect for the feedback of rehabilitation efficacy
The amalgamation of machine learning with wearable and wireless inertial sensor systems enables the ability to distinguish between various scenarios, such as hemiplegic gait for the affected leg contrasted to the unaffected leg [1, 7,8,9,10,11, 14]
The objective of the research endeavor was to differentiate between an affected leg and an unaffected leg for hemiplegic gait using the conformal wearable and wireless inertial sensor system provided by the BioStamp nPoint and an assortment of machine learning algorithms, such as the multilayer perceptron neural network, J48 decision tree, random forest, K-nearest neighbors, logistic regression, and naïve Bayes
Summary
The opportunity to quantify gait patterns is an inherent aspect for the feedback of rehabilitation efficacy. The objective of the research endeavor was to differentiate between an affected leg and an unaffected leg for hemiplegic gait using the conformal wearable and wireless inertial sensor system provided by the BioStamp nPoint and an assortment of machine learning algorithms, such as the multilayer perceptron neural network, J48 decision tree, random forest, K-nearest neighbors, logistic regression, and naïve Bayes. The performance of these machine learning algorithms was evaluated in the context of the classification accuracy achieved to differentiate the affected leg and unaffected leg respective of hemiplegic gait and the time to develop the machine learning model. Wearable and wireless inertial sensor systems have been proposed for the quantification and determination of rehabilitation efficacy for the restoration of gait to more optimal functionality [1, 3, 5,6,7,8,9,10,11]
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