Implementation of Machine Learning Classification Regarding Hemiplegic Gait Using an Assortment of Machine Learning Algorithms with Quantification from Conformal Wearable and Wireless Inertial Sensor System ()
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.
Share and Cite:
LeMoyne, R. and Mastroianni, T. (2021) Implementation of Machine Learning Classification Regarding Hemiplegic Gait Using an Assortment of Machine Learning Algorithms with Quantification from Conformal Wearable and Wireless Inertial Sensor System.
Journal of Biomedical Science and Engineering,
14, 415-425. doi:
10.4236/jbise.2021.1412035.