Conformal Wearable and Wireless Inertial Sensor System for Machine Learning Classification of Hemiplegic Reduced Arm Swing

Abstract

The development of wearable and wireless inertial sensor systems enables a highly robust and convenient means for quantifying gait. In particular, the quantification hemiplegic gait can objectively identify disparity, such as with respect to reduced arm swing. Recent developments have produced the BioStamp nPoint, which is a conformal wearable and wireless inertial sensor system. The BioStamp nPoint is conveniently mounted by adhesive to an aspect of the human anatomy, such as the dorsum of the hand, for quantifying reduced arm swing for hemiplegic gait. The acquired inertial sensor data quantifying the characteristics of reduced arm swing for hemiplegic gait is wirelessly transmitted to a secure Cloud computing environment for subsequent post-processing. The pertinent gyroscope signal data is consolidated into a feature set suitable for machine learning classification. Using a multilayer perceptron neural network considerable classification accuracy is attained to distinguish between a hemiplegic affected arm with reduced arm swing and the unaffected arm. These accomplishments imply a pathway for ameliorating reduced arm swing through the application of conformal wearable and wireless inertial sensor systems, such as the BioStamp nPoint.