Implementation of a Smartphone as a Wearable and Wireless Gyroscope Platform for Machine Learning Classification of Hemiplegic Gait Through a Multilayer Perceptron Neural Network

Abstract

The smartphone represents a wearable and wireless system with the potential to have transformative influence on the biomedical and healthcare industry. An intrinsic feature of the smartphone is a gyroscope sensor, for which with a software application the smartphone functions as a wearable and wireless gyroscope platform. The resultant gyroscope data recording presents a clinical recognizable signal, which has been successful demonstrated to quantify aspects of human movement characteristics, such as the patellar tendon reflex. Gait another associated feature of human movement can be readily quantified by a smartphone functioning as a wearable and wireless gyroscope platform. The research objective is to distinguish between an affected leg and unaffected leg during hemiplegic gait based on a smartphone functioning as a wearable and wireless gyroscope platform though machine learning classification. A single smartphone is applied to quantify hemiplegic gait. The smartphone is first mounted to the affected leg and then the unaffected leg with velocity constrained to a constant velocity by a treadmill. Through wireless connectivity to the Internet the gyroscope signal data is conveyed as an email attachment for post-processing at a remote location. Software automation consolidates the gyroscope signal data of hemiplegic gait to a feature set for machine learning classification. With the application of a multilayer perceptron neural network considerable classification accuracy is attained for distinguishing between the affected leg and unaffected leg of hemiplegic gait. Future implications of the successful implementation of a smartphone as a wearable and wireless gyroscope for machine learning classification of hemiplegic gait through a multilayer perceptron neural network elucidate pathways to highly optimized therapy through machine learning with the potential for patients to reside remote from their therapist.