Machine Learning Techniques for Automated Tremor Detection in the Presence of External Stressors

Author:

Vanitha K. M.1,Talasila Viswanath2

Affiliation:

1. Department of Electronics and Instrumentation Engineering, Ramaiah Institute of Technology, Vidya Soudha, MSR Nagar, MSRIT Post, Bangalore – 560054, India

2. Department of Telecommunication Engineering, Ramaiah Institute of Technology, Vidya Soudha, MSR Nagar, MSRIT Post, Bangalore – 560054, India

Abstract

In this study tremor data of 25 subjects (Senile tremor = 5, Alcohol induced tremor = 9, Healthy individuals = 11) were collected using a wearable device consisting of five Inertial Measuring Units (IMUs) and an embedded optical sensor. The subjects were made to draw the Archimedes spiral under the influence of external stressors. Features were extracted from measured acceleration data and also from an optical sensor. Using the selected features few supervised machined learning algorithms were explored for automatic classification of tremor. Performance matrix used to evaluate the classifier was accuracy, recall, and precision. It is observed that the algorithms are able to accurately classify healthy, senile tremor and alcohol induced tremor.

Publisher

North Atlantic University Union (NAUN)

Subject

Electrical and Electronic Engineering,Signal Processing

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