On-Device Machine Learning for Diagnosis of Parkinson's Disease from Hand Drawn Artifacts

Abstract

Effective diagnosis of neuro-degenerative diseases is critical in providing early treatments, which in turn can lead to substantial savings in medical costs. Machine learning models can help with the diagnosis of such diseases like Parkinson’s and aid in assessing disease symptoms. This work introduces a novel system that integrates pervasive computing, mobile sensing, and machine learning to classify hand-drawn images and provide diagnostic insights for screening of Parkinson’s disease patients. We design a computational framework that combines data augmentation techniques with optimized convolutional neural network design for on-device and real-time image classification. We assess the performance of the proposed system using two datasets of images of Archimedean spirals drawn by hand and demonstrate that our approach achieves 76% and 83% accuracy respectively. Thanks to 4x memory reduction via integer quantization, our system can run fast on an Android smartphone. Our study demonstrates that pervasive computing may offer an inexpensive and effective tool for early diagnosis of Parkinson’s disease.

Asiful Arefeen

Graduate Research Assistant

I am a PhD student at Arizona State University (ASU). I am working under the supervision of Professor Hassan Ghasemzadeh at the Embedded Machine Intelligence Lab (EMIL). My research topics include machine learning, health monitoring system development and mobile health. I received my B.S. in Electrical and Electronic Engineering from Bangladesh University of Engineering & Technology (BUET) in 2019.

Hassan Ghasemzadeh

Director

Hassan Ghasemzadeh is an Associate Professor of Biomedical Informatics at Arizona State University (ASU) and a Computer Science Adjunct Faculty at Washington State University (WSU).