TITLE:
Implementation of a Smartphone as a Wearable and Wireless Accelerometer and Gyroscope Platform for Ascertaining Deep Brain Stimulation Treatment Efficacy of Parkinson’s Disease through Machine Learning Classification
AUTHORS:
Robert LeMoyne, Timothy Mastroianni, Cyrus McCandless, Christopher Currivan, Donald Whiting, Nestor Tomycz
KEYWORDS:
Parkinson’s Disease, Deep Brain Stimulation, Wearable and Wireless Systems, Smartphone, Machine Learning, Wireless Accelerometer, Wireless Gyroscope, Hand Tremor
JOURNAL NAME:
Advances in Parkinson's Disease,
Vol.7 No.2,
May
31,
2018
ABSTRACT: Parkinson’s disease manifests in movement disorder symptoms, such as hand
tremor. There exists an assortment of therapy interventions. In particular
deep brain stimulation offers considerable efficacy for the treatment of Parkinson’s
disease. However, a considerable challenge is the convergence toward
an optimal configuration of tuning parameters. Quantified feedback from a
wearable and wireless system consisting of an accelerometer and gyroscope
can be enabled through a novel software application on a smartphone. The
smartphone with its internal accelerometer and gyroscope can record the
quantified attributes of Parkinson’s disease and tremor through mounting the
smartphone about the dorsum of the hand. The recorded data can be then
wirelessly transmitted as an email attachment to an Internet derived resource
for subsequent post-processing. The inertial sensor data can be consolidated
into a feature set for machine learning classification. A multilayer perceptron
neural network has been successfully applied to attain considerable classification
accuracy between deep brain stimulation “On” and “Off” scenarios for a
subject with Parkinson’s disease. The findings establish the foundation for the
broad objective of applying wearable and wireless systems for the development of closed-loop optimization of deep brain stimulation parameters in the
context of cloud computing with machine learning classification.