Assessment of Machine Learning Classification Strategies for the Differentiation of Deep Brain Stimulation “On” and “Off” Status for Parkinson’s Disease Using a Smartphone as a Wearable and Wireless Inertial Sensor for Quantified Feedback

Author:

LeMoyne Robert,Mastroianni Timothy,Whiting Donald,Tomycz Nestor

Publisher

Springer Nature Singapore

Reference20 articles.

1. R. LeMoyne, N. Tomycz, T. Mastroianni, C. McCandless, M. Cozza, D. Peduto, Implementation of a smartphone wireless accelerometer platform for establishing deep brain stimulation treatment efficacy of essential tremor with machine learning, in 37th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS) (2015), pp. 6772–6775

2. R. LeMoyne, T. Mastroianni, N. Tomycz, D. Whiting, M. Oh, C. McCandless, C. Currivan, D. Peduto, Implementation of a multilayer perceptron neural network for classifying deep brain stimulation in ‘On’ and ‘Off’ modes through a smartphone representing a wearable and wireless sensor application, in 47th Society for Neuroscience Annual Meeting (featured in Hot Topics; top 1% of abstracts) (2017)

3. R. LeMoyne, T. Mastroianni, C. McCandless, C. Currivan, D. Whiting, N. Tomycz, 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. Adv. Park. Dis. 7(2), 19–30 (2018)

4. R. LeMoyne, C. Coroian, T. Mastroianni, P. Opalinski, M. Cozza, W. Grundfest, The merits of artificial proprioception, with applications in biofeedback gait rehabilitation concepts and movement disorder characterization, in Biomedical Engineering. (InTech, Vienna, 2009), pp. 165–198

5. R. LeMoyne, T. Mastroianni, Smartphone and portable media device: a novel pathway toward the diagnostic characterization of human movement, in Smartphones from an Applied Research Perspective. (InTech, Rijeka, Croatia, 2017), pp. 1–24

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