Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Sensor Validation and Diagnostic Potential of Smartwatches in Movement Disorders

Version 1 : Received: 21 March 2021 / Approved: 22 March 2021 / Online: 22 March 2021 (15:37:13 CET)

A peer-reviewed article of this Preprint also exists.

Varghese, J.; Alen, C.M.; Fujarski, M.; Schlake, G.S.; Sucker, J.; Warnecke, T.; Thomas, C. Sensor Validation and Diagnostic Potential of Smartwatches in Movement Disorders. Sensors 2021, 21, 3139. Varghese, J.; Alen, C.M.; Fujarski, M.; Schlake, G.S.; Sucker, J.; Warnecke, T.; Thomas, C. Sensor Validation and Diagnostic Potential of Smartwatches in Movement Disorders. Sensors 2021, 21, 3139.

Journal reference: Sensors 2021, 21, 3139
DOI: 10.3390/s21093139

Abstract

Smartwatches provide technology-based assessments in Parkinson’s disease (PD). We present results for sensor validation and disease classification via Machine Learning (ML). A comparison setup was designed with two different series of Apple smartwatches, one Nanometrics seismometer and a high-precision shaker to measure tremor-like amplitudes and frequencies. Clinical smartwatch measurements were acquired from a prospective study including 450 participants with PD, differential diagnoses (DD) and healthy participants. All participants wore two smartwatches and within a 15-min examination. Symptoms and medical history were captured on the paired smartphone. A broad range of different ML classifiers were cross-validated. Amplitude and frequency differences between smartwatches and the seismometer were under the level of clinical significance. The most advanced task of distinguishing PD vs DD was evaluated with 74,1% balanced accuracy, 86,5% precision and 90,5% recall by Multilayer Perceptrons. Deep Learning architectures significantly underperformed in all classification tasks. Smartwatches are capable of capturing subtle-tremor signs with low noise. This study provided the largest PD sample size of two-hand smartwatch measurements and our preliminary ML-evaluation shows that such a system provides powerful means for diagnosis classification and new digital biomarkers but it remains challenging for distinguishing similar disorders.

Keywords

Smartwatches, Artificial Intelligence, Movement Disorders, Parkinson’s Disease

Subject

MEDICINE & PHARMACOLOGY, Clinical Neurology

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