Abstract
Smartwatches provide technology-based assessments in Parkinson’s disease (PD). It is necessary to evaluate their reliability and accuracy in order to include those devices in an assessment. We present unique 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 throughout a 15-min examination. Symptoms and medical history were captured on the paired smartphone. The amplitude error of both smartwatches reaches up to 0.005 g, and for the measured frequencies, up to 0.01 Hz. A broad range of different ML classifiers were cross-validated. 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. Amplitude and frequency differences between smartwatches and the seismometer were under the level of clinical significance. 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.
Highlights
Introduction published maps and institutional affilSmart devices are broadly used in everyday life with many use cases for classification tasks, e.g., human activity recognition via wearable sensors, smart phones or cameras [1,2,3].In addition, there are emerging research applications for different diseases—in particular, movement disorders [4]
Our work focuses on smartwatch-based analyses in diagnostic research of Parkinson’s disease (PD)
Our research focuses on acceleration-based hand movement analyses using a smart device system (SDS) that utilizes two smartwatches and a smartphone to distinguish PD
Summary
The smartwatch validation experiments were carried out during the human subject The trial trial generated generated the the acceleration accelerationand andquestionnaire-based questionnaire-baseddata datain inclinical clinicalexamiexamtrial. Generation inintroduces into human subject trial, which generates for machine the machine learning troduces into thethe human subject trial, which generates datadata for the learning task task of disease classification. Validation details the validation of disease classification. Validation details the validation exexperiment with seismometer. Pipeline and Features describes periment with seismometer. The section Machine Learning Pipeline and Features describes data processing processing steps data steps for for the the disease diseaseclassification classificationtask. Figure provide a deeper insight into the data features and technical machine learning steps. 3 provide a deeper insight into the data features and technical machine learning steps
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