A Perspective on Wearable Sensor Measurements and Data Science for Parkinson’s Disease - Matias, Ricardo; Paixão, Vitor; Bouça, Raquel; Ferreira, Joaquim J. (Frontiers in Neurology, 2017)
Miniaturised and wearable sensor-based measurements enable the assessment of PD motor-related features like never before and hold great promise as non-invasive biomarkers for early and accurate diagnosis, and monitoring the progression of PD. High fidelity human movement reconstruction and simulation can already be conducted in a clinical setting with increasingly precise and affordable motion technology enabling access to high quality labelled data on patients’ subcomponents of movement (kinematics and kinetics). At the same time, body-worn sensors now allow us to extend some quantitative movement-related measurements to patients’ daily living activities. This era of patient movement “cognification” is bringing us previously inaccessible variables that encode patients’ movement, and that, together with measures from clinical examinations, poses new challenges in data analysis. We present herein examples of the application of an unsupervised methodology to classify movement behaviour in healthy individuals and patients with PD where no specific knowledge on the type of behaviours recorded is needed. We are most certainly leaving the early stage of the exponential curve that describes the current technological evolution and soon will be entering its steep ascent. But there is already a benefit to be derived from current motion technology and sophisticated data science methods to objectively measure parkinsonian impairments.