Computational tracking of Parkinsonian motor fluctuations in a real-world setting: a case study

Digital biomarkers based on accurate tracking of motor behaviour can provide a cost-effective, objective, and robust measure for Parkinson’s Disease progression, changes in care needs, and the effect of interventions. Markerless motion capture technology offers a promising approach for running it in the home. This technology uses depth sensors to capture movement unobtrusively and generate objective and quantifiable movement features. Here we present a 4-month long case study during which the patient visits our lab every month to perform mobility tasks and daily living tasks. Our data suggest accurate tracking of symptom fluctuations during both task types. This is a promising proof-of-concept towards passive tracking in-the-home of Parkinsonian symptom fluctuations