Recent advances in mobile health have demonstrated great potential to leverage sensor-based technologies for quantitative, remote monitoring of health and disease – particularly for diseases affecting motor function such as Parkinson’s disease. Such approaches have been rolled out using research-grade wearable sensors and, increasingly, through the use of smartphones and consumer wearables, such as smart watches and fitness trackers. These devices not only provide the ability to measure much more detailed disease phenotypes but also provide the ability to follow patients longitudinally with much higher frequency than is possible through clinical exams. However, the conversion of sensor-based data streams into digital biomarkers is complex and no methodological standards have yet evolved to guide this process.
Parkinson’s disease (PD) is a neurodegenerative disease that primarily affects the motor system but also exhibits other symptoms. Typical motor symptoms of the disease include tremors, slowness (bradykinesia), posture and walking perturbations, muscle rigidity and speech perturbations. Additionally, patients may experience side effects from their medication in the form of dyskinesia (involuntary movement). In the clinic, symptoms and side-effects are evaluated using physician observation and patient reports, however this infrequent assessment may not reflect a patient’s typical state on an average day. Mobile sensor (accelerometers) may be useful for tracking a patient’s symptom severity and response to medication, and have the potential to provide more detailed information that patients and physicians can use to make care decisions.
While much work in this field has been done to predict symptom severity using sensor feeds during the completion of specific tasks, very little work has focused on information available from sensors worn during the course of daily living. The Biomarker and Endpoint Assessment to Track Parkinson’s Disease (BEAT-PD) Challenge is a first of it’s kind challenge, designed to benchmark methods for the processing of unstructured (free-living) sensor data, in order to be predictive of Parkinson’s Disease severity. Participants will be provided with raw sensor (accelerometer and gyroscope) time series data recorded during the course of daily living, and will be asked to predict individuals’ medication state and symptom severity.