Short-Timescale Assessment of Motor Symptomatology in Parkinson’s Disease and Essential TremorParkinson’s disease (PD) affects over 4 million people worldwide, making it the second-most prevalent neurodegenerative disease behind Alzheimer’s disease. Cardinal motor symptoms of PD include resting tremor, bradykinesia, rigidity, and postural or gait-related instability. Current therapies, including dopamine replacement and deep brain stimulation of the subthalamic nucleus (STN) or internal globus pallidus (GPi), aim to alleviate these hallmark motor symptoms: as such, clinical evaluation focuses on assessing their severity. Currently, clinicians use the Unified Parkinson’s Disease Rating Scale (UPDRS) to quantify symptomatology in PD patients. While this scale has been validated, its subjectivity, susceptibility to inter-rater variability, and its inability to capture short-timescale fluctuations in symptomatology make it an imperfect tool. PD researchers, clinicians, and patients have begun to harness the rise of ubiquitous mobile computing to assess symptomatology. The mPower app, which allowed patients to complete tasks designed to evaluate cognition, gait, vocal tremor, and speed/dexterity, became available on Apple’s app store in 2015. Within the first year, over 1,000 participants with a confirmed diagnosis of PD enrolled in the study, indicating the promise of such an approach. Here, we use a novel, iPad-based task to assess motor symptomatology over seven different calculated domains in three-second epochs. Using a support vector machine (SVM) learning algorithm, we can generate 7-dimensional hyperplane classifiers for PD and ET patients that can accurately differentiate patients from control subjects, and PD patients from ET patients.Sanderson, JohnAuthorYu, JamesAuthorLiu, DavidAuthorAkbar, UmerAuthorD'Abreu, AnelyssaAuthorLee, ShaneAuthorAsaad, WaelAuthorBrown University. Alpert Medical School. Scholarly Concentration Program. Non-Scholarly Concentratorresearch programNeurosciencesParkinson's diseaseMedical technologyEnglish2018Non-Scholarly ConcentratorAll rights reservedtext