The advent of objective and seamless measurement of parkinsonism will help close the feedback loop between clinical assessment and pharmacological treatment. Furthermore, with the help of data science, biomedical algorithms could even be predictive by recognizing patterns that evade the clinical assessment of doctors. We believe that pharmacotherapy with dopaminergic for patients with Parkinson’s disease is just short of undergoing a similar transformation, like insulin treatment did in previous decades.
In the study with the National Neuroscience Institute Singapore, we developed and refined an objective measure for motor fluctuations in PD patients and target objective measurement of specific PD symptoms. The patients are rated by their doctors using clinical rating scales (UPDRS, MOCA etc.). We use a single wrist-worn device to collect supervised and free-living motion data. Furthermore, we test user experience and gather feedback from patients to develop methods and applications for automated motor state monitoring.
With a sample of 100 patients, we are in the midst of acquiring 3000 minutes of supervised expertly labeled motion data (with additional retrospective video assessment) and more than 100x of unsupervised free-living motion data. The raw data is then processed and used to develop a predictive classification model of PD motor state, and detect specific PD motor symptoms. We have performed previous studies in the field of motion sensing in Parkinson’s Disease which has been achieved in a test-based as well as free-living setup, and serves as profound scientific basis for this research study.
For more information on our clinical studies on Parkinson’s Disease, please see below: