A generalizable and open-source algorithm for real-life monitoring of tremor in Parkinson's disease

Wearable sensors can objectively and continuously monitor daily-life tremor in Parkinson's Disease (PD). We developed an open-source algorithm for real-life monitoring of PD tremor which achieves generalizable performance across different wrist-worn devices. We achieved this using a unique combination of two independent, complementary datasets. The first was a small, but extensively video-labeled gyroscope dataset collected during unscripted activities at home (n = 24 PD; n = 24 controls). We used this to train and validate a logistic regression tremor detector based on cepstral coefficients. The second was a large, unsupervised dataset (n = 517 PD; n = 50 controls, data collected for 2 weeks with a different device), used to externally validate the algorithm. Results show that our algorithm can reliably quantify real-life PD tremor (sensitivity of 0.61 (0.20) and specificity of 0.97 (0.05)). Weekly aggregated tremor time and power showed excellent test-retest reliability and moderate correlation to MDS-UPDRS rest tremor scores. This opens possibilities to support clinical trials and individual tremor management with wearable technology.