Recent advances in technology present an important opportunity in medicine to augment episodic, expert-based observations of patients' disease signs, obtained in the clinic, with continuous and sensitive measures using wearable and ambient sensors. In Parkinson's disease (PD), such technology-based objective measures have shown exciting potential for passively monitoring disease signs, their fluctuation, and their progression. We are developing a system to passively and continuously capture data from people with PD in their daily lives, and provide a real-time estimate of their motor functions, that is analogous to scores obtained during Part III of the human-administered Movement Disorder Society's Unified Parkinson's Disease assessment (MDS-UPDRS3). Our hypothesis is that complex human movements can be decomposed into movement primitives related to the performance of the MDS-UPDRS3 motor assessment. Toward this hypothesis, we developed a system for integrating and analyzing multiple streams of sensor data collected from volunteers executing the tasks based on the MDS-UPDRS3. In this paper, we show how we can leverage the data collected from MDS-UPDRS3 tasks to develop machine learning models that can identify movement primitives in activities of daily living.