Stroke is among the leading causes of disability, with 795,000 individuals experiencing a new or recurrent stroke each year. Upper extremity sensorimotor deficits, including diminished grip strength, are the most common long-term deficits among stroke survivors. Diminished hand function is a significant challenge for stroke survivors and health professionals. Most technology-driven approaches to address rehabilitation rely on intrusive and sensory device-based systems to analyze and assist hand rehabilitation. We strive to develop a non-intrusive system for hand rehabilitation using camera-based virtual rehabilitation. Taking steps towards this goal, in this thesis, we develop the baseline standard for hand function by analyzing the hand function of healthy adults using camera-based data over five daily life activities. We conclude by defining the gold standard representation of an individual’s hand function based on computer vision, specialized Jerk metrics, statistical analysis, and unsupervised learning.