We describe two approaches for unobtrusively sensing subtle nonverbal behaviors using a consumer-level depth sensing camera. The first signal, respiratory rate, is estimated by measuring the visual expansion and contraction of the user's chest cavity during inhalation and exhalation. Additionally, we detect a specific type of fidgeting behavior, known as "leg jiggling," by measuring high-frequency vertical oscillations of the user's knees. Both of these techniques rely on the combination of skeletal tracking information with raw depth readings from the sensor to identify the cyclical patterns in jittery, low-resolution data. Such subtle nonverbal signals may be useful for informing models of users' psychological states during communication with virtual human agents, thereby improving interactions that address important societal challenges in domains including education, training, and medicine.