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Round-the-clock monitoring of human behavior and emotions is required in manyhealthcare applications which is very expensive but can be automated usingmachine learning (ML) and sensor technologies. Unfortunately, the lack ofinfrastructure for collection and sharing of such data is a bottleneck for MLresearch applied to healthcare. Our goal is to circumvent this bottleneck bysimulating a human body in virtual environment. This will allow generation ofpotentially infinite amounts of shareable data from an individual as a functionof his actions, interactions and emotions in a care facility or at home, withno risk of confidentiality breach or privacy invasion. In this paper, wedevelop for the first time a system consisting of computational models forsynchronously synthesizing skeletal motion, electrocardiogram, blood pressure,respiration, and skin conductance signals as a function of an open-ended set ofactions and emotions. Our experimental evaluations, involving user studies,benchmark datasets and comparison to findings in the literature, show that ourmodels can generate skeletal motion and physiological signals with highfidelity. The proposed framework is modular and allows the flexibility toexperiment with different models. In addition to facilitating ML research forround-the-clock monitoring at a reduced cost, the proposed framework will allowreusability of code and data, and may be used as a training tool for MLpractitioners and healthcare professionals.

Synthesizing Skeletal Motion and Physiological Signals as a Function of a Virtual Human's Actions and Emotions