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

TL;DR

This paper introduces a modular system that synthesizes skeletal motion and physiological signals based on actions and emotions, enabling scalable, privacy-preserving data generation for healthcare ML applications.

Contribution

It presents the first computational models for synchronized synthesis of skeletal motion and physiological signals driven by actions and emotions in a virtual environment.

Findings

Models generate high-fidelity skeletal and physiological data

System is validated through user studies and benchmark datasets

Framework enables scalable, privacy-preserving data simulation

Abstract

Round-the-clock monitoring of human behavior and emotions is required in many healthcare applications which is very expensive but can be automated using machine learning (ML) and sensor technologies. Unfortunately, the lack of infrastructure for collection and sharing of such data is a bottleneck for ML research applied to healthcare. Our goal is to circumvent this bottleneck by simulating a human body in virtual environment. This will allow generation of potentially infinite amounts of shareable data from an individual as a function of his actions, interactions and emotions in a care facility or at home, with no risk of confidentiality breach or privacy invasion. In this paper, we develop for the first time a system consisting of computational models for synchronously synthesizing skeletal motion, electrocardiogram, blood pressure, respiration, and skin conductance signals as a function of an open-ended set of actions and emotions. Our experimental evaluations, involving user studies, benchmark datasets and comparison to findings in the literature, show that our models can generate skeletal motion and physiological signals with high fidelity. The proposed framework is modular and allows the flexibility to experiment with different models. In addition to facilitating ML research for round-the-clock monitoring at a reduced cost, the proposed framework will allow reusability of code and data, and may be used as a training tool for ML practitioners and healthcare professionals.