Tracking human motion is important for a range of activities and applications, from dance and music performance to rehabilitation and human-robot interaction.

Wearable devices using physiological sensor data capture precise information about muscle activity, but provide very little information about how a person feels during the activity.

This project combines a real-time interactive audiovisual system and machine learning techniques to develop algorithms that impart additional high-level information about the mover’s emotional and affective states.

The goal is to improve algorithms for movement and effort tracking by incorporating people’s felt experience of movement.

Participants

The Faculty of Social Sciences and Humanities (SSH) the Technical Faculty of IT and Design (TECH) and the Faculty of Engineering and Science (ENG)

Elizabeth Jochum: RELATE
Cumhur Erkut, Dan Overholt, Sofia Dahl, George Palamas:  Augmented Performance Lab
Shaoping Bai: Department of Materials and Production