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Bettina Eska, Steeven Villa, Sven Mayer, Jasmin Niess
Designing a Wearable Sensor-Fusion Toolkit for Motor Skill Learning
Proceedings of the 2022 Workshop on Toolkits & Wearables: Developing Toolkits for Exploring Wearable Designs, 2022-04-30 (bib)

User movement data is essential for providing feedback in the area of motor-skill learning. For instance, when learning a new sport such as dancing, people can benefit from meaningful technology-based feedback. However, movement tracking equipment for real-time feedback is costly and challenging to implement. In contrast, wearable devices tracking users' movements are accessible and lightweight. While their lower cost makes them available to a broader audience, several open issues include sensor placement, sensor count, and data synchronization. To address these issues, we propose a wearable sensor-fusion approach for motor skill learning that allows researchers and developers to use one or multiple body-worn sensors for motion tracking. The extracted motion can then be used to deliver real-time feedback on the user's performance, supporting positive learning experiences.