@inproceedings{eska2022designing,
	title = {Designing a Wearable Sensor-Fusion Toolkit for Motor Skill Learning},
	author = {Bettina Eska and Steeven Villa and Sven Mayer and Jasmin Niess},
	year = {2022},
	booktitle = {Proceedings of the 2022 Workshop on Toolkits \& Wearables: Developing Toolkits for Exploring Wearable Designs},
	url = {https://sven-mayer.com/wp-content/uploads/2022/05/eska2022designing.pdf},
	date = {2022-04-30},
	abstract = {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.},
	keywords = {wearable technology, sensor fusion, motor skill learning, motion tracking, real-time feedback, movement data, wearable toolkits}
}