Decoding Intent With Control Theory: Comparing Muscle Versus Manual Interface Performance

要旨

Manual device interaction requires precise coordination which may be difficult for users with motor impairments. Muscle interfaces provide alternative interaction methods that may enhance performance, but have not yet been evaluated for simple (eg. mouse tracking) and complex (eg. driving) continuous tasks. Control theory enables us to probe continuous task performance by separating user input into intent and error correction to quantify how motor impairments impact device interaction. We compared the effectiveness of a manual versus a muscle interface for eleven users without and three users with motor impairments performing continuous tasks. Both user groups preferred and performed better with the muscle versus the manual interface for the complex continuous task. These results suggest muscle interfaces and algorithms that can detect and augment user intent may be especially useful for future design of interfaces for continuous tasks.

キーワード
User intent
control theory
interaction
muscle interfaces
electromyography
motor impairments
accessibility
著者
Momona Yamagami
University of Washington, Seattle, WA, USA
Katherine M. Steele
University of Washington, Seattle, WA, USA
Samuel A. Burden
University of Washington, Seattle, WA, USA
DOI

10.1145/3313831.3376224

論文URL

https://doi.org/10.1145/3313831.3376224

動画

会議: CHI 2020

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)

セッション: Accessible input & learning

Paper session
316B MAUI
5 件の発表
2020-04-29 20:00:00
2020-04-29 21:15:00
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