1. Customized Mid-Air Gestures for Accessibility: A $B Recognizer for Multi-Dimensional Biosignal Gestures
- Author
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Yamagami, Momona, Mitchell, Claire L., Portnova-Fahreeva, Alexandra A., Kong, Junhan, Mankoff, Jennifer, and Wobbrock, Jacob O.
- Subjects
Computer Science - Human-Computer Interaction - Abstract
Biosignal interfaces, using sensors in, on, or around the body, promise to enhance wearables interaction and improve device accessibility for people with motor disabilities. However, biosignals are multi-modal, multi-dimensional, and noisy, requiring domain expertise to design input features for gesture classifiers. The \$B-recognizer enables mid-air gesture recognition without needing expertise in biosignals or algorithms. \$B resamples, normalizes, and performs dimensionality reduction to reduce noise and enhance signals relevant to the recognition. We tested \$B on a dataset of 26 participants with and 8 participants without upper-body motor disabilities performing personalized ability-based gestures. For two conditions (user-dependent, gesture articulation variability), \$B outperformed our comparison algorithms (traditional machine learning with expert features and deep learning), with > 95% recognition rate. For the user-independent condition, \$B and deep learning performed comparably for participants with disabilities. Our biosignal dataset is publicly available online. $B highlights the potential and feasibility of accessible biosignal interfaces., Comment: 20 pages, 7 figures, 1 table
- Published
- 2024