Customized Mid-Air Gestures for Accessibility: A $B Recognizer for Multi-Dimensional Biosignal Gestures
<|reference_start|>Customized Mid-Air Gestures for Accessibility: A $B Recognizer for Multi-Dimensional Biosignal Gestures: 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.<|reference_end|>
@article{yamagami2024customized,
    title={Customized Mid-Air Gestures for Accessibility: A $B Recognizer for
  Multi-Dimensional Biosignal Gestures},
    author={Momona Yamagami, Claire L. Mitchell, Alexandra A. Portnova-Fahreeva,
  Junhan Kong, Jennifer Mankoff, Jacob O. Wobbrock},
    journal={arXiv preprint arXiv:2409.08402},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08402},
    primaryClass={cs.HC}
}