Gesture-enabled remote control for healthcare

Abstract : In recent years, wearable sensor-based gesture recognition is proliferating in the field of healthcare. It could be used to enable remote control of medical devices, contactless navigation of X-ray display and Magnetic Resonance Imaging (MRI), and largely enhance patients' daily living capabilities. However, even though a few commercial or prototype devices are available for wearable gesture recognition, none of them provides a combination of (1) fully open API for various healthcare application development, (2) appropriate form factor for comfortable daily wear, and (3) affordable cost for large scale adoption. In addition, the existing gesture recognition algorithms are mainly designed for discrete gestures. Accurate recognition of continuous gestures is still a significant challenge, which prevents the wide usage of existing wearable gesture recognition technology. In this paper, we present Gemote, a smart wristband-based hardware/software platform for gesture recognition and remote control. Due to its affordability, small size, and comfortable profile, Gemote is an attractive option for mass consumption. Gemote provides full open API access for third party research and application development. In addition, it employs a novel continuous gesture segmentation and recognition algorithm, which accurately and automatically separates hand movements into segments, and merges adjacent segments if needed, so that each gesture only exists in one segment. Experiments with human subjects show that the recognition accuracy is 99.4% when users perform gestures discretely, and 94.6% when users perform gestures continuously
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https://hal.archives-ouvertes.fr/hal-01884849
Contributor : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Submitted on : Monday, October 1, 2018 - 2:18:08 PM
Last modification on : Sunday, October 20, 2019 - 9:48:01 AM

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Hongyang Zhao, Shuangquan Wang, Gang Zhou, Daqing Zhang. Gesture-enabled remote control for healthcare. CHASE 2017: 2nd IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, Jul 2017, Philadelphia, United States. pp.392 - 401, ⟨10.1109/CHASE.2017.123⟩. ⟨hal-01884849⟩

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