Analysis of User-defined Radar-based Hand Gestures Sensed through Multiple Materials

Radar sensing can penetrate non-conducting materials, such as glass, wood, and plastic, which makes it appropriate for recognizing gestures in environments with poor visibility, limited accessibility, and privacy sensitivityWhile the performance of radar-based gesture recognition in these environmen...

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Bibliographic Details
Published in:IEEE access 2024-01, Vol.12, p.1-1
Main Authors: Sluyters, Arthur, Lambot, Sebastien, Vanderdonckt, Jean, Villarreal-Narvaez, Santiago
Format: Article
Language:English
Subjects:
Doppler radar
Emergency exits
Gesture elicitation study
Gesture recognition
Gesture sensing through materials
Hand gesture recognition
New datasets
One-shot radar calibration
Polyvinyl chloride
Preferences
Radar
Radar detection
Radar measurements
Radar-based gesture recognition
Real-time systems
Sensors
User-defined gestures
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Summary:Radar sensing can penetrate non-conducting materials, such as glass, wood, and plastic, which makes it appropriate for recognizing gestures in environments with poor visibility, limited accessibility, and privacy sensitivityWhile the performance of radar-based gesture recognition in these environments has been extensively researched, the preferences that users express for these gestures are less known. To analyze such gestures simultaneously according to their user preference and their system recognition performance, we conducted three gesture elicitation studies each with n 1 =30 participants to identify user-defined, radar-based gestures sensed through three distinct materials: the glass of a shop window, the wood of an office door, and polyvinyl chloride in an emergency. On this basis, we created a new dataset of nine selected gesture classes for n 2 =20 participants repeating twice the same gesture captured by radar through three materials, i.e ., glass, wood, and polyvinyl chloride. To uniformly compare recognition rates in these conditions with sensing variations, a specifically tailored procedure was defined and conducted with one-shot radar calibration to train and evaluate a gesture recognizer. 'Wood' achieved the best recognition rate (96.44%), followed by 'Polyvinyl chloride' and 'Glass'. We perform a preference-performance analysis of the gestures by combining the agreement rate from the elicitation studies and the recognition rate from the evaluation.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3366667