| Abstrakt: |
We examine radar-based gesture input for interactive computer systems, a technology that has recently grown in terms of commercial availability, affordability, and popularity among researchers and practitioners, where radar sensors are leveraged to detect user input performed in mid-air, on the body, and around physical objects and digital devices. We analyze forty-five academic papers published on this topic between 2010 and 2021, and report results regarding gesture recognition techniques, application types, and evaluation approaches for radar-based gesture input. Our findings reveal that (1) deep learning techniques, such as Convolutional Neural Networks, have been the most popular approach for radar-based gesture recognition, (2) application opportunities for implementing radar gestures have been diverse, but without any clear contender for a game changer in this area, and (3) the gesture sets employed in prior work have been small with a median of just six gesture types. Based on these findings, we draw ten implications for integrating radar-based gesture sensing in ambient intelligence environments. [ABSTRACT FROM AUTHOR] |
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