Exploring Radar Capabilities to Support Gesture-Based Interaction in Smart Environments

The environments we live in are becoming increasingly smarter, creating the need of finding suitable ways to interact with them. While interaction modes such as speech, text, and touch remain prevalent, there are associated challenges related to background noise interference, accessibility issues, and reliance on devices (e.g., mobile, wearable). Gesture-based interaction emerges as a promising alternative or complement to those modes. In this context, radars represent a good option for enabling gesture recognition, since they rely on radio waves to detect moving targets, not presenting the disadvantages of other sensors such as cameras and wearables, which can be considered too intrusive by the users. This contribution intends to investigate the capabilities of radars for gesture-based interaction in smart environments, focusing on the possibility of using data provided by a radar and transfer learning to recognize a set of pre-defined arm gestures regardless of the distance between the user and radar. The results obtained based on data collected from a subject standing at four different distances (1, 2, 3, and 4 m) from the radar, showed a poor performance when considering a distance-independent solution. For a distancedependent solution, relatively good results were achieved when considering all four distances together (mean accuracy of 96%). These results are very useful for future developments of gesturebased interaction with smart environments in real scenarios.