Given the significant amount of time that people spend indoors, public spaces are reinventing themselves to deliver immersive experiences. One approach is to provide interactive gaming, such as the spaces created in big international airports like Singapore. The problem is that these gaming areas rely on either (i) wearable devices, which can easily spread germs, particularly in highly transited areas, such as those in airports, or (ii) cameras, which are increasingly raising privacy concerns and are being forbidden in some public areas. To provide an immersive gaming experience that is device-free and privacy-aware, we propose a system based on mmWave radar. In particular, our work provides three contributions. (1) For the first time, we compare various tracking and human pose estimation (HPE) models in the SoA under a unified framework. This approach allows us to identify the best methods in terms of position accuracy, latency, and smoothness-which are critical gaming metrics. (2) We build an integrated system with tracking and HPE capabilities. Our system includes the design of four games with different levels of complexity and a dataset collected specifically to train models for gaming applications. (3) Our evaluation, which includes a subjective users' survey and an objective comparison with a Kinect console, shows that today's mmWave is suitable for games where only the user's location is required or when coarse-grained gestures are needed. There are two key areas where mmWave research needs to improve to match camera-based consoles: sensors need a higher sampling rate, since a high frame rate is critical for seamless gaming, and HPE models need to master fine-grained gestures, in particular when arms end up in front of the body. All the code and datasets will be made available as a stepping stone for future work.

This paper proposes ElastiCast, a novel Bluetooth Low Energy (BLE) broadcast mode that reduces the neighbor discovery latency in offline finding networks (OFNs). ElastiCast adapts the broadcast mode of the lost devices to the scan modes of the finder devices, considering their diversity. We start with an overview of OFNs, followed by a detailed analysis of the issues and challenges of existing solutions, which motivates the design of ElastiCast. Then we provide Blender, a simulator that models the neighbor discovery behavior of different broadcasters and scanners. By adopting Blender, ElastiCast can be implemented with three components: Local Optima Estimation, Common Interest Extraction, and Interval Multiplexing, in which we capture the key features of BLE neighbor discovery and globally optimize the broadcast mode interacting with diverse scan modes. Experimental evaluation results and commercial product deployment experience demonstrate that ElastiCast is effective in achieving stable and bounded neighbor discovery latency within the power budget.