With robots increasingly integrating into human environments, understanding and predicting human motion is essential for safe and efficient interactions. Modern human motion and activity prediction approaches require high quality and quantity of data for training and evaluation, usually collected from motion capture systems, onboard or stationary sensors. Setting up these systems is challenging due to the intricate setup of hardware components, extensive calibration procedures, occlusions, and substantial costs. These constraints make deploying such systems in new and large environments difficult and limit their usability for in-the-wild measurements. In this paper we investigate the possibility to apply the novel Ultra-Wideband (UWB) localization technology as a scalable alternative for human motion capture in crowded and occlusion-prone environments. We include additional sensing modalities such as eye-tracking, onboard robot LiDAR and radar sensors, and record motion capture data as ground truth for evaluation and comparison. The environment imitates a museum setup, with up to four active participants navigating toward random goals in a natural way, and offers more than 130 minutes of multi-modal data. Our investigation provides a step toward scalable and accurate motion data collection beyond vision-based systems, laying a foundation for evaluating sensing modalities like UWB in larger and complex environments like warehouses, airports, or convention centers.

If you have teenagers at home, it's likely you often feel like you're speaking to a brick wall. Now, a study has confirmed that teenagers really do ignore you, spending less time looking at your face when you're speaking to them than another adult would. A team led by the University of Kent recorded three groups of volunteers, aged 10-19, 20-40 and 60-80 in real-world social interaction situations. The situations involved them having a face-to-face conversation and navigating an environment, with eye-tracking glasses used to monitor their interactions. The findings revealed that adolescents pay less attention to social cues in real-world interactions than adults.

It is good news for those people who find making eye contact with strangers uncomfortable. It is not necessary to gaze into someone's eyes while speaking to them, as looking at another part of their face will do just as well. A study using eye-tracking glasses found people can hardly tell the difference when someone is looking at their eyes or their mouth. When 46 people were spoken to by a researcher, they thought he was making eye contact even when he was looking at their lips. Although the person they were talking to barely met their eyes, they enjoyed the conversation just as much.

Despite the ubiquity of drones nowadays, it seems to be generally accepted that learning how to control them properly is just too much work. Consumer drones are increasingly being stuffed full of obstacle-avoidance systems, based on the (likely accurate) assumption that most human pilots are to some degree incompetent. It's not that humans are entirely to blame, because controlling a drone isn't the most intuitive thing in the world, and to make it easier, roboticists have been coming up with all kinds of creative solutions. There's body control, face control, and even brain control, all of which offer various combinations of convenience and capability. The more capability you want in a drone control system, usually the less convenient it is, in that it requires more processing power or infrastructure or brain probes or whatever. Developing a system that's both easy to use and self-contained is quite a challenge, but roboticists from the University of Pennsylvania, U.S. Army Research Laboratory, and New York University are up to it--with just a pair of lightweight gaze-tracking glasses and a small computing unit, a small drone will fly wherever you look.