Uploaded October 2022 | Updated September 2026, 3 hours ago
Published at ACM UIST 2022
Real-time tracking of a user’s hands, arms and environment is valuable in a wide variety of HCI applications, from context awareness to virtual reality. Rather than rely on fixed and external tracking infrastructure, the most flexible and consumer-friendly approaches are mobile, self-contained, and compatible with popular device form factors (e.g., smartwatches). In this vein, we contribute DiscoBand, a thin sensing strap not exceeding 1 cm in thickness. Sensors operating so close to the skin inherently face issues with occlusion. To help overcome this, our strap uses eight distributed depth sensors imaging the hand from different viewpoints, creating a sparse 3D point cloud. An additional eight depth sensors image outwards from the band to track the user’s body and surroundings. In addition to evaluating arm and hand pose tracking, we also describe a series of supplemental applications powered by our band’s data, including held object recognition and environment mapping.
AUTHORS
Nathan Devrio (Carnegie Mellon University)
Chris Harrison (Carnegie Mellon University)
Published at ACM UIST 2022
Real-time tracking of a user’s hands, arms and environment is valuable in a wide variety of HCI applications, from context awareness to virtual reality. Rather than rely on fixed and external tracking infrastructure, the most flexible and consumer-friendly approaches are mobile, self-contained, and compatible with popular device form factors (e.g., smartwatches). In this vein, we contribute DiscoBand, a thin sensing strap not exceeding 1 cm in thickness. Sensors operating so close to the skin inherently face issues with occlusion. To help overcome this, our strap uses eight distributed depth sensors imaging the hand from different viewpoints, creating a sparse 3D point cloud. An additional eight depth sensors image outwards from the band to track the user’s body and surroundings. In addition to evaluating arm and hand pose tracking, we also describe a series of supplemental applications powered by our band’s data, including held object recognition and environment mapping.
AUTHORS
Nathan Devrio (Carnegie Mellon University)
Chris Harrison (Carnegie Mellon University)
![TouchTools [CMU/Qeexo]](https://i.ytimg.com/vi/N8s8NJf34fM/mqdefault.jpg "TouchTools [CMU/Qeexo]
Gestures on todays touch devices are simplistic, solely relying on the number of fingers (1-finger pan, 2-finger zoom, 3-finger swipe, etc). We propose instead that gesture design be inspired by the manipulation of real world artifacts specifically tools. The result is a set of rich gestures for touch interaction that leverages user familiarity and fluency with real world objects.")
