Knowing when a user picks up an object plays a vital role in many context-aware applications. For example, tracking water consumption, counting calories consumed, or reminding you to bring your keys are all context-centered scenarios involving picking up objects. In this project, we propose Contextra, a wrist-worn system that uses sensor fusion to recognize when a user grasps objects. Sensor fusion allows all parts of the grasp to be sensed in ways single channels cannot alone. In our wristband, we fuse EMG and IMU data with video captured from three low-power IR cameras. These cameras maintain privacy by using an active-illumination technique to only capture features close to the sensors. Beyond grasps alone, we see Contextra as playing a foundational role in providing continuous awareness of context triggers to extend the functionality of existing AI devices that cannot run continuously due to power and privacy concerns.

The ability to track a user’s arm pose could be valuable in a wide range of applications, including fitness, rehabilitation, augmented reality input, life logging, and context-aware assistants. Unfortunately, this capability is not readily available to consumers. Systems either require cameras, which carry privacy issues, or utilize multiple worn IMUs or markers. In this work, we describe how an off-the-shelf smartphone and smartwatch can work together to accurately estimate arm pose. Moving beyond prior work, we take advantage of more recent ultra-wideband (UWB) functionality on these devices to capture absolute distance between the two devices. This measurement is the perfect complement to inertial data, which is relative and suffers from drift. We quantify the performance of our software-only approach using off-the-shelf devices, showing it can estimate the wrist and elbow joints with a median positional error of 11.0 cm, without the user having to provide training data.

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.