Hands are the chief appendage with which we manipulate the world around us, creating sounds as they go. As such, they are a rich source of information that computers can leverage for input and context sensing. Indeed, many prior works in HCI have explored this idea by instrumenting users’ hands with a microphone, often integrated into a ring, wristband, or watch. In this work, we explore an alternative bare-hands approach — by using a microphone array integrated into a user’s headset/glasses, we can use beamforming to create a virtual microphone that tracks with the user’s fingers in 3D space. We show this method can capture even the subtle noise of a finger translating across surfaces, including skin-to-skin contact for micro-gestures, as well as passive widget interactions.

Powerful computing devices are now small enough to be easily worn on the body. However, batteries pose a major design and user experience obstacle, adding weight and volume, and generally requiring periodic device removal and recharging. In response, we developed Power-over-Skin, an approach using the human body itself to deliver power to many distributed, battery-free, worn devices. We demonstrate power delivery from on-body distances as far as from head-to-toe, with sufficient energy to power microcontrollers capable of sensing and wireless communication. We share results from a study campaign that informed our implementation, as well as experiments that validate our final system. We conclude with several demonstration devices, ranging from input controllers to longitudinal bio-sensors, which highlight the efficacy and potential of our approach.

An omni-directional (360°) camera captures the entire viewing sphere surrounding its optical center. Such cameras are growing in use to create highly immersive content and viewing experiences. When such a camera is held by a user, the view includes the user’s hand grip, finger, body pose, face, and the surrounding environment, providing a complete understanding of the visual world and context around it. This capability opens up numerous possibilities for rich mobile input sensing. In OmniSense, we explore the broad input design space for mobile devices with a built-in omni-directional camera and broadly categorize them into three sensing pillars: i) near device ii) around device and iii) surrounding device. In addition we explore potential use cases and applications that leverage these sensing capabilities to solve user needs. Following this, we develop a working system to put these concepts into action, by leveraging these sensing capabilities to enable potential use cases and applications. We studied the system in a technical evaluation and a preliminary user study to gain initial feedback and insights. Collectively these techniques illustrate how a single, omni-purpose sensor on a mobile device affords many compelling ways to enable expressive input, while also affording a broad range of novel applications that improve user experience during mobile interaction.

Methods for faithfully capturing a user’s holistic pose have immediate uses in AR/VR, ranging from multimodal input to expressive avatars. Although body-tracking has received the most attention, the mouth is also of particular importance, given that it is the channel for both speech and facial expression. In this work, we describe a new RF-based approach for capturing mouth pose using an antenna integrated into the underside of a VR/AR headset. Our approach side-steps privacy issues inherent in camera-based methods, while simultaneously supporting silent facial expressions that audio-based methods cannot. Further, compared to bio-sensing methods such as EMG and EIT, our method requires no contact with the wearer’s body and can be fully self-contained in the headset, offering a high degree of physical robustness and user practicality. We detail our implementation along with results from two user studies, which show a mean 3D error of 2.6 mm for 11 mouth keypoints across worn sessions without re-calibration.

Pointing with one's finger is a natural and rapid way to denote an area or object of interest. It is routinely used in human-human interaction to increase both the speed and accuracy of communication, but it is rarely utilized in human-computer interactions. In this work, we use the recent inclusion of wide-angle, rear-facing smartphone cameras, along with hardware-accelerated machine learning, to enable real-time, infrastructure-free, finger-pointing interactions on today's mobile phones. We envision users raising their hands to point in front of their phones as a "wake gesture". This can then be coupled with a voice command to trigger advanced functionality. For example, while composing an email, a user can point at a document on a table and say "attach". Our interaction technique requires no navigation away from the current app and is both faster and more privacy-preserving than the current method of taking a photo.

Touchscreen tracking latency, often 80ms or more, creates a rubber-banding effect in everyday direct manipulation tasks such as dragging, scrolling, and drawing. This has been shown to decrease system preference, user performance, and overall realism of these interfaces. In this research, we demonstrate how the addition of a thin, 2D micro-patterned surface with 5 micron spaced features can be used to reduce motor-visual touchscreen latency. When a finger, stylus, or tangible is translated across this textured surface frictional forces induce acoustic vibrations which naturally encode sliding velocity. This acoustic signal is sampled at 192kHz using a conventional audio interface pipeline with an average latency of 28ms. When fused with conventional low-speed, but high-spatial-accuracy 2D touch position data, our machine learning model can make accurate predictions of real time touch location.

EtherPose is a continuous hand pose tracking system employing two wrist-worn antennas, from which we measure the real-time dielectric loading resulting from different hand geometries (i.e., poses). Unlike worn camera-based methods, our RF approach is more robust to occlusion from clothing and avoids capturing potentially sensitive imagery. Through a series of simulations and empirical studies, we designed a proof-of-concept, worn implementation built around compact vector network analyzers. Sensor data is then interpreted by a machine learning backend, which outputs a fully-posed 3D hand. In a user study, we show how our system can track hand pose with a mean Euclidean joint error of 11.6 mm, even when covered in fabric. We also studied 2DOF wrist angle and micro-gesture tracking. In the future, our approach could be miniaturized and extended to include more and different types of antennas, operating at different self resonances.

Handheld controllers are an essential part of VR systems. Modern sensing techniques enable them to track users’ finger movements to support natural interaction using hands. The sensing techniques, however, often fail to precisely determine whether two fingertips touch each other, which is important for the robust detection of a pinch gesture. To address this problem, we propose AtaTouch, which is a novel, robust sensing technique for detecting the closure of a finger pinch. It utilizes a change in the coupled impedance of an antenna and human fingers when the thumb and finger form a loop. We implemented a prototype controller in which AtaTouch detects the finger pinch of the grabbing hand. A user test with the prototype showed a finger-touch detection accuracy of 96.4%. Another user test with the scenarios of moving virtual blocks demonstrated low object-drop rate (2.75%) and false-pinch rate (4.40%). The results and feedback from the participants support the robustness and sensitivity of AtaTouch.

Completing tasks on smartwatches often requires multiple gestures due to the small size of the touchscreens and the lack of sufficient number of touch controls that are easily accessible with a finger. We propose to increase the number of functions that can be triggered with the touch gesture by enabling a smartwatch to identify which finger is being used. We developed MagTouch, a method that uses a magnetometer embedded in an off-the-shelf smartwatch. It measures the magnetic field of a magnet fixed to a ring worn on the middle finger. By combining the measured magnetic field and the touch location on the screen, MagTouch recognizes which finger is being used. The tests demonstrated that MagTouch can differentiate among the three fingers used to make contacts at a success rate of 95.03%.

The space around the body not only expands the interaction space of a mobile device beyond its small screen, but also enables users to utilize their kinesthetic sense. Therefore, body-centric peephole interaction has gained considerable attention. To support its practical implementation, we propose OddEyeCam, which is a vision-based method that tracks the 3D location of a mobile device in an absolute, wide, and continuous manner with respect to the body of a user in both static and mobile environments. OddEyeCam tracks the body of a user using a wide-view RGB camera and obtains precise depth information using a narrow-view depth camera from a smartphone close to the body. We quantitatively evaluated OddEyeCam through an accuracy test and two user studies. The accuracy test showed the average tracking accuracy of OddEyeCam was 4.17 and 4.47cm in 3D space when a participant is standing and walking, respectively. In the frst user study, we implemented various interaction scenarios and observed that OddEyeCam was well received by the participants. In the second user study, we observed that the peephole target acquisition task performed using our system followed Fitts? law. We also analyzed the performance of OddEyeCam using the obtained measurements and observed that the participants completed the tasks with suffcient speed and accuracy.