We propose event-based vibrotactile feedback as an effective approach for enhancing bare-hand XR interaction, delivered through commodity smartwatches that users already wear. While controllers and haptic gloves provide rich tactile feedback, they introduce additional hardware that users must carry and wear throughout the day. Importantly, they interpose hardware between the user and the virtual world, occupying the hands, constraining finger motion, and undermining truly unobstructed bare-hand interaction. Our proposed approach delivers short discrete pulses at key manipulation moments such as object contact and state changes, providing tactile confirmation at the wrist without requiring additional hardware or disrupting finger movement. Two user studies with 26 participants demonstrate that this referred feedback significantly enhances user experience at either wrist placement, with 94% finding it helpful and particular benefits during complex tasks like knob rotation where haptic cues reduced visual attention demands. These findings establish design guidelines for integrating accessible haptics into everyday XR through personal devices, supporting broader adoption of natural hand interaction.

We introduce “Whirling Interface,” a selection method for XR displays using bare-hand motion matching gestures as an input technique. We extend the motion matching input method, by introducing different input states to provide visual feedback and guidance to the users. Using the wrist joint as the primary input modality, our technique reduces user fatigue and improves performance while selecting small and distant targets. In a study with 16 participants, we compared the whirling interface with a standard ray casting method using hand gestures. The results demonstrate that the Whirling Interface consistently achieves high success rates, especially for distant targets, averaging 95.58% with a completion time of 5.58 seconds. Notably, it requires a smaller camera sensing field of view of only 21.45° horizontally and 24.7° vertically. Participants reported lower workloads on distant conditions and expressed a higher preference for the Whirling Interface in general. These findings suggest that the Whirling Interface could be a useful alternative input method for XR displays with a small camera sensing FOV or when interacting with small targets.

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.

Interacting with non-touchscreens such as TV or public displays can be difficult and inefficient. We propose WATouCH, a novel method that localizes a smartwatch on a display and allows direct input by turning the smartwatch into a tangible controller. This low-cost solution leverages sensor fusion of the built-in inertial measurement unit (IMU) and photoplethysmogram (PPG) sensor on a smartwatch that is used for heart rate monitoring. Specifically, WATouCH tracks the smartwatch movement using IMU data and corrects its location error caused by drift using the PPG responses to a dynamic visual pattern on the display. We conducted a user study on two tasks -- a point and click and line tracing task -- to evaluate the system usability and user performance. Evaluation results suggested that our sensor fusion mechanism effectively confined IMU-based localization error, achieved encouraging targeting and tracing precision, was well received by the participants, and thus opens up new opportunities for interaction.

The automatic recognition of how people use their hands and fingers in natural settings -- without instrumenting the fingers -- can be useful for many mobile computing applications. To achieve such an interface, we propose a vision-based 3D hand pose estimation framework using a wrist-worn camera. The main challenge is the oblique angle of the wrist-worn camera, which makes the fingers scarcely visible. To address this, a special network that observes deformations on the back of the hand is required. We introduce DorsalNet, a two-stream convolutional neural network to regress finger joint angles from spatio-temporal features of the dorsal hand region (the movement of bones, muscle, and tendons). This work is the first vision-based real-time 3D hand pose estimator using visual features from the dorsal hand region. Our system achieves a mean joint-angle error of 8.81 degree for user-specific models and 9.77 degree for a general model. Further evaluation shows that our system outperforms previous work with an average of 20% higher accuracy in recognizing dynamic gestures, and achieves a 75% accuracy of detecting 11 different grasp types. We also demonstrate 3 applications which employ our system as a control device, an input device, and a grasped object recognizer.

We propose RotoSwype, a technique for word-gesture typing using the orientation of a ring worn on the index finger. RotoSwype enables one-handed text-input without encumbering the hand with a device, a desirable quality in many scenarios, including virtual or augmented reality. The method is evaluated using two arm positions: with the hand raised up with the palm parallel to the ground; and with the hand resting at the side with the palm facing the body. A five-day study finds both hand positions achieved speeds of at least 14 words-per-minute (WPM) with uncorrected error rates near 1%, outperforming previous comparable techniques.

To better explore the incorporation of pointing and gesturing into ubiquitous computing, we introduce WRIST, an interaction and sensing technique that leverages the dexterity of human wrist motion. WRIST employs a sensor fusion approach which combines inertial measurement unit (IMU) data from a smartwatch and a smart ring. The relative orientation difference of the two devices is measured as the wrist rotation that is independent from arm rotation, which is also position and orientation invariant. Employing our test hardware, we demonstrate that WRIST affords and enables a number of novel yet simplistic interaction techniques, such as (i) macro-micro pointing without explicit mode switching and (ii) wrist gesture recognition when the hand is held in different orientations (e.g., raised or lowered). We report on two studies to evaluate the proposed techniques and we present a set of applications that demonstrate the benefits of WRIST. We conclude with a discussion of the limitations and highlight possible future pathways for research in pointing and gesturing with wearable devices.

We introduce a vision-based technique to recognize static hand poses and dynamic finger tapping gestures. Our approach employs a camera on the wrist, with a view of the opisthenar (back of the hand) area. We envisage such cameras being included in a wrist-worn device such as a smartwatch, fitness tracker or wristband. Indeed, selected off-the-shelf smartwatches now incorporate a built-in camera on the side for photography purposes. However, in this configuration, the fingers are occluded from the view of the camera. The oblique angle and placement of the camera make typical vision-based techniques difficult to adopt. Our alternative approach observes small movements and changes in the shape, tendons, skin and bones on the opisthenar area. We train deep neural networks to recognize both hand poses and dynamic finger tapping gestures. While this is a challenging configuration for sensing, we tested the recognition with a real-time user test and achieved a high recognition rate of 89.4% (static poses) and 67.5% (dynamic gestures). Our results further demonstrate that our approach can generalize across sessions and to new users. Namely, users can remove and replace the wrist-worn device while new users can employ a previously trained system, to a certain degree. We conclude by demonstrating three applications and suggest future avenues of work based on sensing the back of the hand.

Novices can learn sports in a variety of ways ranging from guidance from an instructor to watching video tutorials. In each case, subsequent and repeated self-directed practice sessions are an essential step. However, during such self-directed practice, constant guidance and feedback is absent. As a result, the novices do not know if they are making mistake or if there are any areas for improvement. In this position paper, we propose using wearable devices to augment such self-directed practice sessions by providing augmented guidance and feedback. In particular, a head-worn display can provide real-time guidance whilst wrist-worn devices can provide real-time tracking and monitoring of various states. We envision this approach being applied to various sports, and in particular this is suitable for sports that utilize precise hand motion such as snooker, billiards, golf, archery, cricket, tennis and table tennis.

Developing cross-device multi-user interfaces (UIs) is a challenging problem. There are numerous ways in which content and interactivity can be distributed. However, good solutions must consider multiple users, their roles, their preferences and access rights, as well as device capabilities. Manual and rule-based solutions are tedious to create and do not scale to larger problems nor do they adapt to dynamic changes, such as users leaving or joining an activity. In this paper, we cast the problem of UI distribution as an assignment problem and propose to solve it using combinatorial optimization. We present a mixed integer programming formulation which allows real-time applications in dynamically changing collaborative settings. It optimizes the allocation of UI elements based on device capabilities, user roles, preferences, and access rights. We present a proof-of-concept designer-in-the-loop tool, allowing for quick solution exploration. Finally, we compare our approach to traditional paper prototyping in a lab study.

We present Project Zanzibar: a flexible mat that can locate, uniquely identify and communicate with tangible objects placed on its surface, as well as sense a user's touch and hover hand gestures. We describe the underlying technical contributions: efficient and localised Near Field Communication (NFC) over a large surface area; object tracking combining NFC signal strength and capacitive footprint detection, and manufacturing techniques for a rollable device form-factor that enables portability, while providing a sizable interaction area when unrolled. In addition, we detail design patterns for tangibles of varying complexity and interactive capabilities, including the ability to sense orientation on the mat, harvest power, provide additional input and output, stack, or extend sensing outside the bounds of the mat. Capabilities and interaction modalities are illustrated with self-generated applications. Finally, we report on the experience of professional game developers building novel physical/digital experiences using the platform.

The popularity of mobile devices with large screens is making single-handed interaction difficult. We propose and evaluate a novel design point around a tilt-based text entry technique which supports single handed usage. Our technique is based on the gesture keyboard (shape writing). However, instead of drawing gestures with a finger or stylus, users articulate a gesture by tilting the device. This can be especially useful when the user's other hand is otherwise encumbered or unavailable. We show that novice users achieve an entry rate of 15 words-per-minute (wpm) after minimal practice. A pilot longitudinal study reveals that a single participant achieved an entry rate of 32 wpm after approximate 90 minutes of practice. Our data indicate that tilt-based gesture keyboard entry enables walk-up use and provides a suitable text entry rate for occasional use and can act as a promising alternative to single-handed typing in certain situations.

SpeCam is a lightweight surface color and material sensing approach for mobile devices which only uses the front-facing camera and the display as a multi-spectral light source. We leverage the natural use of mobile devices (placing it face-down) to detect the material underneath and therefore infer the location or placement of the device. SpeCam can then be used to support discreet micro-interactions to avoid the numerous distractions that users daily face with today's mobile devices. Our two-parts study shows that SpeCam can i) recognize colors in the HSB space with 10 degrees apart near the 3 dominant colors and 4 degrees otherwise and ii) 30 types of surface materials with 99% accuracy. These findings are further supported by a spectroscopy study. Finally, we suggest a series of applications based on simple mobile micro-interactions suitable for using the phone when placed face-down.

Today we can see an increasing number of object recognition systems of very different sizes, portability, embedability and form factors which are starting to become part of the ubiquitous, tangible, mobile and wearable computing ecosystems that we might make use of in our daily lives. These systems rely on a variety of technologies including computer vision, radar, acoustic sensing, tagging and smart objects.

Virtual fitting rooms equipped with magic mirrors let people evaluate fashion items without actually putting them on. The mirrors superimpose virtual clothes on the user's reflection. We contribute the Mirror Mirror system, which not only supports mixing and matching of existing fashion items, but also lets users design new items in front of the mirror and export designs to fabric printers. While much of the related work deals with interactive cloth simulation on live user data, we focus on collaborative design activities and explore various ways of designing on the body with a mirror.

The screen size of a smartwatch provides limited space to enable expressive multi-touch input, resulting in a markedly difficult and limited experience. We present WatchMI: Watch Movement Input that enhances touch interaction on a smartwatch to support continuous pressure touch, twist, pan gestures and their combinations. Our novel approach relies on software that analyzes, in real-time, the data from a built-in Inertial Measurement Unit (IMU) in order to determine with great accuracy and different levels of granularity the actions performed by the user, without requiring additional hardware or modification of the watch. We report the results of an evaluation with the system, and demonstrate that the three proposed input interfaces are accurate, noise-resistant, easy to use and can be deployed on a variety of smartwatches. We then showcase the potential of this work with seven different applications including, map navigation, an alarm clock, a music player, pan gesture recognition, text entry, file explorer and controlling remote devices or a game character.

In RadarCat we present a small, versatile radar-based system for material and object classification which enables new forms of everyday proximate interaction with digital devices. We demonstrate that we can train and classify different types of materials and objects which we can then recognize in real time. Based on established research designs, we report on the results of three studies, first with 26 materials (including complex composite objects), next with 16 transparent materials (with different thickness and varying dyes) and finally 10 body parts from 6 participants. Both leave one-out and 10-fold cross-validation demonstrate that our approach of classification of radar signals using random forest classifier is robust and accurate. We further demonstrate four working examples including a physical object dictionary, painting and photo editing application, body shortcuts and automatic refill based on RadarCat. We conclude with a discussion of our results, limitations and outline future directions.