As Extended Reality (XR) advances, a device has the potential to be used across contexts from immersive productivity at a desk to on-the-go, public scenarios. Existing input solutions lack the versatility to provide both high-throughput, mouse-grade input and subtle, ergonomic interaction. We introduce FlowRing, a novel ring-form device that combines microgestures with precise 2D mouse-like input on surfaces. FlowRing supports five microgestures for discreet interaction and 2D input for richer tasks, using an optical flow sensor, skin-contact microphone, and IMU at the base of the finger. In a study with 11 participants, FlowRing achieved 93.6% microgesture recognition accuracy across sessions and 85.2% across unseen users, rising to 90.1% with just four gesture set examples from a new user. A separate 2D Fitts’ law study demonstrated its effectiveness for continuous input on various surfaces. FlowRing emerges as a versatile, user-friendly solution for the future of interactive technology.

All-day smart glasses are likely to emerge as platforms capable of continuous contextual sensing, uniquely positioning them for unprecedented assistance in our daily lives. Integrating the multi-modal AI agents required for human memory enhancement while performing continuous sensing, however, presents a major energy efficiency challenge for all-day usage. Achieving this balance requires intelligent, context-aware sensor management. Our approach, EgoTrigger, leverages audio cues from the microphone to selectively activate power-intensive cameras, enabling efficient sensing while preserving substantial utility for human memory enhancement. EgoTrigger uses a lightweight audio model (YAMNet) and a custom classification head to trigger image capture from hand-object interaction (HOI) audio cues, such as the sound of a drawer opening or a medication bottle being opened. In addition to evaluating on the QA-Ego4D dataset, we introduce and evaluate on the Human Memory Enhancement Question-Answer (HME-QA) dataset. Our dataset contains 340 human-annotated first-person QA pairs from full-length Ego4D videos that were curated to ensure that they contained audio, focusing on HOI moments critical for contextual understanding and memory. Our results show EgoTrigger can use 54% fewer frames on average, significantly saving energy in both power-hungry sensing components (e.g., cameras) and downstream operations (e.g., wireless transmission), while achieving comparable performance on datasets for an episodic memory task. We believe this context-aware triggering strategy represents a promising direction for enabling energy-efficient, functional smart glasses capable of all-day use - supporting applications like helping users recall where they placed their keys or information about their routine activities (e.g., taking medications).

We introduce a low-power communication method that lets smartwatches leverage existing electrocardiogram (ECG) hardware as a data communication interface. Our unique approach enables the connection of external, inexpensive, and low-power "add-on" sensors to the smartwatch, expanding its functionalities. These sensors cater to specialized user needs beyond those offered by pre-built sensor suites, at a fraction of the cost and power of traditional communication protocols, including Bluetooth Low Energy. To demonstrate the feasibility of our approach, we conduct a series of exploratory and evaluative tests to characterize the ECG interface as a communication channel on commercial smartwatches. We design a simple transmission scheme using commodity components, demonstrating cost and power benefits. Further, we build and test a suite of add-on sensors, including UV light, body temperature, buttons, and breath alcohol, all of which achieved testing objectives at low material cost and power usage. This research paves the way for personalized and user-centric wearables by offering a cost-effective solution to expand their functionalities.

We present Z-Ring, a wearable ring that enables gesture input, object detection, user identification, and interaction with passive user interface (UI) elements using a single sensing modality and a single point of instrumentation on the finger. Z-Ring uses active electrical field sensing to detect changes in the hand’s electrical impedance caused by finger motions or contact with external surfaces. We develop a diverse set of interactions and evaluate them with 21 users. We demonstrate: (1) Single- and two-handed gesture recognition with up to 93% accuracy (2) Tangible input with a set of passive touch UI elements, including buttons, a continuous 1D slider, and a continuous 2D trackpad with 91.8% accuracy, <4.4 cm MAE, and <4.1cm MAE, respectively (3) Object recognition across six household objects with 94.5% accuracy (4) User identification among 14 users with 99% accuracy. Z-Ring’s sensing methodology uses only a single co-located electrode pair for both receiving and sensing, lending itself well to future miniaturization for use in on-the-go scenarios.

Face orientation can often indicate users’ intended interaction target. In this paper, we propose FaceOri, a novel face tracking technique based on acoustic ranging using earphones. FaceOri can leverage the speaker on a commodity device to emit an ultrasonic chirp, which is picked up by the set of microphones on the user’s earphone, and then processed to calculate the distance from each microphone to the device. These measurements are used to derive the user’s face orientation and distance with respect to the device. We conduct a ground truth comparison and user study to evaluate FaceOri’s performance. The results show that the system can determine whether the user orients to the device at a 93.5% accuracy within a 1.5 meters range. Furthermore, FaceOri can continuously track user’s head orientation with a median absolute error of 10.9 mm in the distance, 3.7° in yaw, and 5.8° in pitch. FaceOri can allow for convenient hands-free control of devices and produce more intelligent context-aware interactions.

We present ClearBuds, the first hardware and software system that utilizes a neural network to enhance speech streamed from two wireless earbuds. Real-time speech enhancement for wireless earbuds requires high-quality sound separation and background cancellation, operating in real-time and on a mobile phone. Clear-Buds bridges state-of-the-art deep learning for blind audio source separation and in-ear mobile systems by making two key technical contributions: 1) a new wireless earbud design capable of operating as a synchronized, binaural microphone array, and 2) a lightweight dual-channel speech enhancement neural network that runs on a mobile device. Our neural network has a novel cascaded architecture that combines a time-domain conventional neural network with a spectrogram-based frequency masking neural network to reduce the artifacts in the audio output. Results show that our wireless earbuds achieve a synchronization error less than 64 μs and our network has a runtime of 21.4 ms on an accompanying mobile phone. In-the-wild evaluation with eight users in previously unseen indoor and outdoor multipath scenarios demonstrates that our neural network generalizes to learn both spatial and acoustic cues to perform noise suppression and background speech removal. In a user-study with 37 participants who spent over 15.4 hours rating 1041 audio samples collected in-the-wild, our system achieves improved mean opinion score and background noise suppression.

We present ClearBuds, the first end-to-end hardware and software system that utilizes a neural network to enhance speech streamed from two wireless earbuds. Real-time speech enhancement for wireless earbuds requires high-quality sound separation and background cancellation, operating in real-time and on a mobile phone. Clear-Buds bridges state-of-the-art deep learning for blind audio source separation and in-ear mobile systems by making two key technical contributions: 1) a new wireless earbud design capable of operating as a synchronized, binaural microphone array, and 2) a lightweight dual-channel speech enhancement neural network that runs on a mobile device. Our demo will allow MobiSys attendees wear our earbuds, and experience noise suppression as they talk in a noisy environment. Companion video can be accessed using the link below:

Debugging printed circuit boards (PCBs) can be a time-consuming process, requiring frequent context switching between PCB design files (schematic and layout) and the physical PCB. To assist electrical engineers in debugging PCBs, we present ARDW, an augmented reality workbench consisting of a monitor interface featuring PCB design files, a projector-augmented workspace for PCBs, tracked test probes for selection and measurement, and a connected test instrument. The system supports common debugging workflows for augmented visualization on the physical PCB as well as augmented interaction with the tracked probes. We quantitatively and qualitatively evaluate the system with 10 electrical engineers from industry and academia, finding that ARDW speeds up board navigation and provides engineers with greater confidence in debugging. We discuss practical design considerations and paths for improvement to future systems. A video demo of the system may be accessed here: https://youtu.be/RbENbf5WIfc .

Debugging printed circuit boards (PCBs) requires frequent context switching and spatial pattern matching between software design files and physical boards. To reduce this overhead, we conduct a series of interviews with electrical engineers to understand their workflows, around which we design a set of AR interaction techniques, we call Augmented Silkscreen, to streamline identification, localization, annotation, and measurement tasks. We then run a set of remote user studies with illustrative video sketches and simulated PCB tasks to compare our interactions with current practices, finding that our techniques reduce completion times. Based on these quantitative results, as well as qualitative feedback from our participants, we offer design recommendations for the implementation of these interactions on a future, deployable AR system.

QWERTY is the primary smartphone text input keyboard configuration. However, insertion and substitution errors caused by hand tremors, often experienced by users with Parkinson’s disease, can severely affect typing efficiency and user experience. In this paper, we investigated Parkinson’s users’ typing behavior on smartphones. In particular, we identified and compared the typing characteristics generated by users with and without Parkinson’s symptoms. We then proposed an elastic probabilistic model for input prediction. By incorporating both spatial and temporal features, this model generalized the classical statistical decoding algorithm to correct insertion, substitution and omission errors, while maintaining direct physical interpretation. User study results confirmed that the proposed algorithm outperformed baseline techniques: users reached 22.8 WPM typing speed with a significantly lower error rate and higher user-perceived performance and preference. We concluded that our method could effectively improve the text entry experience on smartphones for users with Parkinson’s disease.

Humans rely on eye gaze and hand manipulations extensively in their everyday activities. Most often, users gaze at an object to perceive it and then use their hands to manipulate it. We propose applying a multimodal, gaze plus free-space gesture approach to enable rapid, precise and expressive touch-free interactions. We show the input methods are highly complementary, mitigating issues of imprecision and limited expressivity in gaze-alone systems, and issues of targeting speed in gesture-alone systems. We extend an existing interaction taxonomy that naturally divides the gaze+gesture interaction space, which we then populate with a series of example interaction techniques to illustrate the character and utility of each method. We contextualize these interaction techniques in three example scenarios. In our user study, we pit our approach against five contemporary approaches; results show that gaze+gesture can outperform systems using gaze or gesture alone, and in general, approach the performance of "gold standard" input systems, such as the mouse and trackpad.