Photoplethysmography (PPG) has become a cornerstone of physiological sensing in wearable devices, enabling non-invasive monitoring of heart rate and related biomarkers. However, its reliability deteriorates sharply under dynamic, high-intensity, or non-periodic motions such as those in sports, where existing datasets fail to capture realistic wrist dynamics. To address this gap, we introduce PPG-Sport, the first large-scale dataset designed for heart rate monitoring from wrist-worn PPG under real sports conditions. The PPG-Sport dataset includes synchronized PPG, inertial measurement unit (IMU), and electrocardiography (ECG) recordings from both wrists of 30 participants across six representative activities: stationary, walking, running, badminton, table tennis, and basketball, amounting to 48 hours of multimodal data. PPG-Sport uniquely captures three critical properties absent in prior datasets: (1) non-periodicity, reflecting irregular and broadband motion patterns; (2) high intensity, with frequent, large-magnitude accelerations; and (3) bilateral asymmetry, caused by distinct functional roles of the dominant and non-dominant hands during sports. We further establish a deep-learning-based benchmark that combines both temporal and spectral representations of PPG and IMU signals to evaluate heart rate estimation performance. Experimental results show that models trained only on conventional periodic activities fail drastically in sports scenarios. Although incorporating sports data mitigates the degradation, significant errors remain. These findings highlight PPG-Sport as an essential and challenging benchmark for developing motion-robust physiological sensing algorithms during sports activities. Dataset and benchmark code are available at: https://github.com/LaserHu/PPG-Sport.
Prolonged desk work has become a major source of musculoskeletal strain, leading to fatigue, discomfort, and reduced mobility among sedentary professionals. Although prior interventions such as standing desks and exergames promote movement, most overlook cumulative postural imbalance and environmental constraints that shape ergonomic risk. We present Adaptive Stretching Pipeline (ASP), a mixed-reality (MR) system that delivers personalized stretching guidance during work breaks. ASP continuously monitors posture through joint-angle and duration analysis and leverages a vision-language model (VLM) to assess workspace supports such as desks or walls. Based on an integrated need-scoring model, the system generates adaptive MR stretching sessions targeting fatigued body regions during microbreak. A three-phase formative study informed the system design, including expert interviews on clinical stretching practices, workplace observations to identify environmental supports, and stretch video analysis to construct a stretching library. A user study with 15 participants compared ASP to a Bend and randomised condition. Our results provide initial evidence that such adaptive mechanisms can shape perceived effort, engagement, and time perception in short-term interactions.
Smart homes are receiving growing interest in global markets. However, current smart home assistant systems either control appliances solely through users' explicit commands or rely on cloud-based large language models (LLMs) for vague command understanding, which brings drawbacks such as high latency, privacy concern, and high cost. In this work, we propose VCU-LLM, the first system to deploy LLMs on edge devices for local vague command understanding and smart device control plan generation. VCU-LLM introduces a novel vague command knowledge retrieval algorithm that refines device-related information in the input prompt, thereby accelerating the LLM's on-device inference and reducing task complexity. We further construct a dataset for LLM fine-tuning to simulate the use of smart home assistants in controlling devices across different households. During inference, a customized KV-cache technique is applied for further inference acceleration. Our evaluations with both human-based and LLM-based scoring demonstrate that VCU-LLM improves the quality of generated control plans by an average of 43.3% compared with SOTA baselines, while reducing time overhead by an average of 8.44x compared with other on-device baselines. We also implement VCU-LLM through a case study in a real home environment, demonstrating its feasibility in real-world application.
End-users of immersive Augmented Reality (AR) applications struggle to discover and execute unfamiliar mid-air gestures. We introduce ARG esture A id , a novel proof-of-concept conversational assistant that provides adaptive, context-aware assistance tailored to users' tasks and gestural errors. By combining users' voice-based task descriptions with formalized representations of hand gesture data together with LLMs, ARG esture A id dynamically supports users through: (1) feedforward , directing users toward correct pose, position, and placement before initiating gestures, and (2) corrective feedback , offering targeted, adaptive instructions to address user errors during discovery and execution. Our initial feasibility study (N=15), which compared users' real-world interaction data against a manually labeled reference dataset, shows that ARG esture A id achieved an overall accuracy of 81.3%. Our follow-up user study (N=15) evaluated ARG esture A id against a BASELINE LLM-based voice assistant across unimanual and bimanual gestures. Results indicate that ARG esture A id achieved higher task completion rates and was rated more favorably for perceived discoverability and usefulness. These findings suggest that adaptive, in-context feedforward and corrective feedback, grounded in formal gesture representations and observed error patterns, may support users during mid-air gesture discovery and execution, and help them refine their understanding of required gestures. We discuss key considerations for designing future adaptive, context-aware AR systems in AR environments.
Honey bees play a vital role in global agriculture. Understanding the movement of a queen bee within a hive box is essential for advancing both biological research and practical apiculture. In this paper, we present SOPAR, a novel Radio Frequency (RF) Sub-harmonic Oscillating Parametric Resonator designed for non-invasive localization and tracking of a queen bee inside a full-size hive box. The core component of SOPAR is a lightweight RF backscatter tag that can be attached to a bee's thorax without affecting its normal behavior. The tag comprises two passive resonators: (i) an inner spiral inductor bridged by a varactor diode and (ii) an outer circular inductor with a gap bridged by a chip capacitor. The outer inductor harvests energy from the external excitation signal, driving the oscillation of the inner spiral resonator to produce sub-harmonic backscattered signals at half the excitation frequency. The frequency separation between excitation and backscatter signals eliminates self-interference at the RF reader, significantly improving signal-to-noise ratio (SNR) and detection range. The layout of the two resonators is meticulously optimized to maximize magnetic coupling, thereby minimizing the overall tag size. Building on this dual-resonator tag, we design an RF reader with a Bayesian estimation algorithm that localizes the tagged bee by exploiting the spatio-temporal characteristics of the sub-harmonic backscattered signals. We have built a prototype of SOPAR, featuring a tag with a diameter of only 3.7 mm and a weight of less than 10 mg. Extensive experiments demonstrate that SOPAR achieves a median localization error of 3.7 cm when tracking a queen bee in a full-size hive box. Moreover, the results confirm that SOPAR remains robust under diverse environmental conditions.
