Bluetooth audio faces a major challenge due to its limited transmission range in meeting users' demands. This work introduces WiLE-Audio, a novel approach that extends Low Energy Audio (LE Audio) coverage through cross-technology communication (CTC) from WiFi to BLE. We first present a novel symbol mapping technique to enable all-channel and reliable CTC. Then, we present a real-time reverse scrambling method to implement CTC to commodity WiFi devices. Finally, we design a precise timing strategy and a priority scheduling to align with the strict time window requirements of the BLE receiver. These systematic innovations allow WiLE-Audio to be easily implemented into existing commercial devices with only a simple software upgrade on the WiFi side. Furthermore, we achieve seamless switching between Bluetooth classic audio and WiLE-Audio, thus supporting whole-house audio roaming. We implement our work on commercial WiFi and BLE devices and demonstrate that WiLE-Audio extends the transmission distance of LE Audio more than 2x.
Daily eating habits shape our long-term health, but most diet apps focus only on calories or macronutrients and overlook deeper issues like chronic inflammation and its effects on cellular aging. Prior medical literature has demonstrated a significant inverse relationship between Dietary Inflammatory Index (DII) and telomere length (TL), a key marker of cellular age. Inspired by this finding, we design ChronoBite, a closed-loop feedback system that pairs real-time inflammation scores with periodic cellular aging insights. In addition to regular calorie tracking, it uses fast-changing DII signals and slow-moving cellular aging markers to guide users toward age-aware eating habits. ChronoBite is a mobile-based prototype that combines food recognition, DII analysis, and cellular aging insights to deliver age-aware dietary feedback. Powered by large language models (LLMs), it offers real-time recommendations while supporting long-term tracking of inflammation patterns and telomere dynamics.
Conventional pulmonary function tests (PFTs) are important but costly. Hence, prior research has proposed IoT sensor-based solutions to facilitate cost-efficient, at-home PFT. However, these solutions require the subject to perform maximal exhalations, a task often challenging without supervision, compromising test accuracy. In response to this challenge, this study introduces EasySpiro that, for the first time, uses non-maximal exhalations to measure PFT indicators. This is challenging since PFT indicators are only defined for maximal exhalations, and there are no guidelines to derive them from submaximal exhalations. To address that, we observe that pulmonary deficiencies affect all types of breathing, where the underlying pulmonary deficiency should be the same under different breathing efforts. Leveraging this insight, we design a reconstruction model to predict the ideal maximal breathing patterns based on submaximal ones and utilize these reconstructions for PFT. Furthermore, since the body dynamics reflect the exhalation effort, we use self-supervised learning techniques to encode body dynamics into breathing effort representations to guide the reconstruction process. We integrate these designs into earphones with microphones to measure breathing patterns and IMUs to measure body dynamics. We collaborate with a hospital and develop a dataset from 50 patients with various diseases to evaluate EasySpiro's performance, which shows an accurate prediction of PFT indicators based on non-maximal exhalations with an error rate of 7%. In addition, we open-source the collected dataset to encourage future research.
As the adoption of LiDAR expands across various fields such as autonomous driving, robotics, and smart cities, the demand for adaptive scanning capabilities to better capture dynamic and complex scenes becomes paramount. Current LiDAR technologies, limited by fixed uniform scan patterns, struggle to prioritize critical areas, resulting in reduced perception accuracy and performance inefficiencies. This paper introduces SmartLiDAR, an advanced LiDAR system that enhances scanning efficiency and performance by adaptively optimizing scan focus through an intelligent, software-defined micro-mirror controller. Unlike traditional systems, SmartLiDAR dynamically adjusts its scan pattern based on environmental characteristics and application-specific requirements, concentrating sample points on key objects without increasing power consumption or scan time. SmartLiDAR achieves this by integrating a novel quadratic micro-mirror controller, an adaptive algorithm for generating fine-grained attention map with prioritized scan focus, and a carefully designed optimization algorithm that maps attention maps to practical scanning patterns. We prototype SmartLiDAR by building a software-defined LiDAR using commercially available optical components and FPGA. Our experimental results demonstrate that SmartLiDAR significantly enhances resolution in regions of interest by 3x and increases average object detection precision by up to 16.11%. Additionally, SmartLiDAR maintains negligible extra energy consumption and processing latency, making it suitable for real-time applications, such as autonomous vehicles.
In-home resistance training (RT) is a convenient and effective way to maintain health andwell-being. However, incorrect exercise execution can result in unintended muscle engagement and an increased risk of injury. Without access to professional coaching, an accurate muscle-aware motion feedback system becomes essential for safe and effective training. However, existing visual language models (VLMs) struggle to provide accurate and effective muscle-aware movement guidance due to their limited understanding of RT motion and the absence of related expert knowledge. In this work, we introduce Myo-Trainer, the first vision-based muscle-aware motion feedback system that uses explicit muscle-aware motion analysis and domain-specific expert knowledge to provide corrective guidance on muscle engagement and movement execution. Also, we propose a novel DAGCN-Former network that integrates both spatial and temporal modeling capabilities to capture the complex dynamics of human RT motion. Experiments involving 26 subjects and 1000+ minutes of RT demonstrate that Myo-Trainer improves the accuracy of motion analysis by 17.22%, achieves a 2.5x reduced inference latency and a BertScore of 85.88% of generated feedback compared to those provided by experienced certified trainers, outperforming existing solutions. Additionally, Myo-Trainer received higher satisfaction ratings from participants compared to other AI trainers and video tutorials, highlighting its potential for real-world applications.
