Mindfulness meditation is a widely accessible and evidence-based method for supporting mental health. Despite the proliferation of mindfulness meditation apps, sustaining user engagement remains a persistent challenge. Personalizing the meditation experience is a promising strategy to improve engagement, but it often requires costly and unscalable manual effort. We present MindfulAgents, a multi-agent system powered by large language models that: (1) generates guided meditation scripts based on an expert-established mindfulness framework, (2) encourages users’ reflection on emotional states and mindfulness skills, and (3) enables real-time personalization of the mindfulness meditation experience for each user. In a formative lab study (N=13), MindfulAgents significantly improved in-session engagement (p = 0.011) and self-awareness (p = 0.014), as well as reduced momentary stress (p = 0.020). Furthermore, a four-week deployment study (N=62) demonstrated a notable increase (p = 0.002) in long-term engagement and level of mindfulness (p = 0.023). Participants reported that MindfulAgents offered more relevant meditation sessions personalized to individual needs in various contexts, supporting sustained practice. Our findings highlight the potential of LLM-driven personalization for enhancing user engagement in digital mindfulness meditation interventions.

ECG is an essential diagnostic tool that offers important insight into a person's cardiac and general health. The rise of intelligent wearable devices has opened a new avenue for clinicians and individuals to capture long term ECG data—albeit with fewer leads than the 12 leads that are typically used clinically, which can be vital for identifying and addressing health concerns. In this work, a multi-task convolutional neural network (CNN) classifier was used to study the influence of various combinations of ECG leads in interpretation of 71 cardiac statements spanning cardiac diagnostics, form, and rhythm. Results of this analysis suggest that the subset of limb leads I and II and chest leads V1, V3, and V6 can be used to identify several cardiac statements without loss of performance (average macro AUC of 0.903) when compared to a model trained using all 12- leads (average macro AUC of 0.905; p = 1). A hybrid CNNLSTM (long short-term memory) model was developed to reconstruct the missing chest leads. The highest performing lead reconstructor achieved an average R2 score of 0.835 when reconstructing three chest leads. This architecture was proposed as the foundation for a wearable system that could record a limited number of ECG leads while also providing a 12-lead ECG for clinical applications.

Wearable devices have begun to incorporate machine learning models to assist with detection of various cardiac conditions. In this work, we developed a multi-task convolutional neural network to simultaneously predict$\mathbf{7 5}$diagnostic, form and rhythm statements from 10-s duration, 12-lead ECGs. The model, originally developed off-line in TensorFlow, was converted to the FlatBuffers format for on-edge AI using the LiteRT toolset. Posttraining quantization was used to compare different numerical precisions in terms of model size, model performance and inference time. Classifier performance for the 12-lead configuration was consistent between the 32-bit floating point model (“float32” baseline), the dynamic range quantized model (DR) and the float16 model$(p=0.92)$with an average macro AUC score of 0.893 with all output statements considered. A large degradation in classification performance was observed for 8-bit integer quantization (int8) which yielded an average macro AUC score of 0.513 for the 12-lead configuration across all statements. To address class imbalance, minority classes were removed. Reducing the number of statements to$\mathbf{4 1}$classes increased macro F1 score by an average of 72.6% (to a mean value about 0.358) for the float32, float16 and DR quantized models.

Multimodal question answering (QA) often requires identifying which video, audio, or sensor tokens are relevant to the question. Yet modality disagreements are common: off-camera speech, background noise, or motion outside the field of view often mislead fusion models that weight all streams equally. We present RAVEN, a unified QA architecture whose core is QuART, a query-conditioned cross-modal gating module that assigns scalar relevance scores to each token across modalities, enabling the model to amplify informative signals and suppress distractors before fusion. RAVEN is trained through a three-stage pipeline comprising unimodal pretraining, query-aligned fusion, and disagreement-oriented fine-tuning - each stage targeting a distinct challenge in multi-modal reasoning: representation quality, cross-modal relevance, and robustness to modality mismatch. To support training and evaluation, we release AVS-QA, a dataset of 300K synchronized Audio-Video-Sensor streams paired with automatically generated question-answer pairs. Experimental results on seven multi-modal QA benchmarks - including egocentric and exocentric tasks - show that RAVEN achieves up to 14.5% and 8.0% gains in accuracy compared to state-of-the-art multi-modal large language models, respectively. Incorporating sensor data provides an additional 16.4% boost, and the model remains robust under modality corruption, outperforming SOTA baselines by 50.23%. Our code and dataset are available at https://github.com/BASHLab/RAVEN.

Recent advancements in Ultra-Wideband (UWB) radar technology have enabled contactless, non-line-of-sight vital sign monitoring, making it a valuable tool for healthcare. However, UWB radar's ability to capture sensitive physiological data, even through walls, raises significant privacy concerns, particularly in human-robot interactions and autonomous systems that rely on radar for sensing human presence and physiological functions. In this paper, we present Anti-Sensing, a novel defense mechanism designed to prevent unauthorized radarbased sensing. Our approach introduces physically realizable perturbations, such as oscillatory motion from wearable devices, to disrupt radar sensing by mimicking natural cardiac motion, thereby misleading heart rate (HR) estimations. We develop a gradient-based algorithm to optimize the frequency and spatial amplitude of these oscillations for maximal disruption while ensuring physiological plausibility. Through both simulations and real-world experiments with radar data and neural networkbased HR sensing models, we demonstrate the effectiveness of Anti-Sensing in significantly degrading model accuracy, offering a practical solution for privacy preservation.

Spoken Language Understanding (SLU) systems must balance performance and efficiency, particularly in resource-constrained environments. Existing methods apply distillation and quantization separately, leading to suboptimal compression as distillation ignores quantization constraints. We propose QUADS, a unified framework that optimizes both through multi-stage training with a pre-tuned model, enhancing adaptability to low-bit regimes while maintaining accuracy. QUADS achieves 71.13\% accuracy on SLURP and 99.20\% on FSC, with only minor degradations of up to 5.56\% compared to state-of-the-art models. Additionally, it reduces computational complexity by 60--73$\times$ (GMACs) and model size by 83--700$\times$, demonstrating strong robustness under extreme quantization. These results establish QUADS as a highly efficient solution for real-world, resource-constrained SLU applications.

Continuous health monitoring is crucial to ensuring better health and taking preventive measures just-in-time. Existing battery-powered health wearables pose a significant limitation to continuous monitoring as batteries wear out after fixed energy cycles and need replacement. Ambient energy harvesting unlocks battery-free sensing but it suffers from spatio-temporal variability, making it unfit for health sensing. Intra-body power transfer (IBPT) provides an alternative energy source for battery-free operation, however, it can only provide limited energy in order to ensure wearer's safety. Existing system support is designed to maximize computational progress in a single energy cycle, thus wasting energy on computations that become stale in the next energy cycle. We instantiate an IBPT-powered health wearable capable of supporting multiple health sensors. To cope with lower incoming energy, we introduce BodyOS; a system support that exposes programming constructs for domain experts to express health applications in terms of the inherent dependencies of bio-signals being monitored by the application. By avoiding unnecessary sensing operations, BodyOS allows energy-efficient application execution and faster capacitor recharge while ensuring that the data sensed by the application is always useful. We evaluate BodyOS to show that it significantly improves energy efficiency, thus increasing the on-time and number of data points collected by the device.

Ultrasound imaging of the forearm has demon-strated significant potential for accurate hand gesture classification. Despite this progress, there has been limited focus on developing a stand -alone end-to-end gesture recognition system which makes it mobile, real-time and more user friendly. To bridge this gap, this paper explores the deployment of deep neural networks for forearm ultrasound-based hand gesture recognition on edge devices. Utilizing quantization techniques, we achieve substantial reductions in model size while maintaining high accuracy and low latency. Our best model, with Float16 quantization, achieves a test accuracy of 92% and an inference time of 0.31 seconds on a Raspberry Pi. These results demonstrate the feasibility of efficient, real-time gesture recognition on resource-limited edge devices, paving the way for wearable ultrasound-based systems.

Early identification of neuro-developmental risks in infants is crucial for timely intervention and improved quality of life. Current screening methods are costly, intrusive, and limited by artificial environments or require the infant to wear multiple sensors. To address these challenges, we propose a novel approach leveraging inertial measurement units (IMUs) to monitor infants' spontaneous motor abilities in natural settings. Our method introduces a hierarchical semi-supervised classifier and the InfantMotion2Vec embedding to capture detailed motion patterns, accommodating a wide age range (up to 36 months) while minimizing reliance on labeled data and cumbersome sensor setups. We collected labeled IMU data from 25 families and unlabeled data from 42 families using a single wearable sensor. Pretraining an embedding network using unlabeled data with a hierarchical pose estimator resulted in a 26% increase in F1-score and a 77.7% increase in Cohen's Kappa score compared to using only labeled data. The InfantMotion2Vec embedding adequately handles highly unbalanced labeled data, demonstrating its effectiveness in infant posture classification.

Most existing speech disfluency detection techniques only rely upon acoustic data.In this work, we present a practical multimodal disfluency detection approach that leverages available video data together with audio.We curate an audiovisual dataset and propose a novel fusion technique with unified weight-sharing modality-agnostic encoders to learn the temporal and semantic context.Our resilient design accommodates real-world scenarios where the video modality may sometimes be missing during inference.We also present alternative fusion strategies when both modalities are assured to be complete.In experiments across five disfluency-detection tasks, our unified multimodal approach significantly outperforms Audio-only unimodal methods, yielding an average absolute improvement of 10% (i.e., 10 percentage point increase) when both video and audio modalities are always available, and 7% even when video modality is missing in half of the samples.

Task-based intermittent software systems always re-execute peripheral input/output (I/O) operations upon power failures since tasks have all-or-nothing semantics. Re-executed I/O wastes significant time and energy and risks memory inconsistency. This paper presents EaseIO, a new task-based intermittent system that remedies these problems. EaseIO programming interface introduces re-execution semantics for I/O operations to facilitate safe and efficient I/O management for intermittent applications. EaseIO compiler front-end considers the programmer-annotated I/O re-execution semantics to preserve the task's energy efficiency and idem-potency. EaseIO runtime introduces regional privatization to eliminate memory inconsistency caused by idempotence bugs. Our evaluation shows that EaseIO reduces the wasted useful I/O work by up to 3× and total execution time by up to 44% by avoiding 76% of the redundant I/O operations, as compared to the state-of-the-art approaches for intermittent computing. Moreover, for the first time, EaseIO ensures memory consistency during DMA-based I/O operations.

Speech disfluency research is pivotal to accommodating atypical speakers in mainstream conversational technology. However, the lack of publicly available labeled and unlabeled datasets is a significant bottleneck to such research. While many works use pseudo dysfluency data with proxy labels and formulate a self-supervised task, we see merit in using real-world data. In this work, we consolidate the corpora of publicly available speech disfluency datasets with and without labels and propose DisfluentSiam – an efficient siamese network-based small-scale pretraining pipeline using task-specific data from multiple domains with only 10M trainable parameters. We show that with DisfluentSiam, we achieve an average of 15% boost in performance across five types of dysfluency event detection compared to direct wav2vec 2.0 embeddings. In particular, with only 4-5 mins of labeled data for fine-tuning, the DisfluentSiam demonstrates the advantage of task-specific pretraining with up to 25% higher accuracy.

The growing use of computing and proliferation of computing devices requires a holistic focus on sustainability as the environmental impacts of computing technologies go beyond their energy consumption.Environmental impacts span all stages of the lifecycle -manufacturing, operation, and disposal.A sustainability mindset must permeate all organizations involved in design, manufacturing, operation, and disposal/recycling of computational devices.This panel will discuss current and new research on sustainability in computing that spans the full lifecycle, all layers of the computing stack and across the computing spectrum from edge to cloud.The panel will also discuss potential crossdisciplinary approaches to sustainable computing and new notions and metrics to quantify sustainability.

The growing use of computing and proliferation of computing devices requires a holistic focus on sustainability as the environmental impacts of computing technologies go beyond their energy consumption. Environmental impacts span all stages of the lifecycle - manufacturing, operation, and disposal. A sustainability mindset must permeate all organizations involved in design, manufacturing, operation, and disposal/recycling of computational devices. This panel will discuss current and new research on sustainability in computing that spans the full lifecycle, all layers of the computing stack and across the computing spectrum from edge to cloud. The panel will also discuss potential cross-disciplinary approaches to sustainable computing and new notions and metrics to quantify sustainability.

In this paper, we first present the results of an empirical study of the comparative statistics of fourteen interference features in licensed and unlicensed bands in a selected route in downtown Worcester, MA. Then, we benefit from these features to train a machine learning algorithm to predict the availability of the channels for intelligent spectrum access for a vehicle. The main component of the vehicular interference monitoring system is an ultra wideband programmable 26GHz Agilent E4407 spectrum analyzer, interfaced with a GPS device to record the location of measurements, and a laptop to store the results in a centralized database. With this measurement system loaded in a car we drive in a selected path to monitor the interference in 1.9-2.5 GHz frequency band, which includes high traffic density neighboring licensed and unlicensed bands. With the empirical monitored interference, we study the statistical behavior of fourteen interference features logically categorized into four classes: interference intensity, correlation properties, spectrum occupancy, and Doppler spectrum in licensed and unlicensed bands. Finally, We benefit from these features to train a machine learning algorithm to predict the availability of the licensed and unlicensed bands for vehicular network access to the fixed backbone network infrastructure.

Battery-free and intermittently powered devices offer long lifetimes and enable deployment in new applications and environments. Unfortunately, developing sophisticated inference-capable applications is still challenging due to the lack of platform support for more advanced (32-bit) microprocessors and specialized accelerators---which can execute data-intensive machine learning tasks, but add complexity across the stack when dealing with intermittent power. We present Protean to bridge the platform gap for inference-capable battery-free sensors. Designed for runtime scalability, meeting the dynamic range of energy harvesters with matching heterogeneous processing elements like neural network accelerators. We develop a modular "plug-and-play" hardware platform, SuperSensor, with a reconfigurable energy storage circuit that powers a 32-bit ARM-based microcontroller with a convolutional neural network accelerator. An adaptive task-based runtime system, Chameleon, provides intermittency-proof execution of machine learning tasks across heterogeneous processing elements. The runtime automatically scales and dispatches these tasks based on incoming energy, current state, and programmer annotations. A code generator, Metamorph, automates conversion of ML models to intermittent safe execution across heterogeneous compute elements. We evaluate Protean with audio and image workloads and demonstrate up to 666x improvement in inference energy efficiency by enabling usage of modern computational elements within intermittent computing. Further, Protean provides up to 166% higher throughput compared to non-adaptive baselines.

Partial computation offloading offers lower latency, privacy (by processing data close to the source), and higher performance. In partial offloading, some of the processing is performed on the less capable Internet-of-Things (IoTs), and more complex computation is performed in more capable cloud computers. Though many well-established security protocols are available to address external intrusion, it introduces significant overhead for large data sample. This overhead is becoming a challenging problem with the recent trend and promises of on-device deep neural network (DNN) inference. This paper studies how much prior data distribution knowledge is required by an intruder to retrieve the input from a partially computed vector. We explore simple and complex DNN tasks, which are unknown to the intruder. We show that for simple datasets, Auto-Encoder(AE) and Variational Auto Encoder(VAE) can retrieve the original input image with a 4% accuracy drop on average. However, the same is not valid for complex data distribution.

Intermittently powered embedded systems are a foundational and growing component of the Internet of Things. It is essential to rigorously prove these systems’ correctness because they arise both in safety-critical applications and applications where quality-of-service is essential to social good. Such proofs are challenging because they are simultaneously cyber–physical and time-sensitive: correctness is affected by physical properties that change with time. This article introduces a new general-purpose formal verification approach for cyber–physical properties of intermittent systems. We define a high-level modeling and specification language for intermittent systems, define its formal semantics, and prove that the language reduces to hybrid games, enabling the application of existing theorem-proving software. Cold storage for COVID vaccines serves as a running example; we provide a machine-checked proof that safe temperatures are maintained under suitable assumptions. The crux of our proof approach is to identify power and timing assumptions under which sufficient power is available to complete time-sensitive tasks. Orthogonal to approaches that prove new guarantees on power or timing, our work rigorously shows which power and timing assumptions are needed for cyber–physical correctness.

Tracking the type and frequency of cough events is critical for monitoring respiratory diseases. Coughs are one of the most common symptoms of respiratory and infectious diseases like COVID-19, and a cough monitoring system could have been vital in remote monitoring during a pandemic like COVID-19. While the existing solutions for cough monitoring use unimodal (e.g., audio) approaches for detecting coughs, a fusion of multimodal sensors (e.g., audio and accelerometer) from multiple devices (e.g., phone and watch) are likely to discover additional insights and can help to track the exacerbation of the respiratory conditions. However, such multimodal and multidevice fusion requires accurate time synchronization, which could be challenging for coughs as coughs are usually concise events (0.3-0.7 seconds). In this paper, we first demonstrate the time synchronization challenges of cough synchronization based on the cough data collected from two studies. Then we highlight the performance of a cross-correlation based time synchronization algorithm on the alignment of cough events. Our algorithm can synchronize 98.9% of cough events with an average synchronization error of 0.046s from two devices.

Mobil respiratory assessments using commodity smartphones and smartwatches are unmet needs for patient monitoring at home. In this paper, we show the feasibility of using multimodal sensors embedded in consumer mobile devices for non-invasive, low-effort respiratory assessment. We have conducted studies with 228 chronic respiratory patients and healthy subjects, and show that our model can estimate respiratory rate with mean absolute error (MAE) 0.72$\pm$0.62 breath per minute and differentiate respiratory patients from healthy subjects with 90% recall and 76% precision when the user breathes normally by holding the device on the chest or the abdomen for a minute. Holding the device on the chest or abdomen needs significantly lower effort compared to traditional spirometry which requires a specialized device and forceful vigorous breathing. This paper shows the feasibility of developing a low-effort respiratory assessment towards making it available anywhere, anytime through users' own mobile devices.

The recent development of extremely low-power computing devices and efficient energy harvesters led to the creation of computing systems that are powered by intermittently available harvested energy, e.g., solar, piezoelectric, and radio-frequency (RF). Such computing systems go through power-on and off phases due to the lack of adequate harvesting energy. These systems are known as Intermittent Computing Systems. While existing works on intermittent computing systems concentrate preliminary on the lower level goals, e.g., execution progress and memory consistency [1], [3], [5], [6], [9], the potential of such systems under timing constraints is yet to be explored. Some applications of intermittent systems with timing constraints include monitoring wildlife, health, infrastructure and environmental conditions, pedestrian safety, indoor localization and occupancy detection. In this work, we schedule tasks on intermittent systems where tasks may have timing constraints. We focus on the timely-response of intermittent systems by (1) developing unified frameworks that integrate harvesting and real-time systems, and (2) engineering machine learning algorithms for timely execution of the important portion of a task via imprecise scheduling.

The sporadic nature of harvestable energy and the mutually exclusive computing and charging cycles of intermittently powered batteryless systems pose a unique and challenging real-time scheduling problem. Existing literature focus either on the time or the energy constraints but not both at the same time. In this paper, we propose two scheduling algorithms, named Celebi-Offline and Celebi-Online, for intermittent systems that schedule both computational and energy harvesting tasks by harvesting the required minimum amount of energy while maximizing the schedulability of computational jobs. To evaluate Celebi, we conduct simulation as well as trace-based and real-life experiments. Our results show that the proposed Celebi-Offline algorithm has 92% similar performance as an optimal scheduler, and Celebi-Online scheduler schedules 8% - 22% more jobs than the earliest deadline first (EDF), rate monotonic (RM), and as late as possible (ALAP) scheduling algorithms. We deployed solar-powered batteryless systems where four intermittent applications are executed in the TI-MSP430FR5994 microcontroller and demonstrate that the system with Celebi-Online misses 63% less deadline than a non-realtime system and 8% less deadline than the system with a baseline (as late as possible) scheduler.

In this paper, we argue that the fusion of machine learning (ML) and batteryless computing systems enables true lifelong learning in mobile devices. The lack of learning from experience in current batteryless systems makes them ignorant of changes in their operating environment. Due to high communication cost, latency, privacy, and dependency issues of offloading computation to an edge device, on-device training is a solution for batteryless systems to learn and adapt in dynamically changing environments. Combining batteryless systems and ML is however a challenging task. Sporadic energy supply and limited resources in a batteryless system cause execution-discontinuity and data-constraints in ML processes. To understand these challenges, we identify suitable ML tasks for such systems and study the energy producers, i.e., harvesters, and consumers, i.e., intermittently executable tasks in a ML pipeline. Using a trace-driven simulation, we demonstrate the feasibility of on-device training of a batteryless learner.

With the prevalence of smartphones, pedestrians and joggers today often walk or run while listening to music. Since they are deprived of their auditory senses that would have provided important cues to dangers, they are at a much greater risk of being hit by cars or other vehicles. In this paper, we build a wearable system that uses multichannel audio sensors embedded in a headset to help detect and locate cars from their honks, engine, and tire noises, and warn pedestrians of imminent dangers of approaching cars. We demonstrate that using a segmented architecture consisting of headset-mounted audio sensors, a front-end hardware platform that performs signal processing and feature extraction, and machine learning-based classification on a smartphone, we are able to provide early danger detection in real time, from up to 60 m away, and alert the user with low latency and high accuracy. To further reduce power consumption of the battery-powered wearable headset, we implement a custom-designed integrated circuit that is able to compute delays between multiple channels of audio with nW power consumption. A regression-based method for sound source localization, angle via polygonal regression, is proposed and used in combination with the IC to improve the granularity and robustness of localization.

Low Power Wide Area Networks (LPWANs) are an excellent fit to city-scale IoT applications becuase of their long range and a battery life of several years, and a data rate of 25-50kbps, which is sufficient to carry IoT traffic. However, a practical limitation of a LPWAN-based real-time wireless network is the duty-cycle limit imposed on the sub-1GHz band by the FCC. In this paper, we overcome this challenge by proposing the first duty-cycle-aware wireless link scheduling algorithm for real-time LPWANs that considers the urgency of the packets as well as the availability of the wireless channels. The proposed algorithm is implemented in a five-node, wide-area outdoor test-bed in multiple realworld scenarios. Simulation results are provided to quantify its performance under different settings (e.g. larger networks, variety of workloads, and multiple baselines). In both realworld deployments and simulations, the proposed algorithm outperforms standard scheduling algorithms in terms of link schedulability, deadline misses, and buffer size.

This demo is an implementation of our motion-triggered camera system that captures, processes, stores, and transmits 3D visual information of a real-world environment using a low-cost camera-based sensor system that is constrained by its limited processing capability, storage, and battery life. This system can be used in applications such as capturing and sharing 3D content in the social media, training people in different professions, and post-facto analysis of an event. This system uses off-the-shelf hardware and standard computer vision algorithms. Its novelty lies in the ability to optimally control camera data acquisition and processing stages to guarantee the desired quality of captured information and battery life. The design of the controller is based on extensive measurements and modeling of the relationships between the linear and angular motion of a camera and the quality of generated 3D point clouds as well as the battery life of the system. To achieve this, we 1) devise a new metric to quantify the quality of generated 3D point clouds, 2) formulate an optimization problem to find an optimal trigger point for the camera system and prolongs its battery life while maximizing the quality of captured 3D environment and 3) make the model adaptive so that the system evolves and its performance improves over time.

The Glimpse.3D is a body-worn camera that captures, processes, stores, and transmits 3D visual information of a real-world environment using a low-cost camera-based sensor system that is constrained by its limited processing capability, storage, and battery life. The 3D content is viewed on a mobile device such as a smartphone or a virtual reality headset. This system can be used in applications such as capturing and sharing 3D content in the social media, training people in different professions, and post-facto analysis of an event. Glimpse.3D uses off-the-shelf hardware and standard computer vision algorithms. Its novelty lies in the ability to optimally control camera data acquisition and processing stages to guarantee the desired quality of captured information and battery life. The design of the controller is based on extensive measurements and modeling of the relationships between the linear and angular motion of a body-worn camera and the quality of generated 3D point clouds as well as the battery life of the system. To achieve this, we 1) devise a new metric to quantify the quality of generated 3D point clouds, 2) formulate an optimization problem to find an optimal trigger point for the camera system that prolongs its battery life while maximizing the quality of captured 3D environment, and 3) make the model adaptive so that the system evolves and its performance improves over time.

Mobility tracking of IoT devices in smart city infrastructures such as smart buildings, hospitals, shopping centers, warehouses, smart streets, and outdoor spaces has many applications. Since Bluetooth Low Energy (BLE) is available in almost every IoT device in the market nowadays, a key to localizing and tracking IoT devices is to develop an accurate ranging technique for BLE-enabled IoT devices. This is, however, a challenging feat as billions of these devices are already in use, and for pragmatic reasons, we cannot propose to modify the IoT device (a BLE peripheral) itself. Furthermore, unlike WiFi ranging - where the channel state information (CSI) is readily available and the bandwidth can be increased by stitching 2.4GHz and 5GHz bands together to achieve a high-precision ranging, an unmodified BLE peripheral provides us with only the RSSI information over a very limited bandwidth. Accurately ranging a BLE device is therefore far more challenging than other wireless standards. In this paper, we exploit characteristics of BLE protocol (e.g. frequency hopping and empty control packet transmissions) and propose a technique to directly estimate the range of a BLE peripheral from a BLE access point by multipath profiling. We discuss the theoretical foundation and conduct experiments to show that the technique achieves a 2.44m absolute range estimation error on average.

In this paper, we study the overhearing problem of continuous acoustic sensing devices such as Amazon Echo, Google Home, or such voice-enabled home hubs, and develop a system called SoundSifter that mitigates personal or contextual information leakage due to the presence of unwanted sound sources in the acoustic environment. Instead of proposing modifications to existing home hubs, we build an independent embedded system that connects to a home hub via its audio input. Considering the aesthetics of home hubs, we envision SoundSifter as a smart sleeve or a cover for these devices. SoundSifter has hardware and software to capture the audio, isolate signals from distinct sound sources, filter out signals that are from unwanted sources, and process the signals to enforce policies such as personalization before the signals enter into an untrusted system like Amazon Echo or Google Home. We conduct empirical and real-world experiments to demonstrate that SoundSifter runs in real-time, is noise resilient, and supports selective and personalized voice commands that commercial voice-enabled home hubs do not.

With the prevalence of smartphones, pedestrians and joggers today often walk or run while listening to music. Since they are deprived of their auditory senses that would have provided important cues to dangers, they are at a much greater risk of being hit by cars or other vehicles. In this demonstration we present SEUS, a wearable system aimed at Sense Enhancement for Urban Safety. SEUS uses a three-stage architecture, consisting of headset mounted audio sensors, an embedded front-end for signal processing and feature extraction, and machine learning based classification on a smartphone, to provide early danger detection for pedestrians in real-time.