Diverse Wi-Fi-based wireless applications have been proposed, ranging from daily activity recognition to vital sign monitoring. Despite their remarkable sensing accuracy, the high energy consumption and the requirement for customized hardware modification hinder the wide deployment of the existing sensing solutions. In this paper, we propose REHSense, an energy-efficient wireless sensing solution based on Radio-Frequency (RF) energy harvesting. Instead of relying on a power-hungry Wi-Fi receiver, REHSense leverages an RF energy harvester as the sensor and utilizes the voltage signals harvested from the ambient Wi-Fi signals to enable simultaneous context sensing and energy harvesting. We design and implement REHSense using a commercial-off-the-shelf (COTS) RF energy harvester. Extensive evaluation of three fine-grained wireless sensing tasks (i.e., respiration monitoring, human activity recognition, and hand gesture recognition) shows that REHSense can achieve comparable sensing accuracy with conventional Wi-Fi-based solutions while adapting to different sensing environments, reducing the power consumption of sensing by 98.7% and harvesting up to 4.5 mW of power from RF energy.

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Eye gaze contains rich information about human attention and cognitive processes. This capability makes the underlying technology, known as gaze tracking, a critical enabler for many ubiquitous applications and has triggered the development of easy-to-use gaze estimation services. Indeed, by utilizing the ubiquitous cameras on tablets and smartphones, users can readily access many gaze estimation services. In using these services, users must provide their full-face images to the gaze estimator, which is often a black box. This poses significant privacy threats to the users, especially when a malicious service provider gathers a large collection of face images to classify sensitive user attributes. In this work, we present PrivateGaze, the first approach that can effectively preserve users’ privacy in black-box gaze tracking services without compromising gaze estimation performance. Specifically, we proposed a novel framework to train a privacy preserver that converts full-face images into obfuscated counterparts, which are effective for gaze estimation while containing no privacy information. Evaluation on four datasets shows that the obfuscated image can protect users’ private information, such as identity and gender, against unauthorized attribute classification. Meanwhile, when used directly by the black-box gaze estimator as inputs, the obfuscated images lead to comparable tracking performance to the conventional, unprotected full-face images.

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Smartphones are moving towards the fullscreen design for better user experience. This trend forces front cameras to be placed under screen, leading to Under-Screen Cameras (USC). Accordingly, a small area of the screen is made translucent to allow light to reach the USC. In this paper, we utilize the translucent screen's features to inconspicuously modify its pixels, imperceptible to human eyes but inducing perturbations on USC images. These screen perturbations affect deep learning models in image classification and face recognition. They can be employed to protect user privacy, or disrupt the front camera's functionality in the malicious case. We design two methods, one-pixel perturbation and multiple-pixel perturbation, that can add screen perturbations to images captured by USC and successfully fool various deep learning models. Our evaluations, with three commercial full-screen smartphones on testbed datasets and synthesized datasets, show that screen perturbations significantly decrease the average image classification accuracy, dropping from 85% to only 14% for one-pixel perturbation and 5.5% for multiple-pixel perturbation. For face recognition, the average accuracy drops from 91% to merely 1.8% and 0.25%, respectively.@en

We design a system, SolarGest, which can recognize hand gestures near a solar-powered device by analyzing the patterns of the photocurrent. SolarGest is based on the observation that each gesture interferes with incident light rays on the solar panel in a unique way, leaving its discernible signature in harvested photocurrent. Using solar energy harvesting laws, we develop a model to optimize design and usage of SolarGest. To further improve the robustness of SolarGest under non-deterministic operating conditions, we combine dynamic time warping with Z-score transformation in a signal processing pipeline to pre-process each gesture waveform before it is analyzed for classification. We evaluate SolarGest with both conventional opaque solar cells as well as emerging see-through transparent cells. Our experiments demonstrate that SolarGest achieves 99% for six gestures with a single cell and 95% for fifteen gesture with a 2 × 2 solar cell array. The power measuement study suggests that SolarGest consume 44% less power compared to light sensor based systems.

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Radio-frequency (RF) energy harvesting is a promising technology for Internet-of-Things (IoT) devices to power sensors and prolong battery life. In this paper, we present a novel side-channel attack that leverages RF energy harvesting signals to eavesdrop mobile app activities. To demonstrate this novel attack, we propose AppListener, an automated attack framework that recognizes fine-grained mobile app activities from harvested RF energy. The RF energy is harvested from a custom-built RF energy harvester which generates voltage signals from ambient Wi-Fi transmissions, and app activities are recognized from a three-tier classification algorithm. We evaluate AppListener with four mobile devices running 40 common mobile apps (e.g., YouTube, Facebook, and WhatsApp) belonging to five categories (i.e., video, music, social media, communication, and game); each category contains five application-specific activities. Experiment results show that AppListener achieves over 99% accuracy in differentiating four different mobile devices, over 98% accuracy in classifying 40 different apps, and 86.7% accuracy in recognizing five sets of application-specific activities. Moreover, a comprehensive study is conducted to show AppListener is robust to a number of impact factors, such as distance, environment, and non-target connected devices. Practices of integrating AppListener into commercial IoT devices also demonstrate that it is easy to deploy. Finally, countermeasures are presented as the first step to defend against this novel attack.

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This paper presents EMGSense, a low-effort self-supervised domain adaptation framework for sensing applications based on Electromyography (EMG). EMGSense addresses one of the fundamental challenges in EMG cross-user sensing—the significant performance degradation caused by time-varying biological heterogeneity—in a low-effort (data-efficient and label-free) manner. To alleviate the burden of data collection and avoid labor-intensive data annotation, we propose two EMG-specific data augmentation methods to simulate the EMG signals generated in various conditions and scope the exploration in label-free scenarios. We model combating biological heterogeneity-caused performance degradation as a multi-source domain adaptation problem that can learn from the diversity among source users to eliminate EMG heterogeneous biological features. To relearn the target-user-specific biological features from the unlabeled data, we integrate advanced self-supervised techniques into a carefully designed deep neural network (DNN) structure. The DNN structure can seamlessly perform two training stages that complement each other to adapt to a new user with satisfactory performance. Comprehensive evaluations on two sizable datasets collected from 13 participants indicate that EMGSense achieves an average accuracy of 91.9% and 81.2% in gesture recognition and activity recognition, respectively. EMGSense outperforms the state-of-the-art EMG-oriented domain adaptation approaches by 12.5%-17.4% and achieves a comparable performance with the one trained in a supervised learning manner.@en

Smart space has emerged as a new paradigm that combines sensing, communication, and artificial intelligence technologies to offer various customized services. A fundamental requirement of these services is person identification. Although a variety of person-identification approaches has been proposed, they suffer from several limitations in practical applications, such as low energy efficiency, accuracy degradation, and privacy issue. This article proposes an energy-harvesting-based privacy-preserving gait recognition scheme for smart space, which is named PrivGait. In PrivGait, we extract discriminative features from 1-D gait signal and design an attention-based long short-term memory (LSTM) network to classify different people. Moreover, we leverage a novel Bloom filter-based privacy-preserving technique to address the privacy leakage problem. To demonstrate the feasibility of PrivGait, we design a proof-of-concept prototype using off-the-shelf energy-harvesting hardware. Extensive evaluation results show that the proposed scheme outperforms state of the art by 6%-10% and incurs low system cost while preserving user's privacy.

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The personalization of augmented reality (AR) experiences based on environmental and user context is key to unlocking their full potential. The recent addition of eye tracking to AR headsets provides a convenient method for detecting user context, but complex analysis of raw gaze data is required to detect where a user's attention and thoughts truly lie. In this demo we present Catch My Eye, the first system to incorporate deep neural network (DNN)-based activity recognition from user gaze into a realistic mobile AR app. We develop an edge computing-based architecture to offload context computation from resource-constrained AR devices, and present a working example of content adaptation based on user context, for the scenario of a virtual art gallery. It shows that user activities can be accurately recognized and employed with sufficiently low latency for practical AR applications.@en

Recent advances in eye tracking have given birth to a new genre of gaze-based context sensing applications, ranging from cognitive load estimation to emotion recognition. To achieve state-of-the-art recognition accuracy, a large-scale, labeled eye movement dataset is needed to train deep learning-based classifiers. However, due to the heterogeneity in human visual behavior, as well as the labor-intensive and privacy-compromising data collection process, datasets for gaze-based activity recognition are scarce and hard to collect. To alleviate the sparse gaze data problem, we present EyeSyn, a novel suite of psychology-inspired generative models that leverages only publicly available images and videos to synthesize a realistic and arbitrarily large eye movement dataset. Taking gaze-based museum activity recognition as a case study, our evaluation demonstrates that EyeSyn can not only replicate the distinct pat-terns in the actual gaze signals that are captured by an eye tracking device, but also simulate the signal diversity that results from dif-ferent measurement setups and subject heterogeneity. Moreover, in the few-shot learning scenario, EyeSyn can be readily incorpo-rated with either transfer learning or meta-learning to achieve 90% accuracy, without the need for a large-scale dataset for training.@en