Universal targeted attacks against mmWave-based human activity recognition system

Xie, Yucheng; Jiang, Rui; Guo, Xiaonan; Wang, Yan; Cheng, Jerry; Chen, Yingying

doi:10.1145/3498361.3538774

Cited by 3 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the camera-based method invades user privacy, suffers from limited recognition accuracy due to ambient light, and has issues with visual blind spots [17]. To tackle these issues, studies introduce HAR systems based on acoustic signals [18], Millimeter Wave (mmWave) [19], Ultra-Wideband (UWB) [20] and Wi-Fi devices [21]. Sound and mmWave are suitable for scenes with a smaller monitoring range, and mmWave is suitable for accurate recognition of tiny movements and the cost is higher.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition via Wi-Fi and Inertial Sensors With Machine Learning

Guo,

Yamagishi,

Jing

2024

IEEE Access

View full text Add to dashboard Cite

Human activity recognition (HAR) plays a crucial role in human-computer interaction, smart home, health monitoring and elderly care. However, existing methods typically utilize camera, radio frequency (RF) signals or wearable devices for activity recognition. Each single-sensor modality has its inherent limitations, like camera-based methods having blind spots, Wi-Fi-based methods depending on the environment and the inconvenience of wearing Inertial Measurement Unit (IMU) devices. In this paper, we propose a HAR system that leverages three types of sensor combinations: Wi-Fi, IMU and a hybrid of Wi-Fi+IMU. We utilize the Channel State Information (CSI) provided by Wi-Fi and the accelerometer and gyroscope data from IMU devices to capture activity characteristics. Then, we employ six machine learning algorithms to recognize eight types of daily activities. These algorithms include Support Vector Machine (SVM), Multi-layer Perceptron (MLP), Decision Tree, Random Forest, Logistic Regression and k-Nearest Neighbors (kNN). Additionally, we investigate the accuracy of hand gesture recognition using different sensor combinations and analyze the calculation speed of each combination. We conduct a survey to collect user feedback on the performance of various sensor combinations in our HAR system. The results show that the combination of CSI+IMU yields the best HAR recognition accuracy, with a accuracy of 89.38%. The SVM algorithm consistently performs well across all systems, especially excelling in the CSI+IMU system supported by energy and average Fast Fourier Transform (FFT) features. However, we also find that the success of sensor fusion depends on specific algorithms and features. Fusion of CSI and IMU does not universally enhance recognition accuracy for all features and algorithms and can, in some cases, actually reduce accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition via Wi-Fi and Inertial Sensors With Machine Learning

Guo,

Yamagishi,

Jing

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Headar

Yang,

Wang,

Dong

et al. 2023

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

View full text Add to dashboard Cite

Nod and shake of one's head are intuitive and universal gestures in communication. As smartwatches become increasingly intelligent through advances in user activity sensing technologies, many use scenarios of smartwatches demand quick responses from users in confirmation dialogs, to accept or dismiss proposed actions. Such proposed actions include making emergency calls, taking service recommendations, and starting or stopping exercise timers. Head gestures in these scenarios could be preferable to touch interactions for being hands-free and easy to perform. We propose Headar to recognize these gestures on smartwatches using wearable millimeter wave sensing. We first surveyed head gestures to understand how they are performed in conversational settings. We then investigated positions and orientations to which users raise their smartwatches. Insights from these studies guided the implementation of Headar. Additionally, we conducted modeling and simulation to verify our sensing principle. We developed a real-time sensing and inference pipeline using contemporary deep learning techniques, and proved the feasibility of our proposed approach with a user study (n=15) and a live test (n=8). Our evaluation yielded an average accuracy of 84.0% in the user study across 9 classes including nod and shake as well as seven other signals -- still, speech, touch interaction, and four non-gestural head motions (i.e., head up, left, right, and down). Furthermore, we obtained an accuracy of 72.6% in the live test which reveals rich insights into the performance of our approach in various realistic conditions.

show abstract

Practical Adversarial Attack on WiFi Sensing Through Unnoticeable Communication Packet Perturbation

Li,

Xu,

et al. 2024

Proceedings of the 30th Annual International Conference on Mobile Computing and Networking

View full text Add to dashboard Cite

Universal targeted attacks against mmWave-based human activity recognition system

Cited by 3 publications

References 3 publications

Human Activity Recognition via Wi-Fi and Inertial Sensors With Machine Learning

Human Activity Recognition via Wi-Fi and Inertial Sensors With Machine Learning

Headar

Practical Adversarial Attack on WiFi Sensing Through Unnoticeable Communication Packet Perturbation

Contact Info

Product

Resources

About