Security and Privacy Vulnerabilities in Human Activity Recognition systems

Liagkou, Vasiliki; Sakka, Sofia; Stylios, Chrysostomos

doi:10.1109/seeda-cecnsm57760.2022.9932957

Cited by 2 publications

(2 citation statements)

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“…In addition, its phase response is linear and thus does not introduce any phase distortion, thus preserving the temporal information of the signal. The core mathematical relationship of the Butterworth filter is shown in Equation (3).…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Human activity recognition (HAR) is the process of automatic detection and classification of human motion states using sensor data, machine learning, and pattern recognition. In recent years, HAR has been applied in many different fields, such as smart homes [1], healthcare [2] (monitoring the health status of elderly and disabled people), and security [3] (biometric or face recognition). Traditional human motion recognition methods include computer vision [4], wearable sensors [5], and radar [6].…”

Section: Introductionmentioning

confidence: 99%

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SM-TCNNET: A High-Performance Method for Detecting Human Activity Using WiFi Signals

Gao

Zhu

et al. 2023

Applied Sciences

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Human activity recognition (HAR) is an important research area with a wide range of application scenarios, such as smart homes, healthcare, abnormal behavior detection, etc. Wearable sensors, computer vision, radar, and other technologies are commonly used to detect human activity. However, they are severely limited by issues such as cost, lighting, context, and privacy. Therefore, this paper explores a high-performance method of using channel state information (CSI) to identify human activities, which is a deep learning-based spatial module-temporal convolutional network (SM-TCNNET) model. The model consists of a spatial feature extraction module and a temporal convolutional network (TCN) that can extract the spatiotemporal features in CSI signals well. In this paper, extensive experiments are conducted on the self-picked dataset and the public dataset (StanWiFi), and the results show that the accuracy reaches 99.93% and 99.80%, respectively. Compared with the existing methods, the recognition accuracy of the SM-TCNNET model proposed in this paper is improved by 1.8%.

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Section: Data Preprocessingmentioning

confidence: 99%