A review on radio based activity recognition

Wang, Shuangquan; Zhou, Gang

doi:10.1016/j.dcan.2015.02.006

Cited by 91 publications

(40 citation statements)

References 56 publications

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“…Threats that attempt to infer private information, usercontexts or user-activities by capturing related electromagnetic, acoustic, optical and/or mechanical emanations from a target device or user and employing them as information side-channels have been well-studied in the literature [40], [27], [4], [6], [11], [9], [7], [46], [8], [21], [5], [49], [28]. With the advent of smartphones, researchers started focusing on employing the phone's on-board hardware and software sensors to investigate the feasibility of similar inference attacks [45].…”

Section: Related Workmentioning

confidence: 99%

Towards Inferring Mechanical Lock Combinations using Wrist-Wearables as a Side-Channel

Maiti

Heard

Sabra

et al. 2018

Proceedings of the 11th ACM Conference on Security &Amp; Privacy in Wireless and Mobile Networks

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Wrist-wearables such as smartwatches and fitness bands are equipped with a variety of high-precision sensors that support novel contextual and activity-based applications. The presence of a diverse set of on-board sensors, however, also expose an additional attack surface which, if not adequately protected, could be potentially exploited to leak private user information. In this work, we investigate the feasibility of a new attack that takes advantage of a wrist-wearable's motion sensors to infer input on mechanical devices typically used to secure physical access, for example, combination locks. We outline an inference framework that attempts to infer a lock's unlock combination from the wrist motion captured by a smartwatch's gyroscope sensor, and uses a probabilistic model to produce a ranked list of likely unlock combinations. We conduct a thorough empirical evaluation of the proposed framework by employing unlocking-related motion data collected from human subject participants in a variety of controlled and realistic settings. Evaluation results from these experiments demonstrate that motion data from wrist-wearables can be effectively employed as a side-channel to significantly reduce the unlock combination search-space of commonly found combination locks, thus compromising the physical security provided by these locks.

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Section: Related Workmentioning

confidence: 99%

Towards Inferring Mechanical Lock Combinations using Wrist-Wearables as a Side-Channel

Maiti

Heard

Sabra

et al. 2018

Proceedings of the 11th ACM Conference on Security &Amp; Privacy in Wireless and Mobile Networks

View full text Add to dashboard Cite

show abstract

“…[6], where the aim is to face the phenomenon of aging population. Following the same unobtrusive approach, researchers are working also on radio-based techniques [7], where they take advantage of signal attenuation due to the body, and channel fading of wireless radio. Other works have been also published considering wearable devices, such as smartphones, that can be used to collect data and to classify actions [8].…”

Section: Methods and Technologies For Harmentioning

confidence: 99%

Human Action Recognition with RGB-D Sensors

Cippitelli¹,

Gambi²,

Spinsante³

2017

Motion Tracking and Gesture Recognition

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Human action recognition, also known as HAR, is at the foundation of many different applications related to behavioral analysis, surveillance, and safety, thus it has been a very active research area in the last years. The release of inexpensive RGB-D sensors fostered researchers working in this field because depth data simplify the processing of visual data that could be otherwise difficult using classic RGB devices. Furthermore, the availability of depth data allows to implement solutions that are unobtrusive and privacy preserving with respect to classic video-based analysis. In this scenario, the aim of this chapter is to review the most salient techniques for HAR based on depth signal processing, providing some details on a specific method based on temporal pyramid of key poses, evaluated on the well-known MSR Action3D dataset.

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“…The advantage of camera assistance is that multiple subjects can be detected at the same time without the aid of other devices [6]. Although the accuracy rate is high in some cases, the practical value of this method is undermined by its high requirements of equipment and its tendency to suffer high interference from environmental factors [7]. Moreover, camera-based methods can generate privacy issues [8].…”

Section: Introductionmentioning

confidence: 99%

A User-Adaptive Algorithm for Activity Recognition Based on K-Means Clustering, Local Outlier Factor, and Multivariate Gaussian Distribution

Zhao

Cao

2018

Sensors

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Mobile activity recognition is significant to the development of human-centric pervasive applications including elderly care, personalized recommendations, etc. Nevertheless, the distribution of inertial sensor data can be influenced to a great extent by varying users. This means that the performance of an activity recognition classifier trained by one user’s dataset will degenerate when transferred to others. In this study, we focus on building a personalized classifier to detect four categories of human activities: light intensity activity, moderate intensity activity, vigorous intensity activity, and fall. In order to solve the problem caused by different distributions of inertial sensor signals, a user-adaptive algorithm based on K-Means clustering, local outlier factor (LOF), and multivariate Gaussian distribution (MGD) is proposed. To automatically cluster and annotate a specific user’s activity data, an improved K-Means algorithm with a novel initialization method is designed. By quantifying the samples’ informative degree in a labeled individual dataset, the most profitable samples can be selected for activity recognition model adaption. Through experiments, we conclude that our proposed models can adapt to new users with good recognition performance.

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A review on radio based activity recognition

Cited by 91 publications

References 56 publications

Towards Inferring Mechanical Lock Combinations using Wrist-Wearables as a Side-Channel

Towards Inferring Mechanical Lock Combinations using Wrist-Wearables as a Side-Channel

Human Action Recognition with RGB-D Sensors

A User-Adaptive Algorithm for Activity Recognition Based on K-Means Clustering, Local Outlier Factor, and Multivariate Gaussian Distribution

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