Enabling Identification and Behavioral Sensing in Homes using Radio Reflections

Hsu, Chen-Yang; Hristov, Rumen; Lee, Guang-He; Zhao, M.; Katabi, Dina

doi:10.1145/3290605.3300778

Cited by 73 publications

(50 citation statements)

References 50 publications

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“…The signal frequency difference is relatively low, generally kHz, so the processing hardware is relatively simple and suitable for data acquisition and digital signal processing. FMCW signals are widely used in human sensing [15][16][17]. For example, RF-Capture [15] uses a combination of FMCW and antenna arrays to estimate the distance and direction between humans and antennas to track human motions.…”

Section: Fmcwmentioning

confidence: 99%

“…is a point on the n th ellipse. For a given radio wavelength λ, the boundary of the n th Fresnel zone is calculated as Equation (17).…”

Section: Amplitudementioning

confidence: 99%

“…Training-once classification requires the valid features robust to the variations in the surrounding environment [12,13,32,37,40,42,44,45,[47][48][49]56,59,60,70,72,73,75,76,78,79,83,84,89,90,92,127,130]. Deep learning automatically extracts features, which often requires only one time of training [16,17,21,48,59,67,77,88,95,96,108]. For features that change dramatically with the environment, multiple times of training should be performed when there is some change in the environments.…”

Section: Activity Classificationmentioning

confidence: 99%

“…For tracking applications, this survey mainly includes human motion tracking [41,71,129], human tracking [15,17,22,24,26,28,29,48,53,58,[64][65][66][67]70,100,103,105,110,112,113,128,129,136], in-air finger tracking [11,115], and walking direction tracking [106], and human indoor localization [25,[101][102][103][104]114,124,128,132,137,138]. The difference between tracking application and the above three types of applications is that they calculate spatial information, such as direction, speed, or distance estimation.…”

Section: Tracking Applicationsmentioning

confidence: 99%

“…When multiple persons are sharing the same physical space, the received signals will contain all the impacts by all the persons' motions. Existing FMCW and antenna array-based solutions like [17] can track the hand gestures of multiple people simultaneously, leveraging a directional antenna array. For other wireless signals, a promising way to isolate concurrent activities of different people is to separate sensing space with a complex web of wireless links in the area.…”

Section: Wireless Sensing Challengesmentioning

confidence: 99%

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Human Activity Sensing with Wireless Signals: A Survey

Liu

Teng

Hong

2020

Sensors

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Wireless networks have been widely deployed with a high demand for wireless data traffic. The ubiquitous availability of wireless signals brings new opportunities for non-intrusive human activity sensing. To enhance a thorough understanding of existing wireless sensing techniques and provide insights for future directions, this survey conducts a review of the existing research on human activity sensing with wireless signals. We review and compare existing research of wireless human activity sensing from seven perspectives, including the types of wireless signals, theoretical models, signal preprocessing techniques, activity segmentation, feature extraction, classification, and application. With the development and deployment of new wireless technology, there will be more sensing opportunities in human activities. Based on the analysis of existing research, the survey points out seven challenges on wireless human activity sensing research: robustness, non-coexistence of sensing and communications, privacy, multiple user activity sensing, limited sensing range, complex deep learning, and lack of standard datasets. Finally, this survey presents four possible future research trends, including new theoretical models, the coexistence of sensing and communications, awareness of sensing on receivers, and constructing open datasets to enable new wireless sensing opportunities on human activities.

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Section: Fmcwmentioning

confidence: 99%

“…is a point on the n th ellipse. For a given radio wavelength λ, the boundary of the n th Fresnel zone is calculated as Equation (17).…”

Section: Amplitudementioning

confidence: 99%

Section: Activity Classificationmentioning

confidence: 99%

Section: Tracking Applicationsmentioning

confidence: 99%

Section: Wireless Sensing Challengesmentioning

confidence: 99%

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Human Activity Sensing with Wireless Signals: A Survey

Liu

Teng

Hong

2020

Sensors

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Unauthorized and privacy‐intrusive human activity watching through Wi‐Fi signals: An emerging cybersecurity threat

Zhao

Bhuiyan

et al. 2022

Concurrency and Computation

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Summary Nowadays, wireless radio signals are ubiquitous and are around us; some signals pass through us, and some reflect off us. Substantial advancements in recent years demonstrate that such signals are utilized for diverse emerging applications, including people activity, motion watches, healthcare, and so forth. A few questions would be that may raise severe concerns in future cybersecurity and private domains. For example, what if Wi‐Fi signals are utilized to watch a person doings and actions, which are mostly without the person's authorization and authentication. How far such signal utilization can attack privacy intrusively, silently, more particularly, what/where we do, say, command, see, write, draw, go, perform, everything can be known. In this article, we investigate watching human activities by leveraging Wi‐Fi signals and discuss a few application prototypes. We attempt to learn whether or not attackers have the ability to passively watch our Internet activity as well as physical activities and motions through Wi‐Fi. With all‐new advances, one must be aware that cyberattackers may apply unauthorized use of these advances to their benefit. We discuss some of the countermeasures and approaches to mitigate these risks with Wi‐Fi signal leveraging.

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In-Home Daily-Life Captioning Using Radio Signals

Fan¹,

Li²,

Yuan³

et al. 2020

Lecture Notes in Computer Science

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This paper aims to caption daily life -i.e., to create a textual description of people's activities and interactions with objects in their homes. Addressing this problem requires novel methods beyond traditional video captioning, as most people would have privacy concerns about deploying cameras throughout their homes. We introduce RF-Diary, a new model for captioning daily life by analyzing the privacy-preserving radio signal in the home with the home's floormap. RF-Diary can further observe and caption people's life through walls and occlusions and in dark settings. In designing RF-Diary, we exploit the ability of radio signals to capture people's 3D dynamics, and use the floormap to help the model learn people's interactions with objects. We also use a multi-modal feature alignment training scheme that leverages existing video-based captioning datasets to improve the performance of our radio-based captioning model. Extensive experimental results demonstrate that RF-Diary generates accurate captions under visible conditions. It also sustains its good performance in dark or occluded settings, where video-based captioning approaches fail to generate meaningful captions. 1

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Enabling Identification and Behavioral Sensing in Homes using Radio Reflections

Cited by 73 publications

References 50 publications

Human Activity Sensing with Wireless Signals: A Survey

Human Activity Sensing with Wireless Signals: A Survey

Unauthorized and privacy‐intrusive human activity watching through Wi‐Fi signals: An emerging cybersecurity threat

In-Home Daily-Life Captioning Using Radio Signals

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