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Sensorless sensing with WiFi
- Tsinghua Sci. Technol
, 2015
"... Abstract: Can WiFi signals be used for sensing purpose? The growing PHY layer capabilities of WiFi has made it possible to reuse WiFi signals for both communication and sensing. Sensing via WiFi would enable remote sensing without wearable sensors, simultaneous perception and data transmission witho ..."
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Abstract: Can WiFi signals be used for sensing purpose? The growing PHY layer capabilities of WiFi has made it possible to reuse WiFi signals for both communication and sensing. Sensing via WiFi would enable remote sensing without wearable sensors, simultaneous perception and data transmission without extra communication infrastructure, and contactless sensing in privacy-preserving mode. Due to the popularity of WiFi devices and the ubiquitous deployment of WiFi networks, WiFi-based sensing networks, if fully connected, would potentially rank as one of the world’s largest wireless sensor networks. Yet the concept of wireless and sensorless sensing is not the simple combination of WiFi and radar. It seeks breakthroughs from dedicated radar systems, and aims to balance between low cost and high accuracy, to meet the rising demand for pervasive environment perception in everyday life. Despite increasing research interest, wireless sensing is still in its infancy. Through introductions on basic principles and working prototypes, we review the feasibilities and limitations of wireless, sensorless, and contactless sensing via WiFi. We envision this article as a brief primer on wireless sensing for interested readers to explore this open and largely unexplored field and create next-generation wireless and mobile computing applications. Key words: Channel State Information (CSI); sensorless sensing; WiFi; indoor localization; device-free human detection; activity recognition; wireless networks; ubiquitous computing 1
WiFi-Based Indoor Line-of-Sight Identification
"... sively deployed and have fostered myriad wireless communica-tion services and ubiquitous computing applications. A primary concern in designing these applications is to combat harsh in-door propagation environments, particularly Non-Line-Of-Sight (NLOS) propagation. The ability to identify the exist ..."
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sively deployed and have fostered myriad wireless communica-tion services and ubiquitous computing applications. A primary concern in designing these applications is to combat harsh in-door propagation environments, particularly Non-Line-Of-Sight (NLOS) propagation. The ability to identify the existence of the Line-Of-Sight (LOS) path acts as a key enabler for adaptive com-munication, cognitive radios, and robust localization. Enabling such capability on commodity WiFi infrastructure, however, is prohibitive due to the coarse multipath resolution with MAC-layer received signal strength. In this paper, we propose two PHY-layer channel-statistics-based features from both the time and frequency domains. To further break away from the intrinsic bandwidth limit of WiFi, we extend to the spatial domain and harness natural mobility to magnify the randomness of NLOS paths while retain-ing the deterministic nature of the LOS component. We propose LiFi, a statistical LOS identification scheme with commodity WiFi infrastructure, and evaluate it in typical indoor environments covering an area of 1500 m2. Experimental results demonstrate that LiFi achieves an overall LOS detection rate of 90.42 % with a false alarm rate of 9.34 % for the temporal feature and an overall LOS detection rate of 93.09 % with a false alarm rate of 7.29 % for the spectral feature. Index Terms—Communication technology, wireless communi-cation, wireless networks.
Non-Invasive Detection of Moving and Stationary Human With WiFi
, 2014
"... Abstract—Non-invasive human sensing based on radio signals has attracted a great deal of research interest and fostered a broad range of innovative applications of localization, gesture recognition, smart health-care, etc., for which a primary primitive is to detect human presence. Previous works ha ..."
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Abstract—Non-invasive human sensing based on radio signals has attracted a great deal of research interest and fostered a broad range of innovative applications of localization, gesture recognition, smart health-care, etc., for which a primary primitive is to detect human presence. Previous works have studied the detection of moving humans via signal variations caused by human movements. For stationary people, however, existing approaches often employ a prerequisite scenario-tailored calibration of chan-nel profile in human-free environments. Based on in-depth under-standing of human motion induced signal attenuation reflected by PHY layer channel state information (CSI), we propose DeMan, a unified scheme for non-invasive detection of moving and stationary human on commodity WiFi devices. DeMan takes advantage of both amplitude and phase information of CSI to detect moving targets. In addition, DeMan considers human breathing as an intrinsic indicator of stationary human presence and adopts so-phisticated mechanisms to detect particular signal patterns caused by minute chest motions, which could be destroyed by significant whole-body motion or hidden by environmental noises. By doing this, DeMan is capable of simultaneously detecting moving and stationary people with only a small number of prior measurements for model parameter determination, yet without the cumbersome scenario-specific calibration. Extensive experimental evaluation in typical indoor environments validates the great performance of DeMan in various human poses and locations and diverse channel conditions. Particularly, DeMan provides a detection rate of around 95 % for both moving and stationary people, while identifies human-free scenarios by 96%, all of which outperforms existing methods by about 30%. Index Terms—Non-invasive, human detection, calibration-free, human breathing, channel state information. I.
mTrack: High-Precision Passive Tracking Using Millimeter Wave Radios
"... Radio-based passive-object sensing can enable a new form of per-vasive user-computer interface. Prior work has employed various wireless signal features to sense objects under a set of predefined, coarse motion patterns. But an operational UI, like a trackpad, often needs to identify fine-grained, a ..."
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Radio-based passive-object sensing can enable a new form of per-vasive user-computer interface. Prior work has employed various wireless signal features to sense objects under a set of predefined, coarse motion patterns. But an operational UI, like a trackpad, often needs to identify fine-grained, arbitrary motion. This paper explores the feasibility of tracking a passive writing object (e.g., pen) at sub-centimeter precision. We approach this goal through a practical design, mTrack, which uses highly-directional 60 GHz millimeter-wave radios as key enabling technology. mTrack runs a discrete beam scanning mechanism to pinpoint the object’s initial location, and tracks its trajectory using a signal-phase based model. In ad-dition, mTrack incorporates novel mechanisms to suppress inter-ference from background reflections, taking advantage of the short wavelength of 60 GHz signals. We prototype mTrack and evaluate its performance on a 60 GHz reconfigurable radio platform. Exper-imental results demonstrate that mTrack can locate/track a pen with 90-percentile error below 8 mm, enabling new applications such as wireless transcription and virtual trackpad.
Extending Mobile Interaction Through Near-Field Visible Light Sensing
"... Mobile devices are shrinking their form factors for portability, but user-mobile interaction is becoming increasingly challenging. In this paper, we propose a novel system called Okuli to meet this challenge. Okuli is a compact, low-cost system that can augment a mobile device and extend its interac ..."
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Mobile devices are shrinking their form factors for portability, but user-mobile interaction is becoming increasingly challenging. In this paper, we propose a novel system called Okuli to meet this challenge. Okuli is a compact, low-cost system that can augment a mobile device and extend its interaction workspace to any nearby surface area. Okuli piggybacks on visible light communication modules, and uses a low-power LED and two light sensors to locate user’s finger within the workspace. It is built on a light propagation/reflection model that achieves around one-centimeter location precision, with zero run-time training overhead. We have prototyped Okuli as an Android peripheral, with a 3D-printed shroud to host the LED and light sensors. Our experiments demonstrate Okuli’s accuracy, stability, energy efficiency, as well as its potential in serving virtual keyboard and trackpad applications.
FEMO: A Platform for Free-weight Exercise Monitoring
"... Regular free-weight exercise helps to strengthen the body’s natural movements and stabilize muscles that are importan-t to strength, balance, and posture of human beings. Prior works have exploited wearable sensors or RF signal changes (e.g., WiFi and Blue-tooth) for activity sensing, recognition an ..."
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Regular free-weight exercise helps to strengthen the body’s natural movements and stabilize muscles that are importan-t to strength, balance, and posture of human beings. Prior works have exploited wearable sensors or RF signal changes (e.g., WiFi and Blue-tooth) for activity sensing, recognition and counting etc.. However, none of them have incorporate three key factors necessary for a practical free-weight exer-cise monitoring system: recognizing free-weight activities on site, assessing their qualities, and providing useful feedbacks to the bodybuilder promptly. Our FEMO system responds to these demands, providing an integrated free-weight ex-ercise monitoring service that incorporates all the essential functionalities mentioned above. FEMO achieves this by attaching passive RFID tags on the dumbbells and lever-aging the Doppler shift profile of the reflected backscatter signals for on-site free-weight activity recognition and as-sessment. The rationale behind FEMO is 1): since each free-weight activity owns unique arm motions, the corre-sponding Doppler shift profile should be distinguishable to each other and serves as a reliable signature for each ac-tivity. 2): the Doppler profile of each activity has a strong spatial-temporal correlation that implicitly reflects the qual-ity of each performed activity. We implement FEMO with COTS RFID devices and conduct a two-week experimen-t. The preliminary result from 15 volunteers demonstrates that FEMO can be applied to a variety of free-weight activ-ities and users, and provide valuable feedbacks for activity alignment.
On Multipath Link Characterization and Adaptation for Device-free Human Detection
"... Abstract—Wireless-based device-free human sensing has raised increasing research interest and stimulated a range of novel location-based services and human-computer interaction appli-cations for recreation, asset security and elderly care. A primary functionality of these applications is to first de ..."
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Abstract—Wireless-based device-free human sensing has raised increasing research interest and stimulated a range of novel location-based services and human-computer interaction appli-cations for recreation, asset security and elderly care. A primary functionality of these applications is to first detect the presence of humans before extracting higher-level contexts such as physical coordinates, body gestures, or even daily activities. In the presence of dense multipath propagation, however, it is non-trivial to even reliably identify the presence of humans. The multipath effect can invalidate simplified propagation models and distort received signal signatures, thus deteriorating detection rates and shrinking detection range. In this paper, we characterize the impact of human presence on wireless signals via ray-bouncing models, and propose a measurable metric on commodity WiFi infrastructure as a proxy for detection sensitivity. To achieve higher detection rate and wider sensing coverage in multipath-dense indoor scenarios, we design a lightweight subcarrier and path configuration scheme harnessing frequency diversity and spatial diversity. We prototype our scheme with standard WiFi devices. Evaluations conducted in two typical office environments demonstrate a detection rate of 92.0 % with a false positive of 4.5%, and almost 1x gain in detection range given a minimal detection rate of 90%. I.
