The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

With the high demand for wireless data traffic, WiFi networks have experienced very rapid growth, because they provide high throughput and are easy to deploy. Recently, Channel State Information (CSI) measured by WiFi networks is widely used for different sensing purposes. To get a better understanding of existing WiFi sensing technologies and future WiFi sensing trends, this survey gives a comprehensive review of the signal processing techniques, algorithms, applications, and performance results of WiFi sensing with CSI. Different WiFi sensing algorithms and signal processing techniques have their own advantages and limitations and are suitable for different WiFi sensing applications. The survey groups CSI-based WiFi sensing applications into three categories, detection, recognition, and estimation, depending on whether the outputs are binary/multi-class classifications or numerical values. With the development and deployment of new WiFi technologies, there will be more WiFi sensing opportunities wherein the targets may go beyond from humans to environments, animals, and objects. The survey highlights three challenges for WiFi sensing: robustness and generalization, privacy and security, and coexistence of WiFi sensing and networking. Finally, the survey presents three future WiFi sensing trends, i.e., integrating cross-layer network information, multi-device cooperation, and fusion of different sensors, for enhancing existing WiFi sensing capabilities and enabling new WiFi sensing opportunities.

WiFi Sensing with Channel State Information: A Survey

Wi-Fi based sensing systems, although sound as being deployed almost everywhere there is Wi-Fi, are still practically difficult to be used without explicit adaptation efforts to new data domains. Various pioneering approaches have been proposed to resolve this contradiction by either translating features between domains or generating domain-independent features at a higher learning level. Still, extra training efforts are necessary in either data collection or model re-training when new data domains appear, limiting their practical usability. To advance cross-domain sensing and achieve fully zero-effort sensing, a domain-independent feature at the lower signal level acts as a key enabler. In this paper, we propose Widar3.0, a Wi-Fi based zero-effort cross-domain gesture recognition system. The key insight of Widar3.0 is to derive and estimate velocity profiles of gestures at the lower signal level, which represent unique kinetic characteristics of gestures and are irrespective of domains. On this basis, we develop a one-fits-all model that requires only one-time training but can adapt to different data domains. We implement this design and conduct comprehensive experiments. The evaluation results show that without re-training and across various domain factors (i.e. environments, locations and orientations of persons), Widar3.0 achieves 92.7% in-domain recognition accuracy and 82.6%-92.4% cross-domain recognition accuracy, outperforming the state-of-the-art solutions. To the best of our knowledge, Widar3.0 is the first zero-effort cross-domain gesture recognition work via Wi-Fi, a fundamental step towards ubiquitous sensing.

https://dl.acm.org/doi/pdf/10.1145/3307334.3326081

Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi

The past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away On the other hand, most home environments only have one WiFi access point and it may not be located in the same room as the target This sensing range constraint greatly limits the application of the proposed approaches in real life 
This paper presents FarSense--the first real-time system that can reliably monitor human respiration when the target is far away from the WiFi transceiver pair FarSense works well even when one of the transceivers is located in another room, moving a big step towards real-life deployment We propose two novel schemes to achieve this goal: (1) Instead of applying the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range; (2) The division operation further enables us to utilize the phase information which is not usable with one single antenna for sensing The orthogonal amplitude and phase are elaborately combined to address the "blind spots" issue and further increase the sensing range Extensive experiments show that FarSense is able to accurately monitor human respiration even when the target is 8 meters away from the transceiver pair, increasing the sensing range by more than 100% We believe this is the first system to enable through-wall respiration sensing with commodity WiFi devices and the proposed method could also benefit other sensing applications

/pdf/farsense-pushing-the-range-limit-of-wifi-based-respiration-14ku2nta1f.pdf

FarSense: Pushing the Range Limit of WiFi-based Respiration Sensing with CSI Ratio of Two Antennas

Non-intrusive respiration sensing without any device attached to the target plays a particular important role in our everyday lives. However, existing solutions either require dedicated hardware or employ special-purpose signals which are not cost-effective, significantly limiting their real-life applications. Also very few work concerns about the theory behind and can explain the large performance variations in different scenarios. In this paper, we employ the cheap commodity Wi-Fi hardware already ubiquitously deployed around us for respiration sensing. For the first time, we utilize the Fresnel diffraction model to accurately quantify the relationship between the diffraction gain and human target's subtle chest displacement and thus successfully turn the previously considered "destructive" obstruction diffraction in the First Fresnel Zone (FFZ) into beneficial sensing capability. By not just considering the chest displacement at the frontside as the existing solutions, but also the subtle displacement at the backside, we achieve surprisingly matching results with respect to the theoretical plots and become the first to clearly explain the theory behind the performance distinction between lying and sitting for respiration sensing. With two cheap commodity Wi-Fi cards each equipped with just one antenna, we are able to achieve higher than 98% accuracy of respiration rate monitoring at more than 60% of the locations in the FFZ. Furthermore, we are able to present the detail heatmap of the sensing capability at each location inside the FFZ to guide the respiration sensing so users clearly know where are the good positions for respiration monitoring and if located at a bad position, how to move just slightly to reach a good position.

From Fresnel Diffraction Model to Fine-grained Human Respiration Sensing with Commodity Wi-Fi Devices

In recent years, wireless sensing has been exploited as a promising research direction for contactless human activity recognition. However, one major issue hindering the real deployment of these systems is that the signal variation patterns induced by the human activities with different devices and environmental settings are neither stable nor consistent, resulting in unstable system performance. The existing machine learning based methods usually take the "black box" approach and fails to achieve consistent performance. In this paper, we argue that a deep understanding of radio signal propagation in wireless sensing is needed, and it may be possible to develop a deterministic sensing model to make the signal variation patterns predictable. With this intuition, in this paper we investigate: 1) how wireless signals are affected by human activities taking transceiver location and environment settings into consideration; 2) a new deterministic sensing approach to model the received signal variation patterns for different human activities; 3) a proof-of-concept prototype to demonstrate our approach and a case study to detect diverse activities. In particular, we propose a diffraction-based sensing model to quantitatively determine the signal change with respect to a target's motions, which eventually links signal variation patterns with motions, and hence can be used to recognize human activities. Through our case study, we demonstrate that the diffraction-based sensing model is effective and robust in recognizing exercises and daily activities. In addition, we demonstrate that the proposed model improves the recognition accuracy of existing machine learning systems by above 10%.

Towards a Diffraction-based Sensing Approach on Human Activity Recognition

Wireless signals have been extensively utilized for contactless sensing in the past few years. Due to the intrinsic nature of employing the weak target-reflected signal for sensing, the sensing range is limited. For instance, WiFi and RFID can achieve 3-6 meter sensing range while acoustic-based sensing is limited to less than one meter. In this work, we identify exciting sensing opportunities with LoRa, which is the new long-range communication technology designed for IoT communication. We explore the sensing capability of LoRa, both theoretically and experimentally. We develop the sensing model to characterize the relationship between target movement and signal variation, and propose novel techniques to increase LoRa sensing range to over 25 meters for human respiration sensing. We further build a prototype system which is capable of sensing both coarse-grained and fine-grained human activities. Experimental results show that (1) human respiration can still be sensed when the target is 25 meters away from the LoRa devices, and 15 meters away with a wall in between; and (2) human walking (both displacement and direction) can be tracked accurately even when the target is 30 meters away from the LoRa transceiver pair.

Exploring LoRa for Long-range Through-wall Sensing

With a big success in data communication, wireless signals are now exploited for fine-grained contactless activity sensing including human respiration monitoring, finger gesture recognition, subtle chin movement tracking when speaking, etc. Different from coarsegrained body and limb movements, these fine-grained movements are in the scale of millimetres and are thus difficult to be sensed. While good sensing performance can be achieved at one location, the performance degrades dramatically at a very nearby location. In this paper, by revealing the effect of static multipaths in sensing, we propose a novel method to add man-made "virtual" multipath to significantly improve the sensing performance. With carefully designed "virtual" multipath, we are able to boost the sensing performance at each location purely in software without any extra hardware.We demonstrate the effectiveness of the proposed method on three fine-grained sensing applications with just one Wi-Fi transceiver-pair, each equipped with a single antenna. For respiration monitoring, we can remove the "blind spots" and achieve full coverage respiration sensing. For finger gesture recognition, our system can significantly increase the recognition accuracy from 33% to 81%. For chin movement tracking, we are able to count the number of spoken syllables in a sentence at an accuracy of 92.8%.

Boosting fine-grained activity sensing by embracing wireless multipath effects

Recent years have witnessed advances of Internet of Things (IoT) technologies and their applications to enable contactless sensing and human–computer interaction in smart homes. For people with motor neurone disease (MND), their motion capabilities are severely impaired and they have difficulties interacting with IoT devices and even communicating with other people. As the disease progresses, most patients lose their speech function eventually which makes the widely adopted voice-based solutions fail. In contrast, most of the patients can still move their fingers slightly even after they have lost the control of their arms and hands. Thus, we propose to develop a Morse code-based text input system, called WiMorse , which allows patients with minimal single-finger control to input and communicate with other people without attaching any sensor to their fingers. WiMorse leverages ubiquitous commodity WiFi devices to track subtle finger movements contactlessly and encode them as Morse code input. In order to sense the very subtle finger movements, we propose to employ the ratio of the channel state information (CSI) between two antennas to enhance the signal to noise ratio. To address the severe location dependency issue in wireless sensing with accurate theoretical underpinning and experiments, we propose a signal transformation mechanism to automatically convert signals based on the input position, achieving stable sensing performance. Comprehensive experiments demonstrate that WiMorse can achieve higher than 95% recognition accuracy for finger generated Morse code, and is robust against input position, environment changes, and user diversity.

/pdf/wimorse-a-contactless-morse-code-text-input-system-using-4du1rd7xvm.pdf

Kai Niu

Papers

From Fresnel Diffraction Model to Fine-grained Human Respiration Sensing with Commodity Wi-Fi Devices

Towards a Diffraction-based Sensing Approach on Human Activity Recognition

Exploring LoRa for Long-range Through-wall Sensing

Boosting fine-grained activity sensing by embracing wireless multipath effects

WiMorse: A Contactless Morse Code Text Input System Using Ambient WiFi Signals