Digital processing of speech signals

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

Intelligent occupancy sensing is becoming a vital underpinning for various emerging applications in smart homes, such as security surveillance and human behavior analysis. However, prevailing approaches mainly rely on video camera, ambient sensors, or wearable devices, which either requires arduous deployment or arouses privacy concerns. In this paper, we present a novel real-time, device-free, and privacy-preserving WiFi-enabled Internet of Things platform for occupancy sensing, which can promote a myriad of emerging applications. It is designed to achieve an optimal tradeoff between performance and scalability. Our system empowers commercial off-the-shelf WiFi routers to collect channel state information (CSI) measurements and provides an efficient cloud server for computing via a lightweight communication protocol. To demonstrate the usefulness of our platform, an occupancy detection system is developed by exploiting the CSI curve of human presence. Furthermore, we also design an innovative activity recognition system based on our platform and machine learning techniques with high availability and extensibility. In the evaluation, the experimental results show that our platform enables these applications efficiently, with the accuracy of 96.8% and 90.6% in terms of occupancy detection and recognition, respectively.

Device-Free Occupant Activity Sensing Using WiFi-Enabled IoT Devices for Smart Homes

A review and categorization of techniques on device-free human activity recognition

With the proliferation of mobile devices, indoor fingerprinting-based localization has caught considerable interest on account of its high precision. Meanwhile, channel state information (CSI), as a promising positioning characteristic, has been gradually adopted as an enhanced channel metric in indoor positioning schemes. In this paper, we propose a CSI amplitude fingerprinting-based localization algorithm in Narrowband Internet of Things system, in which we optimize a centroid algorithm based on CSI propagation model. In particular, in the fingerprint matching, we utilize the method of multidimensional scaling (MDS) analysis to calculate the Euclidean distance and time-reversal resonating strength between the target point and the reference points and then employ the ${K}$ -nearest neighbor (KNN) algorithm for location estimation. By conjugate gradient method, moreover, we optimize the localization error of triangular centroid algorithm and combine the positioning result with MDS and KNN’s estimated position to get the final estimated position. Experiment results show that compared to some existing localization methods, our proposed algorithm can effectively reduce positioning error.

CSI Amplitude Fingerprinting-Based NB-IoT Indoor Localization

In this paper, we explore the properties of the Channel State Information (CSI) of WiFi signals and present a device-free indoor activity recognition system Our proposed system uses only one ubiquitous router access point and a laptop as a detection point, while the user is free and neither needs to wear sensors nor carry devices The proposed system recognizes six daily activities, such as walk, crawl, fall, stand, sit, and lie We have built the prototype with an effective feature extraction method and a fast classification algorithm The proposed system has been evaluated in a real and complex environment in both line-of-sight (LOS) and none-line-of-sight (NLOS) scenarios, and the results validate the performance of the proposed system

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Device-Free Indoor Activity Recognition System

/pdf/device-free-home-intruder-detection-and-alarm-system-using-4ntjtvjj3r.pdf

Device-Free Home Intruder Detection and Alarm System Using Wi-Fi Channel State Information

Most location-based services are based on a global positioning system (GPS), which only works well in outdoor environments Compared to outdoor environments, indoor localization has created more buzz in recent years as people spent most of their time indoors working at offices and shopping at malls, etc Existing solutions mainly rely on inertial sensors (ie, accelerometer and gyroscope) embedded in mobile devices, which are usually not accurate enough to be useful due to the mobile devices’ random movements while people are walking In this paper, we propose the use of shoe sensing (ie, sensors attached to shoes) to achieve 3D indoor positioning Specifically, a short-time energy-based approach is used to extract the gait pattern Moreover, in order to improve the accuracy of vertical distance estimation while the person is climbing upstairs, a state classification is designed to distinguish the walking status including plane motion (ie, normal walking and jogging horizontally), walking upstairs, and walking downstairs Furthermore, we also provide a mechanism to reduce the vertical distance accumulation error Experimental results show that we can achieve nearly 100% accuracy when extracting gait patterns from walking/jogging with a low-cost shoe sensor, and can also achieve 3D indoor real-time positioning with high accuracy

3D Tracking via Shoe Sensing.

In this Demo, we present the design and implementation of a novel human localization and tracking system based on pyroelectric infrared(PIR) sensors. It is a new approach to setup several low-cost sensors to improve the simplicity of deployment. We first present a novel design in mechanical architecture, circuit board and sensors which enables our PIR sensor based system to detect the moving objects and track human beings in real time. Different from the previous work which only adopts binary output, we further explore the usage of analog signals from the sensors. This improvement is beneficial to accurate estimation of the distance between the device and human body. We have implemented several prototypes for evaluation and demonstration. According to the experimental results, our system can achieve an accuracy of 0.113m in an area of 12m * 6m.

Demonstration Abstract: A Novel Human Tracking and Localization System Based on Pyroelectric Infrared Sensors

With the proliferation of context-aware services and human-computer interaction scenarios, the novel techniques of motion detection have attracted growing interests. There are many products which use camera, infrared sensors, RF, etc. With more Wi-Fi devices in homes and the ability to measure the PHY layer infomation, Wi-Fi based motion detection has attracted more attention than before. In this poster, we present the design of RoMD, a robust motion detection system which uses existing Wi-Fi devices. Changing environment and human movement would cause significant pattern changes of the CSI amplitude over time. The basic idea of RoMD is to eliminate the “bad antennas” and interference of CSI measurements so that the features we extract would not be affected by changing environments. To achieve this goal, we implement an efficient data processing structure to accurately extract the feature and identify the human motion. Then, we simplely evaluate the scheme and demonstrate feasibility. And finally, we introduce the work in the future.

Guo Liu

Papers

Device-Free Indoor Activity Recognition System

Device-Free Home Intruder Detection and Alarm System Using Wi-Fi Channel State Information

3D Tracking via Shoe Sensing.

Demonstration Abstract: A Novel Human Tracking and Localization System Based on Pyroelectric Infrared Sensors

RoMD: Robust device-free motion detection usin PHY layer information