Ruijun Fu

Enlighten Wearable Physiological Monitoring Systems: On-Body RF Characteristics Based Human Motion Classification Using a Support Vector Machine

The real-time health monitoring system is a promising body area network application to enhance the safety of firefighters when they are working in harsh and dangerous environments. Other than monitoring the physiological status of the firefighters, on-body monitoring networks can be also regarded as a candidate solution of motion detection and classification. In this paper, we consider motion classification with features obtained from the on-body radio frequency (RF) channel. Various relevant RF features have been identified and a support vector machine (SVM) has been implemented to facilitate human motion classification. In particular, we distinguish the most frequently appearing human motions of firefighters including standing, walking, running, lying, crawling, climbing, and running upstairs with an average true classification rate of 88.69 percent. Classification performance has been analyzed from three different perspectives including typical classification results, effects of candidate human motions, and effects of on-body sensor locations. We prove that even a subset of available RF features provides an acceptable classification rate, which may result in less computational cost and easier implementation by using our proposed scheme.

Performance bounds for RF positioning of endoscopy camera capsules

In this paper, we evaluate the factors affecting the accuracy achievable in localization of an endoscopic wireless capsule as it passes through the digestive system of the human body. Using a three-dimension full electromagnetic wave simulation model, we obtain bounds on the capsule-location estimation errors when the capsule is in each of three individual organs: stomach, small intestine and large intestine. The simulations assume two different external sensor arrays topologies. We compare these performance bounds and draw the conclusion that location-estimation errors are different for different organs and for various topologies of the external sensor arrays.

On the accuracy of RF positioning in multi-Capsule endoscopy

In this paper, we derive and analyze cooperative localization bounds for endoscopic wireless capsule as it passes through the human gastrointestin (GI) tract. We derive the Cramer-Rao lower bound (CRLB) variance limits on location estimators which use measured received signal strength(RSS). Using a three-dimension human body model from a full wave simulation software and log-normal models for RSS propagation from implant organs to body surface, we calculate bounds on location estimators in three digestive organs: stomach, small intestine and large intestine. We provide analysis of the factors affecting localization accuracy including various organ environments, external sensor array topology and number of pills in cooperation. The simulation results show that the number of receiver sensors on body surface has more influence on the accuracy of localization than the number of pills in cooperation inside the GI tract.

Challenges in Channel Measurement and Modeling for RF Localization Inside the Human Body

In this invited paper, the authors introduce an overview of the fundamentals of radio frequency RF channel measurement and modeling techniques needed for localization inside the human body. To address these fundamentals, the authors use capsule endoscopy as an example application. The authors first provide the results of the Cramer Rao Lower Bound CRLB for received signal strength RSS based endoscopy capsule localization, inside the human body, using existing path-loss models for radio propagation. Then challenges demanding further research are highlighted for attaining more precise localization using the time-of-arrival TOA based ranging techniques.

Doppler spread analysis of human motions for Body Area Network applications

Many current and future medical devices are wearable and the human body is used as a carrier for wireless communication, which implies that the human body is a crucial part of the transmission medium in Body Area Networks (BANs). In order to understand the propagation characteristics of the human body, it is imperative to analyze the Doppler spread spectrum, which is caused by human body motions. Using a network analyzer, Doppler spreads and coherence time of temporal variations caused by human body motions can be measured and analyzed using a single tone waveform for different scenarios in a shielded room. From the narrowband measurement results, the Doppler spread varies approximately from 0.6Hz to 12Hz for different scenarios, the RMS Doppler bandwidth is in the range from 0.6Hz to 4Hz, and the coherence time differs from 20ms to 1s, all of which are measured at the Medical Implant Communication Service (MICS) band, the Industrial Scientific Medical (ISM) band and the Ultra-Wideband (UWB) band. Root mean square fittings of three different functions to received signal strength measurements were performed for different scenarios. Results show that the Gaussian function generally provides a good fitting model, which is independent of center frequencies.

The Performance of Simulated Annealing Algorithms for Wi-Fi Localization Using Google Indoor Map

Wi-Fi localization is currently the most promising approach to build indoor localization systems. Especially after the recent release of Google Indoor Map for the Android system smart phones, many companies such like Skyhook and TCS etc. have flourished into the business of developing accurate Wi-Fi based indoor localization techniques due to their numerous applications. In this paper, we proposed an accurate Wi-Fi based indoor localization algorithm with the help of Google Indoor Map. The initial position of the mobile station (MS) is estimated according to the received signal strength (RSS) from the calibrated Wi-Fi access points (APs). Then, the position of the MS is allocated by using the simulated annealing (SA) algorithm to search for a better solution. During the searching process, the SA takes the indoor map structure into consideration and updates the cost function weights accordingly. This procedure makes sure the final estimation reaches to a better convergence. Extensive experimental results confirm that our solution is able to provide a much better result compared with other existing indoor localization techniques.

Characteristic and Modeling of Human Body Motions for Body Area Network Applications

Many current and future medical devices are wearable and human body is used as a carrier for wireless communication, which implies human body to be a crucial part of the transmission medium in body area networks (BANs). In order to understand the propagation characteristics around human body, the statistical model is derived for communication links in the medical implant communication service band, industrial scientific medical band and ultra-wideband based on the narrowband measurement. The channel model of diffracting components around human body were different from one scenario to another. Moreover, second order statistics, including level crossing rate and fade duration, are presented for each scenario to evaluate the link quality and outage performance for on-body to on-body scenario. Using a network analyzer, Doppler spread spectrum in frequency domain and coherence time in time domain from temporal variations of human body movements are also analyzed from diverse perspectives. Additionally, the shape of Doppler spread spectrum is fitted to describe the relationship of power and frequency. The proposed on-body to on-body channel model for human body motions can be used to better design wireless network protocols for BANs.

RF localization inside human body: Enabling micro-robotic navigation for medical applications

In this paper we introduce issues relevant to understanding of the human body as a medium for radio frequency (RF) navigation of smart robots travelling inside the body for wireless medical applications. We provide the results of Cramer Rao Lower Bound (CRLB) for in-body localization using received signal strength (RSS) and we highlight challenges demanding further research for attaining more precise localization inside body using time-of-arrival (TOA) techniques.

Heterogeneous cooperative localization for social networks with mobile devices

Location-aware techniques, which combine multiple sensors in the smart-phone, have been researched and developed to estimate accurate locations of the mobile users in the social networks. In cooperative localization, each mobile user with the WiFi and GPS sensors works in a peer-to-peer, independent and assistant mode. This paper provides a comparison of four probabilistic cooperative localization algorithms for smart-phone applications: Centroid method, Nearest Neighbor method, Kernel method and AP density method. The location of the unknown mobile user is estimated based on Receive Signal Strength (RSS) from the shared APs and GPS locations from reference nodes. An empirical evaluation of the system is given to demonstrate the feasibility of these algorithms by reporting the results in a real-world environment. And a Monte Carlo simulation is also carried out to evaluate the performance of the cooperative algorithms for social networks.

Heterogeneous Cooperative Localization for Social Networks

AbstractLocation-aware techniques has become a hot research topic with great value in commercial and military applications. Cooperative localization, which utilizes multiple sensors in portable devices to estimate locations of the mobile users in the social networks, is one of the most promising solution for the indoor geo-location. Traditional cooperative localization methods are based on ranging techniques, they are highly dependent on the distance interpreted from the received signal strength (RSS) or time of arrival from anchors. However, a precise ranging procedure demands high performance hardware which would increase the cost to the current mobile platform. In this paper, we describes four ranging-free probabilistic cooperative localization algorithms: centroid scheme, nearest neighbor scheme, kernel scheme and AP density scheme to improve the accuracy for the indoor geo-location using current mobile devices. Since the GPS sensor embedded in the smart phone is able to provide accurate location information in the outdoor area, those mobile nodes can be used as calibrated anchors. The position of the indoor mobile node can be estimated by exchanging locations and RSSs from shared wireless access points information between the target node and anchor nodes. An empirical evaluation of the system is given to demonstrate the feasibility of these cooperative localization algorithms by reporting the results in a real-world environments, e.g. suburban area and city downtown. Moreover, we compared our results with the WiFi positioning system made by Skyhook Wireless to validate the accuracy of the proposed algorithms. Meanwhile, a Monte Carlo simulation is carried out to evaluate the performance of the cooperative algorithms under different scenarios. Results show that given the same scenario setting, the AP density scheme and kernel scheme outperform than other schemes.