Yishuang Geng

Modeling the Effect of Human Body on TOA Based Indoor Human Tracking

In time-of-arrival (TOA) based indoor human tracking system, the human body mounted with the target sensor can cause non-line of sight (NLOS) scenario and result in significant ranging error. However, the previous studies on the behavior of indoor TOA ranging did not take the effects of human body into account. In this paper, measurement of TOA ranging error has been conducted in a typical indoor environment and sources of inaccuracy in TOA-based indoor localization have been analyzed. To quantitatively describe the TOA ranging error caused by human body, we introduce a statistical TOA ranging error model for body mounted sensors based on the measurement results. This model separates the ranging error into multipath error and NLOS error caused by the creeping wave phenomenon. Both multipath error and NLOS error are modeled as a Gaussian variable. The distribution of multipath error is only relative to the bandwidth of the system while the distribution of NLOS error is relative to the angle between human facing direction and the direction of transmitter–receiver, signal to noise ratio and bandwidth of the system, which clearly shows the effects of human body on TOA ranging.

Motion detection using RF signals for the first responder in emergency operations: A PHASER project

The real-time health monitoring system is a promising body area network application to enhance the safety of fire fighters when they are working in harsh and dangerous environment. Except for monitoring the physiological status of the fire fighters, on-body monitoring network can be also regarded as a candidate solution of motion detection and classification. In this paper, a novel Support Vector Machine (SVM) classifier has been implemented using RF signals as classification features. The classifier is capable of detecting and classifying seven frequently appeared motions of fire fighters including standing, walking, running, lying, crawling, climbing and running up stairs. The average true classification rate of our classifier reaches 87.9175% and the effects of different human motions and sensor locations have been analyzed by plotting Receiver Operating Characteristics (ROC) curves.

A Cyber Physical Test-Bed for Virtualization of RF Access Environment for Body Sensor Network

Performance evaluation of wireless access and localization is important for body sensor networks, as any defects in the design not only cause wastage of resources, but also threaten an individuals health and safety. The typical cyber methods, however, such as software simulation, often fail to accurately simulate the influence of hardware implementation. The traditional physical methods, however, such as field testing, are not capable of creating repeatable and controllable channel conditions. To combine cyber and physical factors as well as to address the issue, we present a cyber physical test-bed for environment virtualization to facilitate the performance evaluation of wireless access and localization in body sensor networks. This test-bed creates a virtualized environment by emulating the wireless channel in a cybernetic way using a real time channel emulator. The original devices or systems under testing can be physically connected to a channel emulator to evaluate the performance in the virtualization environment. Furthermore, the cyber physical test-bed supports various scenarios from in-body data transmission to time of arrival based indoor localization. To validate the cyber physical approach, emulated outputs are compared with the empirical data obtained from actual measurements. To overcome the bandwidth limitation of traditional digital channel emulators, we have designed an analog channel emulator for UWB technologies. The preliminary verification of this analog emulator is introduced at the end of this paper.

Analysis of three-dimensional maximum likelihood algorithm for capsule endoscopy localization

Wireless capsule endoscopy (WCE) has become a good therapeutic method for a period of time. It helps detect, exam and heal gastro-intestinal (GI) related diseases. In the Capsule endoscopy application, knowledge of capsule position inside human body is rather important because it enables doctors locate the tumor of bleeding inside GI track and prepare for further therapeutic operations. However, due to the harsh environment for in-body wireless channel, in-body localization remains difficult and erroneous. In this paper, an improved three dimensional maximum likelihood algorithm has been introduced based on received signal strength (RSS) localization technology. Human body mesh and GI track mesh are built as the environment of algorithm simulation. Algorithm performance has been evaluated by comparison with the Cramer-Row Lower Bound (CRLB) and ranging error of the algorithm varies from 25mm to 140mm. By analyzing the results, we conclude that the three dimensional maximum likelihood is heavily impacted by the distance between implant and base station and its performance can be further improved.

On the accuracy of RF and image processing based hybrid localization for wireless capsule endoscopy

RF based and image processing based hybrid localization for wireless capsule endoscopy (WCE) has been adopted as one of the leading in-body localization approach for its overwhelming advantage on the accuracy of location estimation. In this paper, in order to investigate the fundamental limits, we derived the 3-dimensional (3D) Posterior Cramer-Rao Lower Bound (PCRLB) for hybrid WCE localization with the knowledge of RF based range estimation and image processing based step length and heading measurements. In addition to the PCRLB derivation, numerical results of this work shows that when the accuracy of image processing based measurement is high enough or the number of on-body RF receiver is large enough, the root-mean-square error (RMSE) of hybrid WCE localization can be limited within 3cm.

Toward Accurate Human Tracking: Modeling Time-of-Arrival for Wireless Wearable Sensors in Multipath Environment

For time-of-arrival-(TOA)-based indoor human tracking system, the wireless channel between human body surface and external reference node can be regarded as the source of inaccuracy. Since only the arrival time of direct path provides accurate range estimate, the nonline of sight caused by human body leads to undetectable direct path condition and thus results in a significant distance measurement error. In this paper, we measured TOA ranging error for indoor human tracking applications inside a typical office environment. A large number of TOA ranging samples was obtained and statistically analyzed. The TOA ranging error was modeled as a Gaussian random variable with the parameters, including position of target sensor, angle between human facing direction, and direction of transmitter-receiver, signal-to-noise ratio, and bandwidth of the system. As a validation of proposed model, excellent agreement has been found between empirical measurement and model-based software simulation.

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.

Modeling indoor TOA ranging error for body mounted sensors

In Time of arrival (TOA) based indoor human tracking system, the human body mounted with the target sensor can cause non-line of sight (NLOS) scenario and result in significant ranging error. However, the previous studies on the behavior of indoor TOA ranging did not take the effects of human body into account. In this paper, we introduce a statistical TOA ranging error model for body mounted sensors based on the measurements in a typical office building. This model separates the ranging error into multipath error cased multipath combination and undetectable direct path (UDP) error derives from the body-caused NLOS. Both multipath error and UDP error are modeled as a Gaussian variable. The distribution of multipath error is relative to the bandwidth of the system and the distribution of UDP error is relative to the angle between the face direction and the direction of TX-RX, SNR and bandwidth of the system, clearly shows the effects of human body on TOA ranging.

Polyp detection and radius measurement in small intestine using video capsule endoscopy

Video Capsule Endoscopy (VCE) was invented in the year 2000 and rapidly became one of the most popular noninvasive non-surgical inspection techniques in the diagnosis of gastrointestinal (GI) tract, especially in small intestine. A critical issue of capsule endoscopic examination is to determine the location and size of polyps. A critical problem associated with capsule endoscopic examination is to localize and correctly estimate the size of polyps for proper clinical treatment. In this paper we present a global statistical method which could automatically detect the polyps in VCE frames and determine their radii. The proposed method gathers the statistical information from available RGB channels. The statistical information is then fed to a support vector machine (SVM) to determine the existence and radii of polyps. The experimental result of this approach shows its improvement of accuracy when compared with other methods in the literature.

Effects of calibration RFID tags on performance of inertial navigation in indoor environment

The hybrid localization system applications nowadays not only mitigate the inaccuracy of standalone RF localization approach, but also increase the reliability in the absence of supporting Radio Frequency (RF) infrastructure. One of the outstanding hybrid approaches is the Radio Frequency Identification(RFID) assisted inertial navigation system, which is notable for its low cost, simple implementation and extraordinary accuracy. Previous work on such hybrid system fails to find out the correlation between the deployment of the multiple calibration points and the indoor localization accuracy. In this paper, we use the Android smart phone to build a hybrid localization platform and conduct measurements with multiple RFID calibration tags. Based on the measurement results, we define a mathematical model which includes the calibration point number, RFID tag density and the RFID tag-to-corner distance to describe the deployment effect on the localization accuracy. Such model facilitates the future study on algorithm design, system evaluation and application development.