A Novel Wireless Three-pad ECG System for Generating Conventional 12-lead Signals by Huasong Cao B.Eng., Wuhan University, 2007 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in The Faculty of Graduate Studies (Electrical and Computer Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2010 c© Huasong Cao 2010 Abstract A wireless body area network (WBAN) is a radio-frequency (RF) based wireless networking tech- nology that interconnects tiny nodes in, on or around a human body. Typically, the transmissions of these nodes cover a short range of about 2 meters. This thesis presents a complete survey on recent advances in WBAN, including the market needs, channel modelling, standardization of low-layer communication protocols, quality-of-service (QoS) provisions, developments of sensors/actuators, WBAN architectures and experimental platforms. A recent work employing the nonbeacon-enabled mode of the IEEE 802.15.4 standard for QoS provisions has motivated us to design a QoS framework based on the beacon-enabled mode of the same standard. The proposed QoS framework can better differentiate WBAN application traffic streams and serve periodic traffic more directly through the time-division-multiple-access (TDMA) based mechanism. A dominant feature of the proposed framework is the minimum adaptation to the existing standard, which makes it easy to adopt our platform and associated algorithms, as well as to implement them on off-the-shelf hardware platforms. Employing the proposed QoS framework, we propose a novel wireless three-pad electrocardiog- raphy (W3ECG) system. W3ECG furthers the pad design idea of single-pad wireless ECG systems. Inspired by the transformation possibility of signals obtained in vectorcardiographic (VCG) sys- tems, we bring two more pads to the single-pad approach to gain spatial variety of the heart activity. Signals obtained from these three pads, plus the spatial information, make it possible to synthesize conventional 12-lead ECG signals. We have been able to manufacture the front-end ECG circuit, and combine it with an IEEE 802.15.4 hardware platform TelosB. Software for the server and pad has also been developed to make a fully running W3ECG possible. By explaining and evaluating our QoS platform designed for general WBAN applications, and our W3ECG system invented for particular healthcare area, we foresee a bright future for wide deployments of such kind of wireless networks on the human body. ii Table of Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Wireless Networks Reaching Body Area . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 WBANs versus Wider Area Wireless Networks . . . . . . . . . . . . . . . . . 1 1.1.2 WBANs versus WBASNs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.3 WBANs versus WSNs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Huge Needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2.1 Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.2 Sports and Fitness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.3 Secure Authentication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Technology Advances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3.1 Body Area Channel Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3.2 Low-power Radio Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3.3 QoS Provisions in WBAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.3.4 Sensors and Actuators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.4 Wired and Wireless ECG Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.4.1 Conventional 12-lead ECG Systems . . . . . . . . . . . . . . . . . . . . . . . 14 1.4.2 Body Surface Mapping Systems . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.4.3 Vectorcardiographic Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.4.4 Wireless Single-pad ECG Systems . . . . . . . . . . . . . . . . . . . . . . . . 15 1.5 Interconnections of WBANs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.5.1 One-tier Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.5.2 Two-tier Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.6 Existing WBAN Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 iii Table of Contents 1.6.1 MITHril . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.6.2 CodeBlue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.6.3 AID-N . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.6.4 WHMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.6.5 MIMOSA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.6.6 WiMoCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.7 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.7.1 A Survey and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.7.2 A QoS Provisioning Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.7.3 A Novel Wireless ECG System . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2 Employing IEEE 802.15.4 for QoS Provisioning in WBAN . . . . . . . . . . . . 21 2.1 Topology Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.2 Service Differentiation Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2.1 QoS Vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2.2 Traffic Prioritization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2.3 Traffic Accommodation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.3 Admissions Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.3.1 Association and Admission . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.3.2 The Admission Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.3.3 Two-step Decision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.4 Traffic Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.1 The Superframe Scheduler . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.2 The CFP Scheduler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3 Proposed Wireless Three-pad ECG System . . . . . . . . . . . . . . . . . . . . . . 33 3.1 Electrode, Lead and Pad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.2 Heart Dipole Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.2.1 Heart-vector Projection Theory . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.2.2 Analyzing Input Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.2.3 Derivation of Transformation Equations . . . . . . . . . . . . . . . . . . . . . 36 3.3 Wireless Three-pad ECG System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.3.1 Architecture Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.3.2 Pad Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.3.3 Pad Placement Positions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.4 Software Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.4.1 Sampling and Radio Communications on The Pad . . . . . . . . . . . . . . . 43 3.4.2 Beacon-frame Generation and Data Forwarding on The Sink . . . . . . . . . 44 3.4.3 Graphical User Interface and MySQL Database on The Server . . . . . . . . 44 iv Table of Contents 4 Performance Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.1 Performance Evaluations of ZigBee for WBASN . . . . . . . . . . . . . . . . . . . . 51 4.1.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.1.2 Average Reception Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.1.3 Throughput . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.1.4 Latency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.1.5 Fairness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.1.6 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.2 Performance Evaluations of Proposed QoS Provisioning Platform . . . . . . . . . . 59 4.2.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.2.2 Number of Admitted Traffic Flows . . . . . . . . . . . . . . . . . . . . . . . . 60 4.2.3 Throughput . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.2.4 Energy Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 4.2.5 Constraint Compliance Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.2.6 Latency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3 Performance Evaluations of The W3ECG System . . . . . . . . . . . . . . . . . . . 63 4.3.1 Experimental Study Using Commercial Patient Monitor . . . . . . . . . . . . 64 4.3.2 Computer Simulations Based on Practical EASI Data Set . . . . . . . . . . . 66 4.3.3 Deployments of W3ECG and Experimental Studies . . . . . . . . . . . . . . 70 4.3.4 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 Appendices A Statement of Co-Authorship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 v List of Tables 1.1 WBAN and WPAN technologies and comparisons. . . . . . . . . . . . . . . . . . . . 8 2.1 Traffic priorities and 2-bit representations. . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2 Capability Information field of Association Request command. . . . . . . . . . . . . 24 2.3 IEEE 802.15.4 MAC command frames. . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.4 QoS notification command format. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.5 Valid values of the Association Status field. . . . . . . . . . . . . . . . . . . . . . . . 28 3.1 Suggested W3ECG pad placement locations. . . . . . . . . . . . . . . . . . . . . . . 43 4.1 ZigBee simulation parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.2 ZigBee application traffic parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.3 QoS application traffic parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.4 Cross-correlation coefficients between each of the three signals obtained from W3ECG. 72 4.5 Placement of electrodes for standard 12 leads. . . . . . . . . . . . . . . . . . . . . . . 73 vi List of Figures 1.1 WBAN in the family of wireless networks. . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Superframe structure of IEEE 802.15.4 beacon-enabled mode. . . . . . . . . . . . . . 9 1.3 Dalhousie’s two dimensional torso model with 352 nodes [18]. . . . . . . . . . . . . . 13 1.4 One-tier system architecture for interconnecting WBANs. . . . . . . . . . . . . . . . 16 1.5 Two-tier system architecture for interconnecting WBANs. . . . . . . . . . . . . . . . 17 2.1 Example of using star topology to directly link WBAN sensors in an IEEE 802.15.4 network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2 A successful admission and scheduling process in the proposed QoS platform. . . . . 32 3.1 Illustration of the heart vector and its projection on a lead vector. . . . . . . . . . . 35 3.2 Illustration of the heart vector and three lead vectors. . . . . . . . . . . . . . . . . . 37 3.3 Schematic of ECG front-end design. . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.4 PCB layout of ECG front-end circuit. . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.5 Snapshot of the manufactured ECG front-end circuits. . . . . . . . . . . . . . . . . . 47 3.6 ECG front-end circuit interfaced with TelosB platform. . . . . . . . . . . . . . . . . . 48 3.7 Placement positions of electrodes for RA, LA and LL, and three sets of electrodes for W3ECG system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.8 A snapshot of the W3ECG server GUI demonstrating that three pads are synchro- nized and transmitting ECG signals. . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.1 Average reception ratio for WBASNs with ExG sensors. . . . . . . . . . . . . . . . . 53 4.3 Throughput for WBASNs with ExG sensors. . . . . . . . . . . . . . . . . . . . . . . 53 4.2 Average reception ratio for WBASNs without ExG sensorss. . . . . . . . . . . . . . . 54 4.4 Throughput for WBASNs without ExG sensors. . . . . . . . . . . . . . . . . . . . . . 54 4.5 Delay for WBASNs with ExG sensors. . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.6 Delay for WBASNs without ExG sensors. . . . . . . . . . . . . . . . . . . . . . . . . 55 4.7 Average reception ratio for sensors of WBASNs positioned two hops away. . . . . . . 56 4.8 Average reception ratio for sensors of WBASNs positioned different numbers of hops away. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.9 Throughput for sensors of WBASNs positioned two hops away. . . . . . . . . . . . . 57 4.10 Throughput for sensors of WBASNs positioned different numbers of hops away. . . . 58 4.11 Number of admitted traffic flows in QoS framework simulation. . . . . . . . . . . . . 60 4.12 Throughput in QoS framework simulation. . . . . . . . . . . . . . . . . . . . . . . . . 61 vii List of Figures 4.13 Energy consumption in QoS framework simulation. . . . . . . . . . . . . . . . . . . . 62 4.14 Constraint compliance ratio in QoS framework simulation. . . . . . . . . . . . . . . . 63 4.15 Latency in QoS framework simulation. . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.16 ECG signals of Lead I, II and III from patient monitor. . . . . . . . . . . . . . . . . 65 4.17 ECG signals of Pad 1, Pad 2 and Pad 3 locations from patient monitor. . . . . . . . 65 4.18 Comparison of cross-correlation coefficients between W3ECG system and EASI sys- tem for Patient 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.19 Comparison of cross-correlation coefficients between W3ECG system and EASI sys- tem for Patient 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.20 Comparison of cross-correlation coefficients between W3ECG system and EASI sys- tem for Patient 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.21 Comparison of cross-correlation coefficients between W3ECG system and EASI sys- tem for Patient 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.22 Comparison between synthesized 12-lead ECG signals and directly obtained versions. 70 4.23 Time domain of ECG graph corrupted by 50/60 Hz noise. . . . . . . . . . . . . . . . 71 4.24 Frequency domain of ECG graph corrupted by 50/60 Hz noise. . . . . . . . . . . . . 72 4.25 Coefficients for cross-correlation between observed and synthesized 12-lead signals. . 74 4.26 Comparison between observed and synthesized Lead I signals. . . . . . . . . . . . . . 75 4.27 Comparison between observed and synthesized Lead II signals. . . . . . . . . . . . . 75 4.28 Comparison between observed and synthesized Lead III signals. . . . . . . . . . . . . 76 4.29 Comparison between observed and synthesized Lead aVR signals. . . . . . . . . . . . 76 4.30 Comparison between observed and synthesized Lead aVL signals. . . . . . . . . . . . 77 4.31 Comparison between observed and synthesized Lead aVF signals. . . . . . . . . . . . 77 4.32 Comparison between observed and synthesized Lead V1 signals. . . . . . . . . . . . . 78 4.33 Comparison between observed and synthesized Lead V2 signals. . . . . . . . . . . . . 78 4.34 Comparison between observed and synthesized Lead V3 signals. . . . . . . . . . . . . 79 4.35 Comparison between observed and synthesized Lead V4 signals. . . . . . . . . . . . . 79 4.36 Comparison between observed and synthesized Lead V5 signals. . . . . . . . . . . . . 80 4.37 Comparison between observed and synthesized Lead V6 signals. . . . . . . . . . . . . 80 viii Acknowledgements My deepest gratitude goes to my supervisors, Dr. Victor C. M. Leung and Dr. Leo Stocco. Without their guidance and support, this work would not have been possible. My special thanks go to Dr. Milan Horácek from the Dalhousie University and Dr. Ilangko Balasingham from Oslo University Hospital, Rikshospitalet. Dr. Horácek kindly provided us clinical ECG data and Dr. Balasingham generously offered me access to the hospital facilities. I would also like to thank Haoming Li for insightful discussions and inspirations, Dr. Min Chen, Dr. Sergio González-Valenzuela, Dr. Xuedong Liang, Dr. Henry Chan and Cupid Chow for kind advice and wonderful collaborations. ix Dedication To my parents, grandparents and family. x Chapter 1 Introduction 1.1 Wireless Networks Reaching Body Area With growing needs in ubiquitous and human-centric communications and recent advances in very- low-power wireless technologies, there has been considerable interest in the development and ap- plication of wireless networks around humans. A wireless body area network (WBAN) is a radio- frequency (RF) based wireless networking technology that interconnects tiny nodes in, on or around a human body [25][4][51]. Typically, the transmissions of these nodes cover a short range of about 2 meters. WBANs target diverse applications including healthcare, sports and fitness, secure au- thentication, and safe-guarding of uniformed personnel. Before the advent of WBANs, there are wireless networks serving wider areas (wider than 2 meters). They are wireless metropolitan area networks (WMAN), wireless local area networks (WLAN) and wireless personal area networks (WPAN) [40]. As we can tell from the names, these wireless networks differ from each other mainly in communication ranges. They also have distinguishments in terms of data rate, latency and energy consumption. Following, we explain these design tradeoffs from the perspectives of different layers. 1.1.1 WBANs versus Wider Area Wireless Networks The design of a WBAN, from the physical layer’s perspective, is a tradeoff between coverage, data rate and power consumption. Compared to the wider area wireless networks, decreasing the coverage to 2 meters and limiting data rate to 1 Mbps, the current draw of a typical low-power radio will be around 10 milliampere. The coverage of WBAN (2 meters) is determined by its definition, the data rate is determined by targeting applications, and the power consumption is a result of the above two plus surrounding environment and the wearer’s action. Moving to middle layers, the tradeoff transforms to be between reliability, latency and energy consumption. Compared to wider area wireless networks, WBANs have much less energy con- sumption, equivalently longer lifetime, by having very low duty cycle and simplified protocol stack. Obviously, the quality-of-service (QoS) requirements (reliability and latency) originate from appli- cations, and the energy consumption reflects the duty cycle of transmissions and overall protocol complexity. When comparing the differences of WBANs and wider area networks at the top layer, WBAN applications (e.g., healthcare, sports and fitness, and secure authentication) focus more on inter- actions between electronic devices and the human. A WBAN system takes both physiological activities and human actions as inputs to process and communicate. This makes the signal pro- 1 1.1. Wireless Networks Reaching Body Area cessing more complicated than that of audio/video applications. However, it offers a unique way of serving people. Figure 1.1 gives an view of typical wireless networks and their applications. ff fiflffi !#"%$'& !#()$'& fi* + fi*, - !#./0$1& 243 !#56$1& 243 798 24:<; 8 :42 Figure 1.1: WBAN in the family of wireless networks. 1.1.2 WBANs versus WBASNs A wireless body area sensor network (WBASN) is a WBAN that emphasizes the sensor/actuator functionalities of nodes in the network. In a WBAN, nodes can be classified without exception into two groups: sensors and actuators, both of which are equipped with the low-power radio. Typical sensors in WBAN include those for monitoring physiological activities (e.g., blood pressure, respi- ration, electrocardiography (ECG), electroencephalography (EEG) and electromyography (EMG)) and those for registering human actions (e.g., accelerometers, gyroscopes, audio/video sensor and remote controller); typical actuators in WBAN are as various as those for outputting computer pro- cessed data (e.g., speaker and display), those for assisting organ functionalities (e.g., pace maker and drug delivery for patient with diabetes) and those for facilitating people with disabilities (e.g., robotic arm/leg/wheelchair). In this thesis, we use WBASN to refer to healthcare related scenarios, and WBAN for all kinds of applications. 2 1.2. Huge Needs 1.1.3 WBANs versus WSNs There have been extensive researches in the area of wireless sensor networks (WSNs) in the past decade [1]. We deem that the research and development on WSNs are one of many factors that have stimulated great interest in WBAN. Actually, WBAN is a specific form of conventional WSN. Unlike WSNs however, WBANs have their own characteristics as discussed below, which distinguish themselves from WSNs and also create new technical challenges. Deployment and Density The number of sensor/actuator nodes deployed on the wearer depends on use cases. Typically, the nodes are placed strategically on the human body or hidden in clothing; they are not deployed with a high redundancy to tolerate node failures as in conventional WSNs, and thus do not require a high node density. WSNs however, are often deployed in the fields which are not easily accessible by personnel and as a result, require more nodes than necessary to be randomly placed and compensate node failures. Data Rate Most WSNs are applied for event-based monitoring, where events can happen irregularly. In com- parison, WBANs are employed for registering human’s physiological activities and actions, which vary in a more periodic manner. As a consequence, the application data streams exhibit relatively stable rates. Latency As mentioned above, the latency requirement of a WBAN originates from the applications and may be traded for reliability and energy consumption. While energy saving is definitely beneficial, replacement of batteries in WBAN nodes is much easier than in WSNs, which nodes can be physi- cally unreachable after deployment. Therefore, it may be necessary to maximize battery life-time in a WSN, compared to in a WBAN, at the expense of higher latency. Mobility Wearers of WBANs may move around. WBAN nodes affiliated with the same wearer move together and in the same direction. In other words, the nodes in one WBAN share the mobility pattern. In contrast, WSN nodes are usually considered to be stationary, and any node mobility does not occur in groups. 1.2 Huge Needs Reaching the body area opens a new era for wireless networks. Huge needs for ubiquitous and human-centric communications drive the advances of WBAN, and appearing WBAN use cases are 3 1.2. Huge Needs creating a greater market that emphasizes these needs. The following gives a picture of what the huge needs are from the perspectives of healthcare, sports and fitness, and secure authentication. 1.2.1 Healthcare According to the U.S. Census Bureau, worldwide population of elderly over age 65 is expected to more than double by 2020, and more than triple by 2050 [48]. According to World Health Organization, more than 1 billion people in the world are overweight, and at least 300 million of those are clinically obese; over 600 million people worldwide have chronic diseases. Statistics have also confirmed the aging trend of women giving first-time births. At the same time, electronic health (e-health) has evolved from telehealth to mobile health (m-health), enabling long-term ambulatory monitoring and point-of-care. Research projects have produced implantable or wearable devices for patients, the disabled, aging people, pregnant women and neonates. 1.2.2 Sports and Fitness According to Bluetooth Alliance’s study on market potential for its low energy technology, the volume in sports and exercise will be 47 million in 2010, and over 100 million in 2012. The financial results briefing for the fiscal year for Nintendo shows that the most successful Wii game, Wii Sports, had been sold 50.54 million copies worldwide as of March 2009. The global trend of integrating unobtrusive devices for sports and fitness has been making them easy and merry. Professional equipments, smart phones and even watches are being connected to the wireless network to enhance the exercise or training experience. A good example is the Nike+Ipod Sports Kit, which connects the Nike shoes and Apple’s portable devices together, and even integrates with web services. 1.2.3 Secure Authentication According to Federal Bureau of Investigation, 1 billion dollars are being spent to create a new biometric database, including DNA, fingerprints and other biometric data. Also in Germany, the market has confirmed an increasing revenues of biometrics, in hundreds of millions of euro dollars. The goal of secure authentication has involved both physiological and behavioral biometrics, among which facial patterns, finger prints and eye irises are employed extensively. The potential problems, e.g., proneness to forgery and duplicability however, have motivated the investigations into new physical/behavior characteristics of human body, e.g., EEG and gait, and multimodal biometric systems. 4 1.3. Technology Advances 1.3 Technology Advances 1.3.1 Body Area Channel Models In the past few years, researchers have made considerable progress in characterizing the body area propagation environment through both measurement-based and simulation-based studies [19][44][53] in order to support: • Prediction of link level performance in alternative sensor deployment configurations; • Development of more effective antennas with, e.g., lower specific absorption and better cou- pling to the dominant propagation modes. These works have been conducted in both the Industrial, Scientific and Medical (ISM) bands between 400 MHz and 2.45 GHz and in the ultra-wideband (UWB) frequency allocation between 3.1 and 10.6 GHz. In each of the frequency bands, intra-body, on-body and off-body channels have been studied [9]. Significant progress has also been made toward: • Identification of the propagation mechanisms that affect signal transmissions between nodes; • Assessment of the effects of multipath reflections from the external environment to signal transmissions between nodes; • Characterization of the fading statistics on body links that occur With body motion and change of body position in both sparse and rich scattering environments; • Development of standard UWB channel impulse response models and evaluation of typical modulation schemes utilizing. 1.3.2 Low-power Radio Technologies Following is a comparative study of emerging and existing low-power radio technologies, including Bluetooth Low Energy [3], ZigBee [2] and IEEE 802.15.4 [26], as well as UWB and IEEE 802.15.6 [25]. Bluetooth Low Energy Technology Bluetooth Low Energy technology, formerly known as Bluetooth Low End Extension (LEE), and later Wibree, provides ultra-low power consumption and cost while minimizing the difference be- tween Bluetooth and itself. Introduced in 2004 by Nokia, Bluetooth LEE was designed to wirelessly connect small devices to mobile terminals. Those devices are often too tiny to bear the power con- sumption as well as cost associated with a standard Bluetooth radio, but are ideal choices for the health-monitoring applications discussed in Section 1.6. Bluetooth LEE was said to be a ”hardware- optimized” radio, which means its major difference from Bluetooth resides in the radio transceiver, baseband digital signal processing and data packet format. After further development under the project MIMOSA, which targets use cases including both WBANs and WPANs, LEE was released 5 1.3. Technology Advances to public with the name Wibree in 2006. One year later, an agreement was reached to include it in future Bluetooth specifications as Bluetooth Low Energy technology. Bluetooth Low Energy technology is expected to provide a data rate of up to 1 Mbps. Using fewer channels for paring devices, synchronization can be done in a few milliseconds compared to Bluetooth’s seconds. This benefits latency-critical WBAN applications, e.g., alarm generation and emergency response, and enhances power saving. Bluetooth Low Energy products can be categorized into two groups: dual-mode chips and stand-alone chips. As the names indicating, stand-alone chips are intended to be equipped with sensors/actuators and talk to each other only, while dual-mode chips are to be equipped with a personal server, e.g., smart phone, and able to also connect to traditional Bluetooth devices. Similar to Bluetooth, Bluetooth Low Energy technology will likely operate using a simpler protocol stack and focus on short-range star-configured networks without complicated routing al- gorithms. This suits WBANs configured in star-topology, and provides better mobility support for them. Inter-WBAN communications can be realized through a second radio or using a dual-mode chip; however, the tradeoff is larger power consumption. ZigBee and IEEE 802.15.4 ZigBee/IEEE 802.15.4 targets low-data-rate and low-power-consumption applications. Specifically, ZigBee Alliance has been working on solutions for smart energy, home automation, building au- tomation and industrial automation. The recently completed ZigBee Health Care public applica- tion profile provides a flexible framework to meet Continua Health Alliance requirements for remote health and fitness monitoring. These solutions better suit WBAN deployment scenarios in a limited area, e.g., a hospital or a house. ZigBee/IEEE 802.15.4 devices can operate in three ISM bands, with data rates from 20 Kbps to 250 Kbps. ZigBee supports three types of topologies - star, cluster tree and mesh. In the star topology, a coordinator initiates and controls the network (i.e., similar to a piconet in Bluetooth) but there is no need for synchronization. The major advantage of ZigBee is its capability of providing multi-hop routing in a cluster tree topology or a mesh topology. As a result, WBAN network coverage can be expanded to a WPAN area using the same radio. A ZigBee mesh network may include both full-function devices (FFD) and reduced-function devices (RFD), where a RFD is equivalent to a stand-alone chip in Bluetooth Low Energy and can only act as an end device while a FFD is equivalent to a dual mode chip and can also act as a coordinator or a router. There have been many academic research projects utilizing ZigBee for transporting health- related data. Most prototypes mentioned in Section 1.6, however, are based on IEEE 802.15.4 chips that do not employ the higher layer ZigBee protocol stack, either because networking capability is not a must, or researchers are interested in devising more appropriate protocols. In our view, ZigBee may have a better chance to be adopted in the area of home automation and industrial automation and control, while in the area of connecting low-power peripheral devices around the human body, e.g., watches, health-related monitors and sports sensors, Bluetooth Low Energy technology possesses a bigger potential to be widely employed, due to its association with Bluetooth 6 1.3. Technology Advances as well as lower cost and lower power consumption. UWB and IEEE 802.15.6 According to the Federal Communications Commission (FCC), UWB refers to any radio technology having a transmission bandwidth exceeding the lesser of 500 MHz or 20% of the arithmetic center frequency. FCC also regulates license-free use of UWB in 3.1 - 10.6 GHz band to have a relatively low power spectral density emission. This leads to the suitability of UWB applications in short- range and indoor environments, and environments sensitive to RF emissions, e.g., in a hospital. Commercial products based on UWB provide extremely high data rates, e.g., ”Certified Wireless USB” devices work at up to 480 Mbps, enabling short-range wireless multimedia applications, such as wireless monitors, wireless digital audio and video players and other HCI use cases. These multimedia devices can be either wirelessly connected with WBANs or are themselves portable as part of a WBAN. UWB is also an ideal technology for precise localization, which complements global positioning system (GPS) in the indoor environment for WBAN tracking. At the same time, concerns with electronic and magnetic energy absorbed by human tissues from RF circuits placed in close proximity to humans mean that WBAN devices need to employ low transmission power and low transmission duty cycles. In this regard UWB outperforms conventional transmission methods and thus attracts much attention. An emerging WBAN standard, IEEE 802.15.6 - Body Area Networks (BANs), will likely employ UWB, according to recent proposals and meeting minutes. The standard intends to endow future generation electronics in close proximity to, or inside human body. However, when this standard and any electronics that utilize it will become available remain unknown. Other Technologies A summary of the above low-power radio standards is listed in Table 1.1 for comparison. Also sum- marized in the table are proprietary and open technologies like Insteon, Z-Wave, ANT, RuBee and radio frequency identification (RFID). Insteon and Z-Wave are both proprietary mesh-networking technologies for home automation. Z-Wave works in the 2.4 GHz ISM band while Insteon makes use of both power lines and the 900 MHz ISM band. ANT is another proprietary sensor network- ing technology, featuring a simpler protocol stack and lower power consumption. ANT has been embedded in Nike+Ipod Sports Kit mentioned above to collect workout data. RuBee and RFID are both used for asset management and tracking. These technologies are complimentary to each other in terms of frequency bands, battery life and use cases. They have all been implemented on silicon chips and are being sold in comparable volumes each year. With the advances of very large scale integration (VLSI), dual and multiple-standard radios can be intergraded into a single chip, greatly reducing the cost and power consumption, while fostering combining as well as merging of technologies. 7 1.3. Technology Advances Technology Frequency Band Data Rate (bps) Multiple Access Method Coverage Area (meter) Network Topology Bluetooth Low Energy 2.4 GHz ISM 1 M FH + TDMA 10 Star ZigBee (IEEE 802.15.4) ISM 250 K CSMA 30-100 Star/Mesh UWB (IEEE 802.15.6) 3.1-10.6 GHz 480 M CSMA/TDMA <10 Star Insteon 131.65 KHz (Powerline) 902-924 MHz 13 K Unknown Home Area Mesh Z-Wave 900 MHz ISM 9.6 K Unknown 30 Mesh ANT 2.4 GHz ISM 1 M TDMA Local Area Star/Mesh RuBee (IEEE 1902.1) 131 KHz 9.6 K Unknown 30 Peer-to- peer RFID (ISO / IEC 18000-6) 860-960 MHz 10-100 K Slotted-aloha / Binary tree 1-100 Peer-to- peer Table 1.1: WBAN and WPAN technologies and comparisons. 1.3.3 QoS Provisions in WBAN IEEE 802.15 Task Group 6 - Body Area Networks - is actively discussing proposals for a new optimized standard for WBAN. Providing a reliable communication service to prioritized data traffic is highly desirable; however, a limited amount of work on this topic exists in the literature for such a network type. A pioneering research employing QoS provisioning for WBAN application traffic has been published in [54]. In this work, a radio-agnostic QoS framework, BodyQoS, which adaptively schedules bandwidth to prioritized WBAN applications, is proposed. BodyQoS has been implemented on a radio scheme that complies with IEEE 802.15.4 nonbeacon-enabled mode [26], which is based on a contention multiple access method. This motivates us to design a QoS framework directly based on the beacon-enabled mode of IEEE 802.15.4 standard utilizing both the contention access period (CAP) and contention-free period (CFP) mechanisms. The proposed QoS framework will better differentiate WBAN application traffic, serve periodic traffic more directly through contention-free multiple access method, and provide easier adaptation to the standard for implementat IEEE 802.15.4 Beacon-enabled Mode The IEEE 802.15.4 standard specifies physical and media access control (MAC) layer protocols that are designed for low data-rate, short-range WPAN. Although a WPAN by definition covers a comparatively larger area than a WBAN, this coverage is achieved in IEEE 802.15.4 by transmitting at a higher power and through multi-hop relay. Therefore IEEE 802.15.4 has been selected for use in several WBAN platforms [4][33], which limit the transmission range by setting an appropriate transmission power and configuring the network in a star topology. Two operation modes are supported in the IEEE 802.15.4 MAC layer, namely beacon-enabled 8 1.3. Technology Advances mode and non-beacon-enabled mode. Beacon-enabled mode utilizes a slotted-CSMA (carrier sense multiple access) approach in CAP and a time division multiple access (TDMA) approach in CFP to provide low-latency communications. On the contrary, non-beacon-enabled mode uses an unslotted- CSMA approach, which enables a more distributed network and power savings. The larger power consumption of the beacon-enabled mode originates from the need for a device to periodically wake up and listen to beacon frames. However, in our QoS platform proposed in Chapter 2, satisfying the time constraints of critical WBAN applications outweighs power-saving concerns. ff fifl ffi ! " ff ff fi ffi ! " ff #$&% '
- Library Home /
- Search Collections /
- Open Collections /
- Browse Collections /
- UBC Theses and Dissertations /
- A novel wireless three-pad ECG system for generating...
Open Collections
UBC Theses and Dissertations
Featured Collection
UBC Theses and Dissertations
A novel wireless three-pad ECG system for generating conventional 12-lead signals Cao, Huasong 2010
pdf
Notice for Google Chrome users:
If you are having trouble viewing or searching the PDF with Google Chrome, please download it here instead.
If you are having trouble viewing or searching the PDF with Google Chrome, please download it here instead.
Page Metadata
Item Metadata
Title | A novel wireless three-pad ECG system for generating conventional 12-lead signals |
Creator |
Cao, Huasong |
Publisher | University of British Columbia |
Date Issued | 2010 |
Description | A wireless body area network (WBAN) is a radio-frequency (RF) based wireless networking technology that interconnects tiny nodes in, on or around a human body. Typically, the transmissions of these nodes cover a short range of about 2 meters. This thesis presents a complete survey on recent advances in WBAN, including the market needs, channel modeling, standardization of low-layer communication protocols, quality-of-service (QoS) provisions, developments of sensors/actuators, WBAN architectures and experimental platforms. A recent work employing the nonbeacon-enabled mode of the IEEE 802.15.4 standard for QoS provisions has motivated us to design a QoS framework based on the beacon-enabled mode of the same standard. The proposed QoS framework can better differentiate WBAN application traffic streams and serve periodic traffic more directly through the time-division-multiple-access (TDMA) based mechanism. A dominant feature of the proposed framework is the minimum adaptation to the existing standard, which makes it easy to adopt our platform and associated algorithms, as well as to implement them on off-the-shelf hardware platforms. Employing the proposed QoS framework, we propose a novel wireless three-pad electrocardiography (W3ECG) system. W3ECG furthers the pad design idea of single-pad wireless ECG systems. Inspired by the transformation possibility of signals obtained in vectorcardiographic (VCG) systems, we bring two more pads to the single-pad approach to gain spatial variety of the heart activity. Signals obtained from these three pads, plus the spatial information, make it possible to synthesize conventional 12-lead ECG signals. We have been able to manufacture the front-end ECG circuit, and combine it with an IEEE 802.15.4 hardware platform TelosB. Software for the server and pad has also been developed to make a fully running W3ECG possible. By explaining and evaluating our QoS platform designed for general WBAN applications, and our W3ECG system invented for particular healthcare area, we foresee a bright future for wide deployments of such kind of wireless networks on the human body. |
Genre |
Thesis/Dissertation |
Type |
Text |
Language | eng |
Date Available | 2010-04-16 |
Provider | Vancouver : University of British Columbia Library |
Rights | Attribution-NonCommercial-NoDerivatives 4.0 International |
DOI | 10.14288/1.0069905 |
URI | http://hdl.handle.net/2429/23741 |
Degree |
Master of Applied Science - MASc |
Program |
Electrical and Computer Engineering |
Affiliation |
Applied Science, Faculty of Electrical and Computer Engineering, Department of |
Degree Grantor | University of British Columbia |
GraduationDate | 2010-05 |
Campus |
UBCV |
Scholarly Level | Graduate |
Rights URI | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
AggregatedSourceRepository | DSpace |
Download
- Media
- 24-ubc_2010_spring_cao_huasong.pdf [ 5.37MB ]
- Metadata
- JSON: 24-1.0069905.json
- JSON-LD: 24-1.0069905-ld.json
- RDF/XML (Pretty): 24-1.0069905-rdf.xml
- RDF/JSON: 24-1.0069905-rdf.json
- Turtle: 24-1.0069905-turtle.txt
- N-Triples: 24-1.0069905-rdf-ntriples.txt
- Original Record: 24-1.0069905-source.json
- Full Text
- 24-1.0069905-fulltext.txt
- Citation
- 24-1.0069905.ris