Design and Implementation of a Pervasive Health Monitoring System via Body Area Networks

Transcription

1 University of Manitoba Department of Electrical & Computer Engineering ECE 4600 Group Design Project Progress Report Design and Implementation of a Pervasive Health Monitoring System via Body Area Networks by Group 01 Cassandra Aldaba Yang Su Jeff Winkler Tianqi Liang Haiyue Wang Academic Supervisor Dr. Jun Cai Industry Supervisor Michael G. Zhang Wellness Institute of Seven Oaks General Hospital Reporting Period April 14, 2014 to January 12, 2015 Date of Submission January 12, 2015 Copyright 2015 Cassandra Aldaba, Tianqi Liang, Yang Su, Haiyue Wang, Jeff Winkler

2 TABLE OF CONTENTS Table of Contents 1 Introduction Project Progress Electrocardiogram: Cassandra & Jeff Spatial Positioning: Tianqi & Yang Posture Recognition: Cassandra & Yang Pedometer: Haiyue & Jeff Android Application: All Future Work Electrocardiogram: Cassandra & Jeff Spatial Positioning: Tianqi & Yang Posture Recognition: Cassandra & Yang Pedometer: Haiyue & Jeff Android Application: All Conclusions References Appendix A Updated Budget Appendix B Updated Gantt Chart i

3 1 Introduction 1 Introduction The purpose of this project is to design and develop a remote patient health monitoring system via body area networks whereby patient-worn sensors will transmit their data wirelessly to a remote computational device. The developed network will connect patient worn sensors to monitor electrocardiogram (ECG) measurements, patient spatial positioning, patient posture, as well as pedometer measurements. Furthermore, all sensor information will be processed and displayed on a dedicated Android application. Such patient information will provide healthcare professionals the ability to easily and continually monitor patient well-being in a noninvasive and unobtrusive manner. The project workload was split into five modules: ECG measurement, patient positioning, posture sensing, pedometer, and Android application development. For all modules except the Android application, two team members took the lead on individual modules. All team members contributed to the development of the Android application module. The total cost of the project is estimated to be $ and within the allotted budget of $ (more details in Appendix A). 2 Project Progress There has been significant progress made in all project aspects, specifically the ECG measurement, posture recognition, spatial positioning, pedometer and Android application modules. The posture recognition module is ahead of schedule, however the spatial positioning module is slightly behind due to unforeseen issues with the software. The remaining modules are on schedule. The above mentioned delay in the spatial positing module has been accounted for in our updated Gantt chart (more details in Appendix B). 1

4 2 Project Progress 2.1 Electrocardiogram: Cassandra & Jeff The ECG signal detection circuit was designed to amplify and extract the desired signal in five modular stages which are as follows: a differential amplifier, low-pass filter, high-pass filter, notch filter and a final amplifier with DC bias. The circuit design was simulated within the software environment Multisim, then optimized to obtain the desired specifications. Next, the ECG circuit was physically constructed and then tested with simulated input signals. The detection circuit was proven to meet all the outlined design criteria. Lastly, a human subject was connected to the detection circuit and the circuit s output visualized via an oscilloscope; a clear ECG waveform was easily distinguishable. For ECG measurements to be recorded and transmitted to our Android application, an rfduino (Arduino-based microcontroller) with integrated Bluetooth Low Energy (BLE) was chosen. Prior to implementing the rfduino with the final Android application, a standalone application was created to receive and display transmitted data onto a graphical data plot. When the transmitter was tested with the standalone application, the data transmission rate between the rfduino and application was too slow to meet our requirement for a sampling rate of 300 Hz. Our further research indicated that standard Bluetooth (BT) transmission technology would be more successful for continuous data transmissions [1]. In conclusion, we have decided to purchase and implement a BT transmitter device to achieve continuous communication between the ECG detection circuitry and the Android application. We are currently awaiting the arrival of the BT transmitter. 2.2 Spatial Positioning: Tianqi & Yang For the patient positioning module, we chose to utilize the Received Signal Strength Indicator (RSSI) fingerprinting method due to its simplicity and accuracy [2].The first step to implementing the fingerprinting method requires the construction of a WiFi signal strength database in the location where we wish to perform patient location tracking. Since our demonstration will take 2

5 2 Project Progress place in the EITC atrium, we chose to create a RSSI database for this specific area. Initially, we recorded the signal strength for 27 locations in the EITC atrium. For each location, the cellular phone was oriented in the same direction, and we used the signal received from the university s wireless routers that are placed in this area. Our results were quite erroneous, with the positional error exceeding 10 meters for some locations simply by changing the orientation of the phone. As a result, we purchased and implemented our own network consisting of six wireless routers to provide a more uniform WiFi signal strength in all orientations. We then recorded the signal strength at four perpendicular orientations for 270 locations in the EITC atrium. The data was compiled into a database and the functionality was tested at various locations in the EITC atrium utilizing the PC software Matlab. Test results indicated that on average, the positional error was now only 2-3 meters. In the last two weeks, we have begun to implement our algorithm into the Android application. 2.3 Posture Recognition: Cassandra & Yang For the posture recognition module, the Texas Instruments sensortag device was chosen as it combines a 3-axis accelerometer sensor with a BLE module in a cost-effective and compact package. We successfully developed, tested, and implemented an initial thresholding algorithm that uses accelerometer readings from two separate sensortags, one placed on the patient s chest, the other on the patient s thigh. The algorithm can determine 4 general postures (standing, bending, sitting or lying down) using angular thresholds. Furthermore, we developed a second algorithm to determine different lying-down positions using fuzzy logic functions. However, the initial and secondary algorithms would not function correctly when combined together. Therefore, the initial algorithm was reconfigured to use fuzzy logic functions only, thus eliminating the issues encountered when combining the initial and secondary algorithms. Additionally, we implemented a filter to exclude nonsensical readings from users who are in motion. In conclusion, the posture recognition module is nearly complete, and is able to successfully determine the patient s posture with high accuracy. 3

6 2 Project Progress 2.4 Pedometer: Haiyue & Jeff Initial research in the area of step sensing indicated that a 3-axis accelerometer placed on the thigh or ankle was the most effective method for accurately and efficiently detecting steps [3]. Firstly, an algorithm was developed for step detection using a thresholding technique. An associated application was then developed that could record acceleration values from the phone s internal accelerometer in terms of the x, y, and z axis. However, testing of the algorithm revealed that the accuracy was greatly dependent upon the spatial orientation of the cellular phone. As such, we decided to implement an algorithm utilizing a combination of techniques, namely thresholding, peak difference, and time interval measurements. We tested the combined-technique algorithm with a chosen sampling rate of 40 Hz, for this sampling rate resulted in the best balance between performance and power consumption. We have currently begun incorporating gyroscope measurements into our step detection algorithm to improve performance for patient s that walk at a slower than normal rate. 2.5 Android Application: All Since all group members had no prior knowledge in the area of Android application development, a significant amount of time was spent learning and testing the Android application architecture. We developed a basic application that could obtain transmitted measurements from the rfduino transmitter and the sensortag via BLE. Measurements received from the rfduino are successfully being displayed on a graphical real-time plot within the application. For the posture recognition module, average accelerometer measurements and a graphical image of the determined posture are displayed within the application. For the positioning system module, we successfully implemented a map of the EITC atrium and user interface into the application. Finally, for the pedometer module, the application displays the number of steps detected and allows a user to manually start, stop, and reset the step counter. 4

7 3 Future Work 3 Future Work Several tasks still remain to be completed within each of the five project modules to achieve overall successful completion by our expected completion date of March 1, The following subsections outline the tasks to be completed within the ECG, spatial positioning, posture recognition, pedometer, and Android application modules. 3.1 Electrocardiogram: Cassandra & Jeff The ECG circuitry requires further development and testing of the electronic and patient protection circuits. In the next two weeks, both circuits will be constructed and tested for compliance. Afterwards, we will purchase an enclosure to contain and affix the required ECG hardware onto a patient s torso. In addition, the existing Android application must be reconfigured to accept regular BT transmission protocols and further developed to include the ability to save ECG measurements to the memory of the Android device. Lastly, performance tests must be conducted and assessed to determine if the wireless transmission rate is within the margin outlined within the initial research and design phase. 3.2 Spatial Positioning: Tianqi & Yang For the patient spatial positioning device, we will dedicate the next two weeks to integrating our algorithm and WiFi fingerprinting database into the Android application for performance testing. Following the successful implementation, we will attempt to reduce the positional error further by implementing an algorithm that takes multiple samples and evaluates the most probable location from the additional samples. 5

8 3 Future Work 3.3 Posture Recognition: Cassandra & Yang Much of the posture recognition device is already complete. From now until the end of Febraury, we will be designing affixation methods for attaching the sensors to the body as well as optimizing the posture recognition algorithm. One sensortag device will be placed on the patient s torso through the integration of the sensortag enclosure with the ECG enclosure. The second sensortag will be placed on a patient s thigh by designing or purchasing a ready-made enclosure. We will continue to develop the posture recognition algorithm to improve the speed of determining the patient s posture. As well, we will implement a time log of the patient s posture that summarizes his or her activity throughout the day. 3.4 Pedometer: Haiyue & Jeff Until the end of Febuary, the step detection algorithm will be reconfigured and developed to integrate with the sensortag s gyroscope measurements in an effort to improve performance for patients that walk at a slower than average rate. As well, an affixation device must be developed to attach another sensortag to the patient for step sensing. The same style of affixation device used to attach a sensortag to a patient s thigh (for posture sensing) will be used to attach the sensortag to the ankle for step detection. 3.5 Android Application: All All group members will continue to optimize and test the Android application until the end of February. It remains to test system modules when all modules are operating simultaneously. As well, the appearance and functionality of the application will continue to be refined in an effort to increase the overall ease of use. 6

9 4 Conclusions 4 Conclusions Overall, there has been significant progress in all aspects of our wireless patient health monitoring system via body area networks. The ECG, posture recognition, pedometer and Android application modules are either on or ahead of our initially proposed schedule. However, we have encountered difficulty in the area of software development within the spatial positioning module. We are thus slightly behind our proposed schedule for the spatial positioning module. We plan to achieve successful overall completion by continuing our development, testing and refinement of each individual module. The estimated final cost of our project is $472.07, and is within our allotted budget of $ We are confident we can achieve successful completion of our project by the date of our final presentation and demonstration, Friday, March 13,

10 REFERENCES References [1] R. Nilsson and B. Saltzstein. Bluetooth Low Energy vs. Classic Bluetooth: Choose the Best Wireless Technology For Your Application. Internet: June 8, 2012 [Jan. 9, 2015]. [2] Jiang Long Liu; Yi He Wan; Bao Gen Xu; Si Long Tang; Xue Ke Ding; Qun Wan, A novel indoor positioning method based on location fingerprinting, Communications, Circuits and Systems (ICCCAS), 2013 International Conference on, vol.2, Nov , pp.239,242. [3] M. De Agostino, A.M. Manzino and M. Piras, Performances comparison of different MEMSbased IMUs, in Position Location and Navigation Symposium (PLANS), 2010 IEEE/ION, May 4-6, 2010, pp

11 Appendix A Updated Budget A complete list of components that were purchased and received are listed in table I. The project s budget provided by the Electrical and Computer Engineering Department is $ The estimated project cost is $ and remains below the project budget. 9

12 Table I: Updated Budget System Item Part Number Supplier Quantity Unit Cost Subtotal All ECG Posture Recognition Patient Positioning Pedometer Note: Enclosure (1) $ $ rfduino (2) 975-RFD90101 Mouser 1 $ $ - rfduino 975-RFD90101 Mouser 1 $ $ rfduino Coin Cell Breakout (2) 975-RFD22128 Mouser 1 $ $ - Cr2032 Coin Cell Battery P189-ND Digikey 10 $ 0.35 $ 3.50 Br-2325/2HAN 6V Coin Cell Battery P143-ND Digikey 10 $ 3.94 $ PCB 854-SB400 Mouser 2 $ 5.86 $ Male Headers - ECE Tech Shop - $ - $ - Female Socket - ECE Tech Shop - $ - $ - ECG Cables - ECE Tech Shop - $ - $ - ECG Electrodes - ECE Tech Shop - $ - $ - INA118 Amplifier 595-INA118P Mouser 3 $ $ OPA2604 Amplifier 595-OPA2604AP Mouser 2 $ 6.48 $ Op-Amp - ECE Tech Shop 2 $ - $ - Passive Devices (Resistors/Capacitors/Diodes) RTL Bluetooth Mate Silver Retail (1 ) V Panasonic Heavy Duty Battery Pkg (1) Right Angle Female Header Receptacle (1) - ECE Tech Shop $ - $ - RTL Abra Electronics 1 $ $ Abra Electronics 6 $ 2.24 $ FH-3 Abra Electronics 1 $ 0.58 $ 0.58 Texas Instruments SensorTag (2) ND Digikey 2 $ $ - Texas Instruments SensorTag Developer Kit ND Digikey 1 $ $ - Cr2032 Coin Cell Battery P189-ND Digikey 10 $ 0.35 $ 3.50 Wiresless Wifi Router TL-WR740N FutureShop 6 $ $ Texas Instruments SensorTag ND Digikey 1 Cr2032 Coin Cell Battery P189-ND Digikey 10 $ $ 0.35 $ $ 3.50 (1) - These items have not been ordered yet. (2) - These items were personal purchases. Shipping Costs $ Grand Total $

13 Appendix B Updated Gantt Chart Please see below for the updated version of our project s Gantt chart that shows the overall progress of the project and how much additional work is required for successful completion. This Gantt chart will be used as a tool to ensure that the project will finish by the expected presentation day on March 13,

14 Phase 1 Literature Review ECG Hardware & Safety Regulations 04/14/ /27/2014 Posture Recognition Techniques 04/14/ /27/2014 Patient Positioning System Methods 04/14/ /27/2014 Pedometer Hardware & Algorithms 04/14/ /27/2014 Wireless Connectivity 04/14/ /27/2014 Phase 2 Design and Simulation ECG Filters & Amplifiers 06/27/ /14/2014 ECG Safety Hardware 06/27/ /14/2014 Posture Recognition Hardware & Algorithm 06/27/ /02/2015 Positioning System Hardware & Algorithm 06/27/ /02/2015 Pedometer Hardware & Algorithm 06/27/ /02/2015 Phase 3 System Building and Testing ECG Filters & Amplifiers 09/15/ /01/2014 Posture Recognition 10/06/ /05/2015 Patient Positioning System 10/06/ /31/2015 Pedometer 10/06/ /31/2015 Low-Level Android Application Development 07/17/ /13/2014 Phase 4 System Integration Layout of Electronic Circuitry 01/05/ /30/2015 Enclosure Design 01/20/ /02/2015 Wireless Transmission Protocol 10/06/ /13/2015 High-Level Android Application Development 09/14/ /01/2015 Administrative Items Tasks Start End Administrative Milestones Proposal 09/15/ /26/2014 Informal Oral Progress Report 11/14/ /28/2014 Formal Written Progress Report 12/29/ /12/2015 Formal Oral Progress Report Presentation File 12/29/ /16/2015 Formal Oral Progress Report 01/16/ /30/2015 Draft of Final Report 02/23/ /27/2015 Final Report 02/03/ /04/2015 Final Presentation 02/03/ /13/2015 Apr. May Jun. Jul. Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar Legend: Work Completed Work Period Current Date Fig. B.1: Updated GANTT Chart for the Pervasive Health Monitoring System. 12