Study of XXX to observe the effects of YYY in Climate Change Replace Student Name Here Bachelor of Engineering Replace Engineering Specialisation Here School of Engineering Macquarie University Month Date, Year Supervisor: Supervisor’s name and title go here ACKNOWLEDGMENTS I would like to acknowledge and sincerely ….. STATEMENT OF CANDIDATE I, [Replace Student’s Name Here], declare that this report, submitted as part of the requirement for the award of Bachelor of Engineering in the School of Engineering, Macquarie University, is entirely my own work unless otherwise referenced or acknowledged. This document has not been submitted for qualification or assessment an any academic institution. Student’s Name: Student’s Signature: Date: ABSTRACT This project sims to demonstrate….. Contents ACKNOWLEDGMENTS. i STATEMENT OF CANDIDATE.. ii ABSTRACT.. iii Contents. iv List of Tables. vi List of Figures. vii Introduction. 1 1.1 Motivation. 1 1.2 Project Scope. 1 1.3 Assumptions. 1 1.4 Deliverables. 1 Background and Related Work. 2 2.1 Physical activity tracking. 2 2.2 Wearable devices for physical activity tracking. 4 2.3 Portable, small sized, internet connected wearable activity tracking devices 6 Wearable body motion detector requirements. 9 3.1 Introduction. 9 3.2 Related Work. 9 3.2.1 ADuCM3029. 9 3.2.2 Wi-Fi Module (ESP8266) 9 3.2.3 Detector sensor (ADXL345) 10 4 Methodology. 11 4.1 Connections between sensor and microcontroller 11 4.2 Testing the sensor 11 4.3 Software programming environment 12 4.4 Data acquisition. 12 Conclusions and Future Work. 13 Appendix A.. 15 Appendix B.. 17 References. 18 List of Tables Table 1 weekly Gantt Chart 21 List of Figures Figure 1 Camera-based monitoring system  9 Figure 2 comparison of input and output 10 Figure 3 shoe sketch map and hardware mounted on shoes 10 Figure 4 system structure 11 Figure 5 Interrupt processing 12 Figure 6 Flowchart of the operation by an overall program 14 Figure 7 Flowchart of the operation by an arm attachment body detection program 15 Figure 8 EVAL-ADICUP3029 diagram 17 Figure 9 SPI connection. 19 Figure 10 SPI sequence chart 19 Chapter 1 Introduction Introductory statements go here…. Followings are some suggested headings…. 1.1 Motivation 1.2 Project Scope 1.3 Assumptions 1.4 Deliverables Chapter 2 Background and Related Work Detecting devices are used to biomedical measurements in the past. The tracking system can keep accountable and motivates users, it also helps see the progress. In addition, these devices are going toward miniaturization, marketization and function. IoT devices such as tracking steps with a pedometer is a great simple solution – more than that, some of the benefits of using a more advanced fitness tracker are the extra features it offers. For example, as well as counting steps, some devices provide the “active minutes” that have completed. They also measure the quality of sleep and give an interesting picture of how restful users’ nights really are. Furthermore, the condition of food, weight and hydration can be tracked, which can help to create a fuller image of users’ overall health. 2.1 Physical activity tracking Video camera record is one method that tracks motions of body. In 2013, Hadjidj and Abdelkrim mention a traditional rehabilitation supervision method  which is effective. This camera-based monitoring system requires multiple cameras that need to be installed to cover different viewing angles, as shown in figure 1. In addition, these cameras require calibration and correlation to achieve accurate 3D positioning. This method clearly shows that this system is complex and can only be used by technical staff at specific area or by disabled people. Figure 1 Camera-based monitoring system  In addition, a real-time tracking system in video sequences . The main input of the system is the data from stationary camera which get the videos of the moving people in the environment. Then the input will be analyzed, and people movement will be transfer to trajectories, in addition, these trajectories will mark out the motion in this recording area. Figure 2 shows the input and output of the system. In the data analysis, system tracks the motion base on the model and then identifies the feature points in video frames. In the process, the transient and overlap parameters in each video frames are sorted and then match the points in video frame and work out the paths. Figure 2 comparison of input and output  Due to the large size of the equipment and working area, the system can only be used in some specific area which needs technique support and not suit for most consumers. In addition, a Shoe-Integrated Wireless Sensor System  is designed to analyze the gait for patients which use the large size transductors. This system decrease the size of the equipment which provides new opportunities for clinicians and patients to diagnose and treat chronic walking problems. The system has two shoe modules and a base station, in the modules, to analyze the kinematics of the foot, two biaxial accelerometers and three gyroscopes are positioned at the back of the shoe so that the sensory axes are aligned along three vertical axes. After data collection, wireless transmission will be used that process data. In addition, this module can also detect heels and toes off the ground, and estimate the direction and position of the feet. The base station consisted of a metal box with an antenna mounted externally, housing the circuit board with the microcontroller and the power board, plus an additional board with a MAX233 serial line level converter chip to drive a conventional RS-232 cable. Then, the microcontroller in the base station looped through a simple time-division multiplexing routine to poll each shoe and receive its data. This system can provide a vast quantity of long-term data not obtainable with current gait analysis systems, however an additional large device is required to support the system. Figure 3 shoe sketch map and hardware mounted on shoes  In addition, the physical activity tracking can be limited by the data transmission confine and activity area. An application on rehabilitation through wireless sensor networks . The device is used to supervise the condition of rehabilitation through wireless sensor networks (WSN) cognitive rehabilitation. It is good at higher accuracy data collection, easy control and low cost. However, the limit range of the data transmission confine the activity area. 2.2 Wearable devices for physical activity tracking Due to the large size of the equipment in body motion tracking system is not suit for most customers. Thus, a wearable device in need. Spatio-temporal gait analysis contains foot-worn inertial sensors is designed for children with cerebral palsy . The wearable device is developed for detecting stride length and speed for in independently walking children with cerebral palsy. Thus, a small size module with a stand-alone unit integrating a micro-controller, memory, a three-axis accelerometer, gyroscope and a battery inside is required. Thus, this device is easy to wear and use, provide a good accuracy and precision and allowed user to compare elements of motor behavior and adaptation strategies during straight and turning trajectories. However, SD card as a memory and it requires offline analysis after transfer data from SD card to PC. A low-cost, portable and wireless gait assessment tool  is developed and validated in 2013. The device with Bluetooth module, microcontroller chip, 9V battery, each instrumented insole, four 13 mm diameter force sensing resistors (FSRs) is used to detect the Spatio-temporal gait for healthy human (figure 4). The data is transmitted to PC by Bluetooth and processed though LabVIEW after that signals were sampled at 30 Hz and logged directly to a spreadsheet file. As a result, it can be used in clinical environment easily with its low-cost and the standard communication protocol which use Bluetooth. Figure 4 system structure  A Body motion detector  allow users make motion with appropriate motion intensity for every motion thereby to obtain an excellent exercise effect while exercising such as walking and running. While a user makes motion, a CPU determines whether the user makes appropriate motion by the amplitude, the period, and the detection frequency of an acceleration signal inputted from an acceleration sensor unit, and when it is determined that the user makes appropriate motion, operates an alarm generator thereby to notify the user that he/she makes motion with appropriate motion intensity. This device capable of notifying whether the user makes appropriate motion (i.e., an appropriate motion intensity, appropriate motion speed, and the appropriate number of motion) for every motion while making repetitive motion, such as walking and running. Further, the present invention provides a body motion detector that allows a user to maintain the appropriate motion intensity, the appropriate motion speed, and the appropriate number of motion by continuously notifying the user whether the motion is appropriate, and thereby to obtain an excellent exercise effect. In order to address or achieve the above, there is provided a body motion detector, including: a body motion detecting device to detect body motion accompanying repetitive motion of a user; a determining device to determine whether the detection result of the body motion detecting device is within a predetermined reference range; and a notifying device to generate a notifying signal whenever the determination result by the determining device is within the predetermined reference range. Figure 5 Interrupt processing  According to the above configuration, because a user can check whether user makes appropriate motion, that is, makes motion within a predetermined reference range by an notifying signal for every motion while making repetitive motion Such as walking and running, it is possible for the user to make repetitive motion to obtain an excellent exercise effect while maintaining appropriate motion when the user exercises so as to obtain a determination result which is continuously positive. The detection result is the motion intensity of the repetitive motion. In addition, to improve the range of application. Another body motion detector called US7034694B2 which will be attached to or carried by a user for detecting body motions of the user, uses a plurality of Sensors each for detecting body motions in a Specified direction to output a body motion signal according to the user’s body motion. These Sensors are disposed So as to detect the body motion in different directions. One of these Sensors are Selected by carrying out calculations on Signals outputted from the sensors. The user’s body motion is detected by using selectively output signals from the selected body motion sensor. The body motion detector may also include a component for detecting the orientation of the body motion detector itself from Signals outputted from these Sensors and another component for detecting the user’s body motions by carrying out calculations on the Signals outputted from these Sensors, corresponding to the orientation as detected by the orientation detecting component. 2.3 Portable, small sized, internet connected wearable activity tracking devices Conventionally, various body motion detection devices such as a pedometer and an activity amount meter have been proposed as a device for detecting body motion of a living body. Such body motion detection devices have been proposed to be used by being attached or accommodated in various places. For example, there has been proposed a pedometer to be attached to a belt at the waist, a chest pocket of the clothes, and the like by clipping with a clip. Such a method of attaching by clipping with the clip has an advantage that stable measurement of the number of steps is enabled since an attachment direction is defined. However, this method has drawbacks in that it is limited to clothing with areas that can be clipped with the clip and it may affect fashion features of a user since the pedometer stands out. There has also been proposed a pendulum type exercise amount meter having a wrist watch shape to be attached to the arm with a belt. Such a method of attaching to the arm with the belt has advantages in that the clothing is not limited and that the user can comfortably see the display content. This method, however, has a problem in that the measurement cannot be carried out unless with an activity in which the arm is strongly swung, and that the application is limited in daily use. There has also been proposed a body motion detection device that enables the measurement of the number of steps even if the main body is tilted by using a sensor of a plurality of axes, and can be carried around in the pocket of the clothing or the bag. Such a method of using the sensor of a plurality of axes has an advantage of excelling in portability. This method, however, has a problem of being easily subjected to the influence of various body motions as the measurement in the tilted state is carried out, and it is difficult to respond to a wide range of activities at high accuracy. Therefore, there are advantages and drawbacks regardless of the attachment form the body motion detection device is used, and the drawbacks cannot be solved with one attachment form. Thus, a new device US 2011/0295547 A1 -BODY MOTION DETECTION DEVICE get some improvements on body motion detector US7034694B2 which increases the freedom of a usage mode and improves the measurement accuracy. This exercise amount meter main body including an acceleration detection unit for detecting acceleration, and a calculation unit for executing a body motion calculating process for calculating body motion of a living body based on acceleration data, further includes an attachment/detachment guide for allowing a belt type attachment body and a clip type attachment body to be attached or detached, wherein the calculation unit executes an attachment/detachment detection process for detecting attachment or detachment from the change in acceleration that appears in the acceleration data when attaching or detaching the belt type attachment body or the clip type attachment body to or from the attachment/detachment guide, and executes the body motion calculating process based on the detected attachment/detachment while switching to a mode complying with a state after the attachment/detachment. Furthermore, the communication unit may be configured by an appropriate communication interface Such as a wire connecting USB (Universal Serial Bus) or a wireless communicating Blue tooth. Figure 6 Flowchart of the operation by an overall program  Figure 6 is a flowchart showing the operation of the calculation unit 14 for executing the body motion calculating process according to the arm attachment body motion detection program in the arm attachment mode. The calculation unit 14 acquires the acceleration data of the XYZ acceleration detected in the three-dimensional acceleration detection unit 12 (step S31). Thereafter, both the number of steps counting process (steps S32 and S33) and the arm swing level calculating process (steps S34 to S36) are processed in parallel, and both the number of steps and the arm Swing level are obtained from one acceleration data. The number of steps counting process (steps S32 and S33) and the arm swing level calculating process (steps S34 to S36) are not limited to the parallel process, and may be sequentially executed. Even if the processes are sequentially executed, the object can be achieved by calculating the number of steps and the arm swing level both from one acceleration data. The calculation unit 14 for performing the number of steps counting process calculates the number of steps from the acceleration data (step S32). In this case, the calculation unit 14 calculates the number of steps using the arm attachment mode parameter. The number of steps can be accurately detected since the number of steps can be counted with the parameter Suited to the state of being attached to the arm. The calculation unit 14 displays the calculated number of steps on the display unit 13 as an arm attachment mode display screen 13a (step S33), and terminates the body motion calculating process. The arm attachment mode display screen 13a in this case may display Arm Mode” indicating the arm attachment mode, “9758 steps” indicating the total number of steps for today, “Swing LV.5′ The calculation unit 14 for performing the arm Swing level calculating process calculates the amplitude of the acceleration in the front and back direction (Z direction) (step S34), and calculates the arm swing level (step S35). In this case, the calculation unit 14 calculates the arm Swing level using the arm attachment mode parameter. The arm Swing level can be accurately calculated since the arm Swing level can be calculated with the parameter suited to the state of being attached to the arm. 0123. The calculation unit 14 displays the calculated arm swing level on the display unit 13 (step S36), and terminates the arm Swing level calculating process. The display in this case may be the same as the display described in step S33. Figure 7 Flowchart of the operation by an arm attachment body detection program  Chapter 3 Wearable body motion detector requirements 3.1 Introduction The wearable body motion detector will be designed based on the literature research papers. In addition, the control board ADCUP3029 (ADuCM3029 is the microcontroller of this board) will be used that connects with the ADXL345 digital accelerometer, then the data will transmit by ESP8266 Wi-Fi module to realize the remote and real-time supervision. Moreover, the software CCES will be used to program and figure out the data output from this body motion tracking system. 3.2 Related Work 3.2.1 ADuCM3029 The EVAL-ADICUP3029 is a development board compatible with Arduino and PMOD, with built-in Bluetooth and Wi-Fi connectivity. The board uses the Eclipse-based open source interactive development environment (IDE) CrossCore Embedded Studio. In side the board ADuCM3029 is an ultra-low power with 32-bit ARM Cortex™-M3 processor microcontroller. It can accepts +7V to +12V DC supply voltage. In addition, USB is provided that used for flash programming and debug interface, furthermore, a virtual serial port connection is provided to the microcontroller. Nevertheless, SPI and I2C connectors are given that can connect with other hardware Figure 8 EVAL-ADICUP3029 diagram  3.2.2 Wi-Fi Module (ESP8266) ESP8266 offers a complete and self-contained Wi-Fi networking solution, allowing it to either host the application or to offload all Wi-Fi networking functions from another application processor. When ESP8266 hosts the application, and when it is the only application processor in the device, it is able to boot up directly from an external flash. It has integrated cache to improve the performance of the system in such applications, and to minimize the memory requirements. Alternately, serving as a Wi-Fi adapter, wireless internet access can be added to any microcontroller-based design with simple connectivity through UART interface or the CPU AHB bridge interface. 3.2.3 Detector sensor (ADXL345) ADXL345 is a small, thin, ultralow power, 3-axis accelerometer with high resolution (13-bit) measurement at up to ±16 g. SPI and I2C digital interface are provided that can match with the ADuCM3029 microcontroller. It can measure dynamic acceleration caused by motion or shock, as well as static acceleration, such as gravity acceleration, so that the device can be used as a tilt sensor. The sensor is a polysilicon surface micro machined structure placed on top of the wafer. Due to the application of acceleration, the polysilicon spring is suspended above the structure on the surface of the wafer to provide strength resistance. The differential capacitor consists of a separate fixed plate and a movable mass connection plate that measures the deflection of the structure. The acceleration deflects the inertial mass and the differential capacitance is unbalanced so that the amplitude of the sensor output is proportional to the acceleration. Phase sensitive demodulation is used to determine the magnitude and polarity of the acceleration. The ADXL345 is well suited for mobile device applications. It measures the static acceleration of gravity in tilt-sensing applications, as well as dynamic acceleration resulting from motion or shock. Its high resolution (3.9 mg/LSB) enables measurement of inclination changes less than 1.0°. 4 Methodology 4.1 Connections between sensor and microcontroller In the connection between controller board and ADXL345, several special sensing functions are provided. Activity and inactivity sensing detect the presence or lack of motion by comparing the acceleration on any axis with user-set thresholds. Tap sensing detects single and double taps in any direction. Freefall sensing detects if the device is falling. These functions can be mapped individually to either of two interrupt output pins. An integrated memory management system with a 32-level first in, first out (FIFO) buffer can be used to store data to minimize host processor activity and lower overall system power consumption. In addition, Low power modes enable intelligent motion-based power management with threshold sensing and active acceleration measurement at extremely low power dissipation. Figure 9 SPI connection  4.2 Testing the sensor In the standard SPI bus timing, the operation between each byte is disconnected, that is, the chip select line SS_n and the serial clock line SCLK are valid when the byte is being transmitted, and the rest of the time remains inactive (ie, SS_n=1, SCLK=1) as shown below: Figure 10 SPI sequence chart  As can be seen from the figure, the SPI bus reads and writes the internal registers of the ADXL345 in units of bytes (this is determined by the 30 internal registers of the ADXL345 which are all byte registers), but every two CS_n and SCLK need to remain valid between bytes of data/address. From the above analysis, when accessing the ADXL345 with the nios2 SPI API function alt_avalon_spi_command() provided by Altera, the last parameter alt_u32 flags must be set to ALT_AVALON_SPI_COMMAMD_MERGE, which means “combining multiple SPI access commands/data together”, If set to 0, it is a standard SPI timing that automatically releases CS_n and SCLK after each access. 3. Read the order of the ADXL345 internal registers First read the DEVID of the address 0x00, the device ID register, and then perform other register operations. As long as the device has a unique ID number, it should be necessary and first to first read the device ID register and determine the correct one. 4.3 Software programming environment 4.4 Data acquisition Chapter 4 Conclusions and Future Work In conclusion, we noticed… Appendix A Appendix B Abbreviation CCES: CrossCore Embedded Studi API: Application programming interface PCB: Printed Circuit Board FSR: Force sensing resistor CPU: Central Processing Unit References 1. Yamaguchi, Kenji, and Norimitsu Baba. “Body motion detector.” U.S. Patent No. 7,034,694. 25 Apr. 2006.Kubo, Nobuo, Kiichiro Miyata, and Hiromi Matsumoto. “Body motion detector.” U.S. Patent No. 6,700,499. 2 Mar. 2004.Asada, Yuji, and Masahiro Kitagawa. “Body motion detection device.” U.S. Patent Application No. 13/184,139.L. Suhuai and H. Qingmao, „A Dynamic Motion Pattern Analysis Approach to Fall Detection”, IEEE International Workshop on Biomedical Circuits & Systems, BioCAS2004, pp. 2.1-5, 2004.Bamberg, Stacy J. Morris, et al. “Gait analysis using a shoe-integrated wireless sensor system.” IEEE transactions on information technology in biomedicine 12.4 (2008): 413-423.Bourgeois, A. Brégou, et al. “Spatio-temporal gait analysis in children with cerebral palsy using, foot-worn inertial sensors.” Gait & posture 39.1 (2014): 436-442.Macleod, Catherine A., et al. “Development and validation of a low-cost, portable and wireless gait assessment tool.” Medical engineering & physics 36.4 (2014): 541-546.et al. “Wireless sensor networks for rehabilitation applications: Challenges and opportunities.” Journal of Network and Computer Applications 36.1 (2013): 1-15.Luo, Suhuai, and Qingmao Hu. “A dynamic motion pattern analysis approach to fall detection.” Biomedical Circuits and Systems, 2004 IEEE International Workshop on. IEEE, 2004.Sonka, Milan, Vaclav Hlavac, and Roger Boyle. Image processing, analysis, and machine vision. Cengage Learning, 2014.Segen, Jakub. “A camera-based system for tracking people in real time.” Proceedings of 13th International Conference on Pattern Recognition. Vol. 3. IEEE, 1996.
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