Low power motion tracking wireless node for IoT applications
Internet of Things (IoT) is one of the most ground-breaking forms of technology in the modern era. Embedded systems allow engineers to augment daily life objects through the use of microcontrollers and microprocessors. The Internet of Things allows communication and exchange of data in a network of...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/10356/74931 |
| _version_ | 1824456033109540864 |
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| author | Wijaya, Davin |
| author2 | Vun Chan Hua, Nicholas |
| author_facet | Vun Chan Hua, Nicholas Wijaya, Davin |
| author_sort | Wijaya, Davin |
| collection | NTU |
| description | Internet of Things (IoT) is one of the most ground-breaking forms of technology in the modern era. Embedded systems allow engineers to augment daily life objects through the use of microcontrollers and microprocessors. The Internet of Things allows communication and exchange of data in a network of objects with embedded microprocessors, sensors, actuators, etc. This concept can be used in a variety of ways to enhance many activities by incorporating “smart” objects to difficult processes.
This project uses Bluetooth 4.2 to connect multiple Texas Instruments SensorTag sensors to a Raspberry Pi. The SensorTags are Inertial Measurement Units that can measure movement using data from in-built gyroscope, magnetometer, and accelerometer and low power microcontroller. The aim of this project is to further explore and improve the motion tracking system developed by my senior, Grace Christina. These tracking nodes can be attached to human body to track body movements for medical or recreational purposes. The data obtained by these tracking nodes are then sent to the Raspberry Pi for further processing. Multiple sensors are used to achieve higher accuracy or to calculate orientation of rotating body parts. Sensors can send data when requested by the Raspberry Pi, or can be sent automatically by the sensor to the Raspberry Pi whenever movement is detected, if notification is enabled. Unlike the previous project, I will focus mainly to improve the notification mode, as this mode is more efficient and less power consuming, which is one of the key criteria in IoT systems. This project will attempt to improve the accuracy and experiment with different ways of processing and transferring data. |
| first_indexed | 2025-02-19T03:47:40Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/74931 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:47:40Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/749312023-03-03T20:29:11Z Low power motion tracking wireless node for IoT applications Wijaya, Davin Vun Chan Hua, Nicholas School of Computer Science and Engineering DRNTU::Engineering Internet of Things (IoT) is one of the most ground-breaking forms of technology in the modern era. Embedded systems allow engineers to augment daily life objects through the use of microcontrollers and microprocessors. The Internet of Things allows communication and exchange of data in a network of objects with embedded microprocessors, sensors, actuators, etc. This concept can be used in a variety of ways to enhance many activities by incorporating “smart” objects to difficult processes. This project uses Bluetooth 4.2 to connect multiple Texas Instruments SensorTag sensors to a Raspberry Pi. The SensorTags are Inertial Measurement Units that can measure movement using data from in-built gyroscope, magnetometer, and accelerometer and low power microcontroller. The aim of this project is to further explore and improve the motion tracking system developed by my senior, Grace Christina. These tracking nodes can be attached to human body to track body movements for medical or recreational purposes. The data obtained by these tracking nodes are then sent to the Raspberry Pi for further processing. Multiple sensors are used to achieve higher accuracy or to calculate orientation of rotating body parts. Sensors can send data when requested by the Raspberry Pi, or can be sent automatically by the sensor to the Raspberry Pi whenever movement is detected, if notification is enabled. Unlike the previous project, I will focus mainly to improve the notification mode, as this mode is more efficient and less power consuming, which is one of the key criteria in IoT systems. This project will attempt to improve the accuracy and experiment with different ways of processing and transferring data. Bachelor of Engineering (Computer Engineering) 2018-05-25T01:57:12Z 2018-05-25T01:57:12Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/74931 en Nanyang Technological University 43 p. application/pdf |
| spellingShingle | DRNTU::Engineering Wijaya, Davin Low power motion tracking wireless node for IoT applications |
| title | Low power motion tracking wireless node for IoT applications |
| title_full | Low power motion tracking wireless node for IoT applications |
| title_fullStr | Low power motion tracking wireless node for IoT applications |
| title_full_unstemmed | Low power motion tracking wireless node for IoT applications |
| title_short | Low power motion tracking wireless node for IoT applications |
| title_sort | low power motion tracking wireless node for iot applications |
| topic | DRNTU::Engineering |
| url | http://hdl.handle.net/10356/74931 |
| work_keys_str_mv | AT wijayadavin lowpowermotiontrackingwirelessnodeforiotapplications |