IoT based IMU sensor network for human arm pose estimation / Jahangir Hassan Khan

Jahangir , Hassan Khan (2019) IoT based IMU sensor network for human arm pose estimation / Jahangir Hassan Khan. Masters thesis, Universiti Malaya.

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Abstract

Human arm pose estimation or tracking is becoming an increasing sought after field of research due to the multitudes of uses it offers. With the start of Industry 4.0, Internet of Things (IoT) concept has made wireless data transfer into reality and hence, remote monitoring and control can be performed relatively inexpensive and portable. The applications can range from Virtual Reality gaming to training industrial robots as well as controlling surgical robots and stroke rehabilitation. For example: In the past, patients had to go periodically for rehabilitation to clinics and hospitals, can now do those same exercises at home using sensors that can send progress data over the internet to their doctors who can analyse the data and provide feedback. However, this technology is still not well-established and thus requires more research. This motivates the current study to work with this research topic. The objective of this project is to design and build an IoT based sensor system that can send its data wirelessly to a computer, which can then analyse the data and estimate the position of the human arm at remote location. The sensor used in this project is the BNO055 Inertial Measurement Unit, which is a low cost sensor incorporating a gyroscope, accelerometer and magnetometer to provide orientation and acceleration data. The medium for wireless data transfer is a Wi-Fi enabled Node MCU micro-controller. The sensor node will transfer data to a computer via Wi-Fi using a custom designed network protocol. A multi-threaded program running on the computer will perform Forward Kinematics on the wirelessly transferred data using an arm model created using the Denavit-Hartenberg convention. The Forward Kinematics approach has great advantage such as being easy to program and its faster computational speed, as compared to the conventional arm pose estimation algorithms. By using the developed arm pose tracking program, several important parameters such as positions of the elbow and wrist with reference to the shoulder joint, moving distance, angular movement speed and movement pattern of arm are successfully measured and estimated. The results of this research show that for basic movements such as shoulder flexion, contraction or elbow flexion and contraction, the accuracy of tracking distance is more than 80 percent. For complicated movements like tracing shapes the accuracy ranges from 60 to 70 percent. For the purpose of tracking arm movements of stroke patients, the accuracy is adequate since the arm movement exercises for stroke rehabilitation consist of basic arm movements for which the proposed design has high accuracy. The proposed system also has high enough sampling and transmission frequency to accurately track fast arm movements which makes it ideal for the purpose of arm position estimation.

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