Purdue University Graduate School

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posted on 2024-06-28, 18:47 authored by Ravi Abhishek ShankarRavi Abhishek Shankar

There is growing research in analyzing human gait in the context of various applications. This has been aided by the improvement in sensing technologies and computation power. A complex motor skill that it is, gait has found its use in medicine for diagnosing different neurological ailments and injuries. In sports, gait can be used to provide feedback to the player/athlete to improve his/her skill and to prevent injuries. In biometrics, gait can be used to identify and authenticate individuals. This can be easier to scale to perform biometrics of individuals in large crowds compared to conventional biometric methods. In the field of Human Computer Interaction (HCI), gait can be an additional input that could be provided to be used in applications such as video games. Gait analysis has also been used for Human Activity Recognition (HAR) for purposes such as personal fitness, elderly care and rehabilitation.

The current state-of-the-art methods for gait analysis involves non-wearable technology due to its superior performance. The sophistication afforded in non-wearable technologies, such as cameras, is better able to capture gait information as compared to wearables. However, non-wearable systems are expensive, not scalable and typically, inaccessible to the general public. These systems sometimes need to be set up in specialized clinical facilities by experts. On the other hand, wearables offer scalability and convenience but are not able to match the performance of non-wearables. So the current work is a step in the direction to bridge the gap between the performance of non-wearable systems and the convenience of wearables.

A magnetic tracking system is developed to be applied for gait analysis. The system performs position and orientation tracking, i.e. 6 degrees of freedom or 6DoF tracking. One or more tracker modules, called Rx modules, is tracked with respect to a module called the Tx module. The Tx module mainly consists of a magnetic field generating coil, Inertial Measurement Unit (IMU) and magnetometer. The Rx module mainly consists of a tri-axis sensing coil, IMU and magnetometer. The system is minimally intrusive, works with Non-Line-of-Sight (NLoS) condition, low power consuming, compact and light weight.

The magnetic tracking system has been applied to the task of Human Activity Recognition (HAR) in this work as a proof-of-concept. The tracking system was worn by participants, and 4 activities - walking, walking with weight, marching and jogging - were performed. The Tx module was worn on the waist and the Rx modules were placed on the feet. To compare magnetic tracking with the most commonly used wearable sensors - IMUs + magnetometer - the same system was used to provide IMU and magnetometer data for the same 4 activities. The gait data was processed by 2 commonly used deep learning models - Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The magnetic tracking system shows an overall accuracy of 92\% compared to 86.69\% of the IMU + magnetometer system. Moreover, an accuracy improvement of 8\% is seen with the magnetic tracking system in differentiating between the walking and walking with weight activities, which are very similar in nature. This goes to show the improvement in gait information that 6DoF tracking brings, that manifests as increased classification accuracy. This increase in gait information will have a profound impact in other applications of gait analysis as well.


Degree Type

  • Doctor of Philosophy


  • Electrical and Computer Engineering

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Byunghoo Jung

Additional Committee Member 2

Kaushik Roy

Additional Committee Member 3

Vijay Raghunathan

Additional Committee Member 4

Shreyas Sen

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