Using a Single inertial measurement unit to measure the knee flexion angle at touchdown in side cutting movements

Joint angles can be an important measure in investigating sport-related injuries as well as in rehabilitation studies. Even though previous researchers have investigated using inertial measurement units to calculate joint angles, only a few studies can be found that were conducted using a single inertial measurement unit (IMU) to measure the joint angles. Furthermore, there have been a few previous studies that used a single IMU sensor to monitor the behavior of the knee joint angle for specific rehabilitation exercises, but not for more complex sports-related movements like side cutting. Hence, this study focused on validating a single IMU sensor and identifying the best placement to position it between the shank and thigh to measure the knee flexion angle at touchdown during side cutting. Fifty subjects were employed for this study and each person was asked to perform 60 leftwards side cuts. The first 20 side cuts were at 90 degrees, the second 20 were at 45 degrees, and the rest were at various angles between the running line and 90 degrees. Data were collected using 12 IMU sensors placed on the right thigh and shank, as well as a Vicon video motion capture system. Random Forest Regression and Gradient Boosting Regression machine learning algorithms were applied to the IMU data. According to the results, the thigh is the best location for a single IMU sensor, and the knee flexion angle at touchdown can be measured with an IMU sensor placed on the thigh, with a mean Coefficient of determination of 0.91 (SD = 0.01), mean absolute error of 1.93 (SD = 0.12), and mean absolute percentage error of 0.08 (SD = 0.06). The results of this study show that a single IMU sensor can be used to successfully measure knee flexion angles at touchdown during side cutting. This finding could aid in the development of more compact and convenient IMU-based joint angle measurement devices in the future.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Kinesiology

Grantor dc:publisher.institution

Kinesiology

Year dc:date.issued

2022

Author dc:creator

• Malagalage Don, Ishan Chathuranga

Advisor dc:contributor.advisor

• Stefanyshyn, Darren

Committee members dc:contributor.committeemember

• Edwards, Brent
• Ferber, Reed
• Yanushkevich, Svetlana

• ACL
• Injury
• ACL Injury
• Single IMU
• side cutting
• anterior cruciate ligament
• ACL injury prediction
• Wearable Technology
• electronic textiles
• machine learning
• knee flexion angle
• touchdown
• video motion capture
• Vicon
• Python
• inertial measurement unit
• Joint Angle
• Knee
• Mixture of experts

Statement dc:rights

• University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.

Language dc:language.iso

eng

DOI

https://dx.doi.org/10.11575/PRISM/40305

OAI identifier oai:identifier

oai:ucalgary.scholaris.ca:1880/115299