Abstract
We estimated lower limb sagittal plane joint moments during treadmill running using wearable sensors and different commonly used locations. We compared outcomes from supervised recurrent neural network machine learning (ML) models to criterion values from motion capture and inverse dynamics. The normalised root mean squared error between outcomes from the ML model fed with the entire wearable dataset (pressure insoles and inertial measurement units at the foot, wrist, T10, and sacrum) was 8.9%, 13.5%, and 18.2% for the ankle, knee, and hip joint respectively. Removal of any two upper body sensors did not decrease the accuracy of the estimations. This work is a springboard to providing biomechanical feedback to runners to help improve performance and minimise injury risk.
Original language | English |
---|---|
Title of host publication | ISBS Proceedings Archives |
Subtitle of host publication | 42nd International Conference on Biomechanics in Sports (2024) Salzburg, Austria, July 15-19, 2024 |
Publisher | International Society of Biomechanics in Sports (ISBS) |
Number of pages | 4 |
Publication status | Acceptance date - 5 Mar 2024 |
Event | International Society of Biomechanics in Sports Conference - Salzburg, Austria Duration: 15 Jul 2024 → 19 Jul 2024 Conference number: 42 https://www.isbs2024.com/ |
Conference
Conference | International Society of Biomechanics in Sports Conference |
---|---|
Abbreviated title | ISBS 20024 |
Country/Territory | Austria |
City | Salzburg |
Period | 15/07/24 → 19/07/24 |
Internet address |