Validation of IMU against optical reference and development of an open source pipeline: Proof of concept case report in transfemoral amputation fitted with a Percutaneous Osseointegrated Implant
Journal article, 2024
Background
Systems that capture motion under laboratory conditions limit validity in real-world environments. Mobile motion capture solutions such as Inertial Measurement Units (IMUs) can progress our understanding of "real" human movement. IMU data must be validated in each application to interpret with clinical applicability; this is particularly true for diverse populations. Our IMU analysis method builds on the OpenSim IMU Inverse Kinematics toolkit integrating the Versatile Quaternion-based Filter and incorporates realistic constraints to the underlying biomechanical model. We validate our processing method against the reference standard optical motion capture in participants with transfemoral amputation fitted with a Percutaneous Osseointegrated Implant (POI) and without amputation.
Results
Average RMSE between the two systems from the amputated participant (TFA) on the amputated and the intact sides were 2.35 ° (IQR = 1.45 °) and 3.59 ° (IQR = 2.00 °) respectively. Equivalent results without amputation (WA) were 2.26 ° (IQR = 1.08 °). Joint level average RMSE between the two systems from the TFA ranged from 1.66 ° to 3.82 ° and from 1.21 ° to 5.46 ° WA. In plane average RMSE between the two systems from the TFA ranged from 2.17 ° (coronal) to 3.91 ° (sagittal) and from 1.96 ° (transverse) to 2.32 ° (sagittal) WA. CMC results between the two systems in the TFA ranged from 0.74 to > 0.99 and from 0.72 to > 0.99 WA and resulted in ‘excellent’ similarity in each data set average, in every plane and at all joint levels. Normalized RMSE between the two systems from the TFA ranged from 3.40 % (knee level) to 54.54 % (pelvis level) and from 2.18 % to 36.01 % WA.
Conclusions
We offer a modular processing pipeline that enables the addition of extra layers, facilitates changes to the underlying biomechanical model, and can accept raw IMU data from any vendor. We successfully validate the pipeline using data, for the first time, from a TFA participant using a POI. We have proved our hypothesis that by using this novel pipeline we can validate IMU motion capture data, to a clinically acceptable degree.
Systems that capture motion under laboratory conditions limit validity in real-world environments. Mobile motion capture solutions such as Inertial Measurement Units (IMUs) can progress our understanding of "real" human movement. IMU data must be validated in each application to interpret with clinical applicability; this is particularly true for diverse populations. Our IMU analysis method builds on the OpenSim IMU Inverse Kinematics toolkit integrating the Versatile Quaternion-based Filter and incorporates realistic constraints to the underlying biomechanical model. We validate our processing method against the reference standard optical motion capture in participants with transfemoral amputation fitted with a Percutaneous Osseointegrated Implant (POI) and without amputation.
Results
Average RMSE between the two systems from the amputated participant (TFA) on the amputated and the intact sides were 2.35 ° (IQR = 1.45 °) and 3.59 ° (IQR = 2.00 °) respectively. Equivalent results without amputation (WA) were 2.26 ° (IQR = 1.08 °). Joint level average RMSE between the two systems from the TFA ranged from 1.66 ° to 3.82 ° and from 1.21 ° to 5.46 ° WA. In plane average RMSE between the two systems from the TFA ranged from 2.17 ° (coronal) to 3.91 ° (sagittal) and from 1.96 ° (transverse) to 2.32 ° (sagittal) WA. CMC results between the two systems in the TFA ranged from 0.74 to > 0.99 and from 0.72 to > 0.99 WA and resulted in ‘excellent’ similarity in each data set average, in every plane and at all joint levels. Normalized RMSE between the two systems from the TFA ranged from 3.40 % (knee level) to 54.54 % (pelvis level) and from 2.18 % to 36.01 % WA.
Conclusions
We offer a modular processing pipeline that enables the addition of extra layers, facilitates changes to the underlying biomechanical model, and can accept raw IMU data from any vendor. We successfully validate the pipeline using data, for the first time, from a TFA participant using a POI. We have proved our hypothesis that by using this novel pipeline we can validate IMU motion capture data, to a clinically acceptable degree.
Author
Kirstin Ahmed
Chalmers, Electrical Engineering, Systems and control
Mohammad Javad Taheri
Chalmers, Electrical Engineering, Systems and control
Ive Weygers
University of Erlangen-Nuremberg (FAU)
Max Jair Ortiz Catalan
Chalmers, Electrical Engineering, Systems and control
Journal of NeuroEngineering and Rehabilitation
17430003 (eISSN)
Subject Categories
Orthopedics