Validation of IMU against optical reference and development of open-source pipeline: proof of concept case report in a participant with 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 a case report with participants with transfemoral amputation fitted with a Percutaneous Osseointegrated Implant (POI) and without amputation walking over level ground. We hypothesis that by using this novel pipeline, we can validate IMU motion capture data, to a clinically acceptable degree.
Results: Average RMSE (across all joints) between the two systems from the participant with a unilateral transfemoral amputation (TFA) on the amputated and the intact sides were 2.35° (IQR = 1.45°) and 3.59° (IQR = 2.00°) respectively. Equivalent results in the non-amputated participant 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° in the non-amputated participant. 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) in the non-amputated participant. Coefficients of Multiple Correlation (CMC) results between the two systems in the TFA ranged from 0.74 to > 0.99 and from 0.72 to > 0.99 in the non-amputated participant 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% in the non-amputated participant.
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 and have proved our hypothesis.

Orientation estimation algorithm

Motion analysis

Gait analysis

Transfemoral amputation gait

Joint kinematics

IMU motion capture

Prosthetic gait

Inertial measurement unit

Motion capture validation

Osseointegration

Author

Kirstin Ahmed

Chalmers, Electrical Engineering, Systems and control

Shayan Taheri

Aalto University

Ive Weygers

University of Erlangen-Nuremberg (FAU)

Max Jair Ortiz Catalan

Prometei Pain Rehabilitation Center

Journal of NeuroEngineering and Rehabilitation

17430003 (eISSN)

Vol. 21 1 128

Areas of Advance

Health Engineering

Subject Categories

Orthopedics

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1186/s12984-024-01426-6

PubMed

39085954

More information

Latest update

8/9/2024 6