Validation of an IMU Suit for Military-Based Tasks

Abstract

Investigating the effects of load carriage on military soldiers using optical motion capture is challenging. However, inertial measurement units (IMUs) provide a promising alternative. Our purpose was to compare optical motion capture with an Xsens IMU system in terms of movement reconstruction using principal component analysis (PCA) using correlation coefficients and joint kinematics using root mean squared error (RMSE). Eighteen civilians performed military-type movements while their motion was recorded using both optical and IMU-based systems. Tasks included walking, running, and transitioning between running, kneeling, and prone positions. PCA was applied to both the optical and virtual IMU markers, and the correlations between the principal component (PC) scores were assessed. Full-body joint angles were calculated and compared using RMSE between optical markers, IMU data, and virtual markers generated from IMU data with and without coordinate system alignment. There was good agreement in movement reconstruction using PCA; the average correlation coefficient was 0.81 ± 0.14. RMSE values between the optical markers and IMU data for flexion-extension were less than 9°, and 15° for the lower and upper limbs, respectively, across all tasks. The underlying biomechanical model and associated coordinate systems appear to influence RMSE values the most. The IMU system appears appropriate for capturing and reconstructing full-body motion variability for military-based movements.

Rehabilitation and Return to Sport Assessment after Anterior Cruciate Ligament Injury: Quantifying Joint Kinematics during Complex High-Speed Tasks through Wearable Sensors

“…Therefore, the WIS system used in the present study (MTw Awinda, Xsens) resulted in a suitable solution for motion capture in the sports environment for the biomechanical assessment of ACL rehabilitation. The assessment of wearables' accuracy before experimental applications has been recently advocated in specific applications such as outdoor sport and the military [19,47]. The present study, alongside all the limitations of the OMB system in outdoor use, endorses the use of WIS in ACL rehabilitation during on-field assessments.…”

“…Furthermore, the accuracy of the WIS system on knee and ankle frontal and transverse plane angles was flawed by the high-and probably non-physiological-values of the OMB systems. This is reported in the literature as an intrinsic limitation of the OMB biomechanical model and protocol selected for the study, which, on the other hand, were selected for their ease of use [18,47]. Lastly, a single full-body configuration was adopted for the WIS system.…”

Development and Validation of a Deep Learning Algorithm and Open-Source Platform for the Automatic Labelling of Motion Capture Markers

“…Data Set B consisted of 18 participants performing the same athletic movements listed above as well as a series of functional and transitional movements including three trials of running, one of walking, and three trials each of kneel-to-prone, kneel-to-run, prone-to-run, prone-to-kneel, run-to-prone, and run-tokneel movements (Mavor et al, 2020). For the walking trial, participants walked in a criss-cross pattern to cover the capture volume.…”

Development and Validation of a Deep Learning Algorithm and Open-Source Platform for the Automatic Labelling of Motion Capture Markers

“…Data Set B consisted of 18 participants performing the same athletic movements listed above as well as a series of functional and transitional movements including three trials of running, one of walking, and three trials each of kneel-toprone, kneel-to-run, prone-to-run, prone-to-kneel, run-toprone, and run-to-kneel movements [13]. For the walking trial, participants walked in a criss-cross pattern to cover the capture volume.…”