To determine the applications of machine learning (ML) techniques used for the primary prevention of work-related musculoskeletal disorders (WMSDs), a scoping review was conducted using seven literature databases. Of the 4,639 initial results, 130 primary research studies were deemed relevant for inclusion. Studies were reviewed and classified as a contribution to one of six steps within the primary WMSD prevention research framework by van der Beek et al. (2017). ML techniques provided the greatest contributions to the development of interventions (48 studies), followed by risk factor identification (33 studies), underlying mechanisms (29 studies), incidence of WMSDs (14 studies), evaluation of interventions (6 studies), and implementation of effective interventions (0 studies). Nearly a quarter (23.8%) of all included studies were published in 2020. These findings provide insight into the breadth of ML techniques used for primary WMSD prevention and can help identify areas for future research and development.

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.

The purpose of this work was to develop an open-source deep learning-based algorithm for motion capture marker labelling that can be trained on measured or simulated marker trajectories. In the proposed algorithm, a deep neural network including recurrent layers is trained on measured or simulated marker trajectories. Labels are assigned to markers using the Hungarian algorithm and a predefined generic marker set is used to identify and correct mislabeled markers. The algorithm was first trained and tested on measured motion capture data. Then, the algorithm was trained on simulated trajectories and tested on data that included movements not contained in the simulated data set. The ability to improve accuracy using transfer learning to update the neural network weights based on labelled motion capture data was assessed. The effect of occluded and extraneous markers on labelling accuracy was also examined. Labelling accuracy was 99.6% when trained on measured data and 92.8% when trained on simulated trajectories, but could be improved to up to 98.8% through transfer learning. Missing or extraneous markers reduced labelling accuracy, but results were comparable to commercial software. The proposed labelling algorithm can be used to accurately label motion capture data in the presence of missing and extraneous markers and accuracy can be improved as data are collected, labelled, and added to the training set. The algorithm and user interface can reduce the time and manual effort required to label optical motion capture data, particularly for those with limited access to commercial software.

Trochlear groove geometry and the location of the tibial tubercle, where the patellar tendon inserts, have both been associated with patellofemoral instability and can be modified surgically. Although their effects on patellofemoral biomechanics have been investigated individually, the interaction between the two is unclear. The authors’ aim was to use statistical shape modeling and musculoskeletal simulation to examine the effect of patellofemoral geometry on the relationship between tibial tubercle location and patellofemoral function. A statistical shape model was used to generate new knee geometries with trochlear grooves ranging from shallow to deep. A Monte Carlo approach was used to create 750 knee models by randomly selecting a geometry and randomly translating the tibial tubercle medially/laterally and anteriorly. Each knee model was incorporated into a musculoskeletal model, and an overground walking trial was simulated. Knees with shallow trochlear geometry were more sensitive to tubercle medialization with greater changes in lateral patella position (−3.0 mm/cm medialization shallow vs −0.6 mm/cm deep) and cartilage contact pressure (−0.51 MPa/cm medialization shallow vs 0.04 MPa/cm deep). However, knees with deep trochlear geometry experienced greater increases in medial cartilage contact pressure with medialization. This modeling framework has the potential to aid in surgical decision making.

The geometry of the patellofemoral joint affects function and pathology. However, the impact of trochlear groove depth on treatments for patellar instability and pain is not clear. Tibial tubercle osteotomy is a common surgical intervention for patellar instability where the tibial insertion of the patellar tendon (PT) is translated to align the extensor mechanism and stabilize the joint. The aim of this work was to investigate the interaction between trochlear groove depth and PT insertion and their effect on patellar stability. Patellofemoral geometry was modified based on a statistical shape model to create knees with a range of trochlear groove depths. A Monte Carlo approach was used and 750 instances of a musculoskeletal model were generated with varying geometry and anterior and medial transfer of the PT. Stability was assessed by applying a lateral perturbation force to the patella during simulation of overground walking. In knees with deep trochlear grooves, a medialized PT increased stability. However, in knees with shallow trochlear grooves, stability was maximized for tendon insertion ∼1 mm medial to its neutral location. This PT insertion also corresponded to the best alignment of the patella in the trochlear groove in these knees, indicating that good alignment may be important to maximizing stability. Anterior PT transfer had minimal effect on stability for all geometries. A better understanding of the effects of articular geometry and tubercle location on stability may aid clinicians in patient-specific surgical planning.