Wrist Posture Estimation Differences and Reliability Between Video Analysis and Electrogoniometer Methods

Biomechanical Assessments of the Upper Limb for Determining Fatigue, Strain and Effort from the Laboratory to the Industrial Working Place: A Systematic Review

Recent human-centered developments in the industrial field (Industry 5.0) lead companies and stakeholders to ensure the wellbeing of their workers with assessments of upper limb performance in the workplace, with the aim of reducing work-related diseases and improving awareness of the physical status of workers, by assessing motor performance, fatigue, strain and effort. Such approaches are usually developed in laboratories and only at times they are translated to on-field applications; few studies summarized common practices for the assessments. Therefore, our aim is to review the current state-of-the-art approaches used for the assessment of fatigue, strain and effort in working scenarios and to analyze in detail the differences between studies that take place in the laboratory and in the workplace, in order to give insights on future trends and directions. A systematic review of the studies aimed at evaluating the motor performance, fatigue, strain and effort of the upper limb targeting working scenarios is presented. A total of 1375 articles were found in scientific databases and 288 were analyzed. About half of the scientific articles are focused on laboratory pilot studies investigating effort and fatigue in laboratories, while the other half are set in working places. Our results showed that assessing upper limb biomechanics is quite common in the field, but it is mostly performed with instrumental assessments in laboratory studies, while questionnaires and scales are preferred in working places. Future directions may be oriented towards multi-domain approaches able to exploit the potential of combined analyses, exploitation of instrumental approaches in workplace, targeting a wider range of people and implementing more structured trials to translate pilot studies to real practice.

Predicting Wrist Posture during Occupational Tasks Using Inertial Sensors and Convolutional Neural Networks

Current methods for ergonomic assessment often use video-analysis to estimate wrist postures during occupational tasks. Wearable sensing and machine learning have the potential to automate this tedious task, and in doing so greatly extend the amount of data available to clinicians and researchers. A method of predicting wrist posture from inertial measurement units placed on the wrist and hand via a deep convolutional neural network has been developed. This study has quantified the accuracy and reliability of the postures predicted by this system relative to the gold standard of optoelectronic motion capture. Ten participants performed 3 different simulated occupational tasks on 2 occasions while wearing inertial measurement units on the hand and wrist. Data from the occupational task recordings were used to train a convolutional neural network classifier to estimate wrist posture in flexion/extension, and radial/ulnar deviation. The model was trained and tested in a leave-one-out cross validation format. Agreement between the proposed system and optoelectronic motion capture was 65% with κ = 0.41 in flexion/extension and 60% with κ = 0.48 in radial/ulnar deviation. The proposed system can predict wrist posture in flexion/extension and radial/ulnar deviation with accuracy and reliability congruent with published values for human estimators. This system can estimate wrist posture during occupational tasks in a small fraction of the time it takes a human to perform the same task. This offers opportunity to expand the capabilities of practitioners by eliminating the tedium of manual postural assessment.

Accuracy of a Low-Cost 3D-Printed Wearable Goniometer for Measuring Wrist Motion

Wrist motion provides an important metric for disease monitoring and occupational risk assessment. The collection of wrist kinematics in occupational or other real-world environments could augment traditional observational or video-analysis based assessment. We have developed a low-cost 3D printed wearable device, capable of being produced on consumer grade desktop 3D printers. Here we present a preliminary validation of the device against a gold standard optical motion capture system. Data were collected from 10 participants performing a static angle matching task while seated at a desk. The wearable device output was significantly correlated with the optical motion capture system yielding a coefficient of determination (R2) of 0.991 and 0.972 for flexion/extension (FE) and radial/ulnar deviation (RUD) respectively (p < 0.0001). Error was similarly low with a root mean squared error of 4.9° (FE) and 3.9° (RUD). Agreement between the two systems was quantified using Bland–Altman analysis, with bias and 95% limits of agreement of 3.1° ± 7.4° and −0.16° ± 7.7° for FE and RUD, respectively. These results compare favourably with current methods for occupational assessment, suggesting strong potential for field implementation.