Kedgley, A. E. | Search bioRxiv

bioRxiv2023

Uniformity of performance during the collection of maximum voluntary contraction tasks for the muscles of the forearm

Electromyographic (EMG) signals are used to gain insight into muscle activation patterns and thus neuromuscular control. To allow for comparisons between studies and participants, the EMG signal is generally normalised, with the signal obtained during a task that is designed to elicit maximum voluntary contraction (MVC) frequently used as the basis. Recommendations for how to collect MVCs have been made; however, previous studies have not been able to determine if the EMG variation noted within a population was due to different muscle activation patterns, or the tasks being performed differently, or other variables such as skin impedance, or the stochastic nature of electromyography. EMG signals were recorded during hand-wrist tasks selected to elicit MVCs in the muscles of the forearm - pull up, push down, radial pull, ulnar pull, pull, pronation, finger flexion, finger extension, and grip - as well as two activities of daily living - pouring a glass of water from a jug and turning a key in a lock. A load cell mounted to a statically mounted handle was used to record contemporaneously the forces and moments exerted by participants in pull up, push down, radial pull, ulnar pull, pull, pronation, finger flexion, and finger extension tasks. Ninety percent of tasks yielded the expected load cell outputs for the directed tasks and thus were considered as having been performed correctly. The tasks performed incorrectly were not the same for all participants, nor were they all performed by the same participants. Of note was that there were instances when a task was performed incorrectly but still an expected MVC was achieved. The EMG signals showed similar variation to that seen in previous studies. However, the applied forces and moments did not appear to explain the variation seen in the tasks that elicited MVCs. The results of this study indicate that different muscle activation patterns may be used to exert the same force by the hand. Thus, it may not be possible for a given task to elicit MVC in the same muscle in all people. However, by using several activities, MVCs for the forearm muscles may be obtained for most of the population. Beyond designing EMG protocols, the results of this study suggest that people have unique muscle activation patterns and raise questions as to whether this is a result of physiology or conditioning.

bioengineering↗

bioRxiv2022

Validation of two-dimensional video-based inference of finger kinematics with pose estimation

Accurate capture finger of movements for biomechanical assessments has typically been achieved within laboratory environments through the use of physical markers attached to a participants hands. However, such requirements can narrow the broader adoption of movement tracking for kinematic assessment outside these laboratory settings, such as in the home. Thus, there is the need for markerless hand motion capture techniques that are easy to use and accurate enough to evaluate the complex movements of the human hand. Several recent studies have validated lower-limb kinematics obtained with a marker-free technique, OpenPose. This investigation examines the accuracy of OpenPose, when applied to images from single RGB cameras, against a gold standard marker-based optical motion capture system that is commonly used for hand kinematics estimation. Participants completed four single-handed activities with right and left hands, including hand abduction and adduction, radial walking, metacarpophalangeal (MCP) joint flexion, and thumb opposition. Accuracy of finger kinematics was assessed using the root mean square error. Mean total active flexion was compared using the Bland-Altman approach, and coefficient of determination of a linear regression. Results showed good agreement for abduction and adduction and thumb opposition activities. Lower agreement between the two methods was observed for radial walking (mean difference between the methods of 5.03{degrees}) and MCP flexion (mean difference of 6.82{degrees}) activities, due to occlusion. This investigation demonstrated that OpenPose, applied to videos captured with monocular cameras, can be used for markerless motion capture for finger tracking with an error below than 11{degrees} and on the order of that which is accepted clinically. Author summaryDecreased hand mobility may limit functionality, and its quantification is fundamental to assess underlying impairments. Optical motion capture technologies are the most accurate means by which to quantify hand motion. As this approach involves placing markers on the skin and recording hand movements using multiple cameras, there are limitations of physical space, time requirements, and financial implications. Therefore, the adoption of these practices is confined to laboratory settings. In clinical settings, goniometry is used to quantify hand range of motion (ROM), but this also involves lengthy processes and requires face-to-face assessments. Alternative solutions have been investigated to quantify hand mobility remotely and support home-based care interventions. However, none has been shown to be accurate enough to replace the gold-standard measurement of hand ROM in clinical settings. Recently, markerless technologies that leverage artificial intelligence have exhibited great potential for human movement analysis, but these studies have validated markerless tracking technologies for the lower limb only. We demonstrate that the validity of these models can be extended to capture hand mobility, making it also possible to assess hand function remotely.

bioengineering↗