Abstract
Knowledge of hand grip force is important in fields such as biomechanics, bioengineering, and ergonomics to measure the loads placed on the musculoskeletal system for assessing and reducing risk of injury and developing hands-free control systems for assistive devices. Difficulties in measuring and predicting grip force in occupational and daily situations have led researchers to explore indirect techniques for these purposes. Muscle electromyography (EMG) and more recently muscle-tendon dimensional changes have been investigated as indirect and non-invasive methods to evaluate hand grip force. This thesis proposes forearm surface dimensional changes (FDSC) as an alternative and novel indirect and non-invasive technique to measure hand grip force. However, practical applications of EMG signal or FSDC rely on the reliability of the measures and the ability to discriminate small changes in hand grip force.
The aim of this study was to evaluate the potential of forearm surface EMG of hand and finger flexor muscles and anterior forearm surface dimensional changes for indirect measurement of hand grip force. The study established their predictive relationships and reliability and determined which muscle’s EMG signals and which surface locations on the forearm are best at differentiating varying grip forces and what level of grip force can be discriminated by EMG signals or FSDC. Furthermore, the study established intra-session and inter-day relative and absolute reliability of the measures for both EMG signals and FSDC in varying grip forces.
The results of the study showed both linear and non-linear relationships between normalized root mean square (RMS) of EMG signals of different flexors and grip force and also between FSDC and grip force for different sites on the forearm. However, non-linear relationships could be approximated as linear for small to moderate forces (grip force < 50%). The study also showed that EMG can distinguish varying grip forces between 0% and 80% MVC. FSDC, on the other hand, were able to discriminate forces less than 50% MVC.
Relative reliability of EMG of flexor muscles and FSDC showed moderate and high intra-session intraclass correlation coefficients (ICCs) for single and averaged measures respectively. Inter-day ICCs were considerably lower than intra-session ICCs showing poor and moderate relative reliability for single and averaged measures respectively. The results didn’t show any specific trend between the relative reliability and grip force or muscle or site on the forearm surface and were similar for both forearm EMG and surface shape changes.
Absolute reliability of the measures, represented by standard error of measurement (SEm), increased with increasing grip force for EMG signals, indicating the dependency of EMG reliability on grip force. Conversely, SEm for FSDC was constant with no specific trend identified between SEm and increasing grip force. Normalized SEm, however, decreased with increasing grip force for both EMG and surface changes of the forearm as the magnitude of the respective measures increased with grip force. Importantly the normalized SEm for EMG was consistently lower than that of surface shape changes across all grip forces, indicating a smaller within subject error relative to the magnitude of the measures. The improved normalized SEm of EMG is reflective of the better ability to discriminate between grip forces and higher coefficients of determination (R2) of the regression relationships to grip force when compared to surface shape changes.
Overall, the study suggests the potential of both forearm surface EMG and FSDC as indirect and non-invasive methods to evaluate grip force. EMG showed stronger relationships with grip force as well as higher ability in distinguishing varying levels of force. For improved results for FSDC, improved reliability of measurement is required due to the small magnitude of the changes. This study has shown strength and weaknesses in both methods and further study is required into instrumentation, the influence of wrist movement and the application to specific populations. These further studies will help fulfill the potential of forearm surface EMG and FSDC in predicting grip force hand grip force to measure the loads on the musculoskeletal system in the biomechanical modeling, developing hands-free control systems for assistive devices, and predicting grip force for evaluating human-machine interface in ergonomics.