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Jeremy Mogk, Peter J Keir
Occupational exposure is typically assessed by measuring forces and body postures to infer muscular loading. Better understanding of workplace muscle activity levels would aid in indicating which muscles may be at risk for overexertion and injury. However, electromyography collection in the workplace is often not practical. Therefore, a set of equations was developed and validated using data from two separate days to predict forearm muscle activity (involving six wrist and finger muscles) from grip force and posture of the wrist (flexed, neutral and extended) and forearm (pronated, neutral, supinated). The error in predicting activation levels of each forearm muscle across the range of grip forces, using the first day data (root mean square error; RMSEmodel), ranged from 8.9% maximal voluntary electrical activation (MVE) (flexor carpi radialis) to 11% MVE (extensor digitorum communis). Grip force was the main contributor to predicting muscle activity levels, explaining over 70% of the variance in flexor activation levels and up to 60% in extensor activation levels, respectively. Inclusion of gender as a variable in the model improved estimates of flexor but not extensor activity. While posture itself explained minimal variance in activation without grip force (510% MVE), wrist and forearm posture were required (with grip force) to explain over 70% of the variance of all six muscles. The validation process indicated good day-to-day reliability of each equation, with similar error for flexor muscle models but slightly higher error in the extensor models when predicting activity levels for the second day of data (RMSEvalid ranging from 8.9% to 12.7% MVE). Detailed error analysis during validation revealed that inclusion of posture in the model effectively decreased error at grip forces above 25% maximum, but was detrimental at very low grip forces. This study presents a potential new tool to estimate forearm muscle loading in the workplace using grip force and posture, as a surrogate to use of a complex biomechanical model.
Computational anatomy incorporates the use of geometric- and statistics-based mathematical techniques to analyze and understand the variation in human form and structure. Biomechanics represents one family of methods by which we can evaluate and understand the biological design of humans -- specifically, the relationship between form and function -- within the larger contexts of physical abilities and behaviour.