How surface electromyography (sEMG) can improve biomechanical risk assessment

Introduction
When assessing the risk of biomechanical overload we can take approaches on various levels, from purely subjective findings to observational methods, and direct measurements. The many tools for recording subjecting findings and/or observations offer the advantage of being easy to use and generally not expensive, but they also suffer from not being fully reproducible, sensitive and accurate.

Methods
Electromyography gives a picture of the “timing” of activation of different muscles by measuring the “onset” and “offset” of activity. How the phases of activity are related can be studied, to examine muscle synergies and agonism and antagonism. Much useful information can be obtained from studying the activation phases in relation to different postures and workstations, or within a cycle.
The amplitude of muscle activity can now be quantified highly precisely with standard normalization procedures.

Results
Looking at some of the tools currently employed in occupational medicine and ergonomics, often observational information on muscle activity is related to EMG activity expressed as a percentage of the maximum voluntary contraction (MVC). This is the case, for instance, for the Garg and Moore Strain Index and the Hand Activity Level proposed by the ACGIH. This latter involves the assessor recording the levels of MCV in 10% intervals. This degree of sensitivity certainly calls for the use of sEMG rather than just observation. Here we provide examples of how sEMG can be used to assess the risk of biomechanical overload in occupational settings. Another interesting possibility in occupational medicine is the use of the sEMG signal to assess muscle fatigue with the modern instruments for signal frequency analysis; earlier limitations have been overcome and dynamic assessment can now be done.

Discussion
Discussed here are some possibilities and prospects for the use of electromyography in assessing the risk of biomechanical overload and studying electrophysiological indicators of muscle fatigue.