Therefore, the main activity of ergonomics is the estimation of physical damage and physiological implications. JAK To prevent MSD, recognizing the load placed on individual muscles, ligaments, and every part is needed. Using EMG-force estimation, the over-loaded parts are recognized; thus providing the possibility to train people for its removal.[20] The torque signal derived from EMG has
also applications in rehabilitation. For example, muscle activation and movement patterns would be altered in individuals following stroke. If affected muscles and their contribution to the pathological pattern are known using EMG-force model, it might be possible to develop more effective rehabilitation therapies and to assess the effect of an intervention and to achieve better motion. These EMG-driven biomechanical models use EMG as inputs rather than trying to understand how muscles are activated in a given movement.[21,22] Related Works Since the time of Inman and Ralston and Lippold in 1952, the shape of the relationship
between surface EMG and muscle force has been studied.[4] One of the major studies in this area was performed by Clancy and Hogan in 1997.[23] They used EMG of flexor and extensor muscle groups and limited the model relationship between muscle group torque contribution and EMG amplitude to be the sum of the basic functions with a linear dependence on a set of tunable parameters. In their work, various degrees of polynomials
were used. In this situation, the problem of finding parameters became a linear least squares (LSs) problem. They also applied single-/multiple-channel and unwhitened/whitened/adaptively-whitened[24] EMG amplitude processors to study their effects. They could improve the torque estimation by different strategies such as using temporal whitening of EMG waveforms, combination of multiple EMG waveforms that improved the EMG amplitude estimation, and finally using agonist-antagonist co-contraction model in a wide range of torques. Accordingly, multi-channel adaptively-whitened processor with the 3rd degree polynomial was determined as the best approximator. Another model was presented by Hoozemans and Van Deen in 2004[25] to predict handgrip forces using surface EMG of six forearm muscles. They Cilengitide used multiple linear regression (MLR) models for this prediction. Although promising, the conditions of using MLR and the validity criteria of the results could not be usually met in other real-world applications. Normality and homoscedasticity are two standard assumptions of regression diagnostics and model evaluation that must be met when using MLR. In 2012, Clancy et al. investigated the relationship between EMG signals of biceps and triceps brachii and elbow torque, using linear and nonlinear dynamic model, different types of EMG amplitude processors, and advanced system identification techniques.