Novel methods in SEMG-Force estimation

Abstract

An accurate determination of muscle force is desired in many applications in different fields such as ergonomics, sports medicine, prosthetics, human-robot interactionand medical rehabilitation. Since individual muscle forces cannot be directly measured,force estimation using recorded electromyographic (EMG) signals has beenextensively studied. This usually involves interpretation and analysis of the recordedEMG to estimate the underlying neuromuscular activity which is related to the forceproduced by the muscle. Although invasive needle electrode EMG recordings haveprovided substantial information about neuromuscular activity at the motor unit(MU) level, there is a risk of discomfort, injury and infection. Thus, non-invasivemethods are preferred and surface EMG (SEMG) recording is widely used. However,physiological and non-physiological factors, including phase cancelation, tissue filtering,cross-talk from other muscles and non-optimal electrode placement, affect theaccuracy of SEMG-based force estimation. In addition, the relative movement of themuscle bulk and the innervation zone (IZ) with respect to the electrode attached tothe skin are two major challenges to overcome in force estimation during dynamiccontractions.

The objective of this work is to improve the accuracy of SEMG-based force estimationunder static conditions, and devise methods that can be applied to forceestimation under dynamic conditions. To achieve this objective, a novel calibrationtechnique is proposed, which corrects for variations in the SEMG with changing jointangle. In addition, a modeling technique, namely parallel cascade identification (PCI)that can deal with non-linearities and dynamics in the SEMG-force relationship is appliedto the force estimation problem. Finally, a novel integrated sensor that sensesboth SEMG and surface muscle pressure (SMP) is developed and the two signalmodalities are used as input to a force prediction model.

The experimental results show significant improvement in force prediction usingdata calibrated with the proposed calibration method, compared to using noncalibrateddata. Joint angle dependency and the sensitivity to the location of thesensor in the SEMG-force relationship is reduced with calibration. The SEMG-forceestimation error, averaged over all subjects, is reduced by 44% for PCI modelingcompared to another modeling technique (fast orthogonalsearch) applied to the same dataset. Significantly improved force estimation results are also achieved for dynamiccontractions when joint angle based calibration and PCI are combined. Using SMPin addition to SEMG leads to significantly better force estimation compared to using
only SEMG signals.

The proposed methods have the potential to be combined and used to obtainbetter force estimation in more complicated dynamic contractions and for applications such as improved control of remote robotic systems or powered prosthetic limbs.

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