Evaluation of methods for extraction of the volitional EMG in dynamic hybrid muscle activation

BackgroundHybrid muscle activation is a modality used for muscle force enhancement, in which muscle contraction is generated from two different excitation sources: volitional and external, by means of electrical stimulation (ES). Under hybrid activation, the overall EMG signal is the combination of the volitional and ES-induced components. In this study, we developed a computational scheme to extract the volitional EMG envelope from the overall dynamic EMG signal, to serve as an input signal for control purposes, and for evaluation of muscle forces.MethodsA "synthetic" database was created from in-vivo experiments on the Tibialis Anterior of the right foot to emulate hybrid EMG signals, including the volitional and induced components. The database was used to evaluate the results obtained from six signal processing schemes, including seven different modules for filtration, rectification and ES component removal. The schemes differed from each other by their module combinations, as follows: blocking window only, comb filter only, blocking window and comb filter, blocking window and peak envelope, comb filter and peak envelope and, finally, blocking window, comb filter and peak envelope.Results and conclusionThe results showed that the scheme including all the modules led to an excellent approximation of the volitional EMG envelope, as extracted from the hybrid signal, and underlined the importance of the artifact blocking window module in the process.The results of this work have direct implications on the development of hybrid muscle activation rehabilitation systems for the enhancement of weakened muscles.

[1]  J Mizrahi,et al.  Fatigue-induced changes in decline running. , 2001, Clinical biomechanics.

[2]  M. Ferrarin,et al.  EMG signals detection and processing for on-line control of functional electrical stimulation. , 2000, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[3]  R. Merletti,et al.  Suppression of stimulation artifacts from myoelectric-evoked potential recordings , 1988, IEEE Transactions on Biomedical Engineering.

[4]  Young-Cheol Park,et al.  Gram-Schmidt M-Wave Canceller for the EMG Controlled FES , 2005, IEICE Trans. Inf. Syst..

[5]  W Girsch,et al.  Electromyogram-controlled functional electrical stimulation for treatment of the paralyzed upper extremity. , 1999, Artificial organs.

[6]  P. Veltink,et al.  Enhancement of Isometric Ankle Dorsiflexion by Automyoelectrically Controlled Functional Electrical Stimulation on Subjects with Upper Motor Neuron Lesions , 2002, Neuromodulation : journal of the International Neuromodulation Society.

[7]  S Saxena,et al.  An EMG-controlled grasping system for tetraplegics. , 1995, Journal of rehabilitation research and development.

[8]  Me Fry,et al.  EMG-controlled closed loop electrical stimulation using a digital signal processor , 2000 .

[9]  Derek T O'Keeffe,et al.  Stimulus artifact removal using a software-based two-stage peak detection algorithm , 2001, Journal of Neuroscience Methods.

[10]  I. W. Hunter,et al.  Dynamic relationship between EMG and torque at the human ankle: Variation with contraction level and modulation , 1988, Medical and Biological Engineering and Computing.

[11]  J Mizrahi,et al.  Muscle enhancement using closed-loop electrical stimulation: volitional versus induced torque. , 2007, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[12]  R Thorsen,et al.  Functional control of the hand in tetraplegics based on residual synergistic EMG activity. , 1999, Artificial organs.

[13]  J.C. Pereira,et al.  Evaluation of adaptive/nonadaptive filtering and wavelet transform techniques for noise reduction in EMG mobile acquisition equipment , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  M Verbeke,et al.  Computer-controlled portable stimulator for paraplegic patients. , 1993, Journal of biomedical engineering.

[15]  Hualou Liang,et al.  Stimulus artifact cancellation in the serosal recordings of gastric myoelectric activity using wavelet transform , 2002, IEEE Trans. Biomed. Eng..

[16]  W Baumann,et al.  The three-dimensional determination of internal loads in the lower extremity. , 1997, Journal of biomechanics.

[17]  Patrick E. Crago,et al.  Stimulus artifact removal in EMG from muscles adjacent to stimulated muscles , 1996, Journal of Neuroscience Methods.

[18]  F. Biering-Sørensen,et al.  Functional neuromuscular stimulation controlled by surface electromyographic signals produced by volitional activation of the same muscle: adaptive removal of the muscle response from the recorded EMG-signal. , 1997, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[19]  R.B. Stein,et al.  Characterization of signals and noise rejection with bipolar longitudinal intrafascicular electrodes , 1999, IEEE Transactions on Biomedical Engineering.

[20]  D. Lloyd,et al.  An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. , 2003, Journal of biomechanics.

[21]  M. Ferrarin,et al.  A pilot study of myoelectrically controlled FES of upper extremity , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[22]  Lotte N. S. Andreasen Struijk,et al.  Artefact reduction with alternative cuff configurations , 2003, IEEE Transactions on Biomedical Engineering.

[23]  Brian T Smith,et al.  Direct effect of percutaneous electric stimulation during gait in children with hemiplegic cerebral palsy: a report of 2 cases. , 2004, Archives of physical medicine and rehabilitation.

[24]  D. Winter,et al.  Predictions of knee and ankle moments of force in walking from EMG and kinematic data. , 1985, Journal of biomechanics.

[25]  J. Mizrahi,et al.  Partition Between Volitional and Induced Forces in Electrically Augmented Dynamic Isometric Muscle Contractions , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[26]  Thierry Keller,et al.  Overcoming abnormal joint torque patterns in paretic upper extremities using triceps stimulation. , 2005, Artificial organs.

[27]  J. Mizrahi,et al.  Stimulus artefact suppressor for EMG recording during FES by a constant-current stimulator , 2006, Medical and Biological Engineering and Computing.