Abstract
The objective of the present study was to develop a myoelectric controller able to classify specific hand movements from few milliseconds window of the EMG signals, detected by a patient’s forearm muscles. Five hand functional tasks have been selected: i) pitching; ii) grasp an object; iii) grasping; iv) lateral grasp; v) wave. The EMG task-selection classifier is designed so that the system predicts the intention to perform a certain task from EMG signals measured in a 100 ms window after the EMG onset corresponding to the electrophysiological delay. The prediction algorithm is based on an unsupervised K-means clustering followed by a cascade of artificial neural networks (ANN). It receives as input the 5 EMG signals measured in the 100 ms window and produces the predicted movement as output. Classifier performance is measured in percentage of correct classifications. The overall cascade ANN classifier produced a performance of 93%±7%, and of 72%±8% respectively on training and testing datasets, comparable with results obtained in literature. The results obtained during the tests on healthy subjects has provided satisfactory results, and the choice of the filtering algorithm to detect movement onset, and artificial neural networks cascade for classification allows a real-time implementation of the controller, opening the way of a real-time movement predictor.
The original version of this chapter was inadvertently published with an incorrect chapter pagination 634–637 and DOI 10.1007/978-3-319-32703-7_122. The page range and the DOI has been re-assigned. The correct page range is 640–643 and the DOI is 10.1007/978-3-319-32703-7_123. The erratum to this chapter is available at DOI: 10.1007/978-3-319-32703-7_260
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-32703-7_260
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References
Pedrocchi A, Ferrante S, Ambrosini E et al. (2013) MUNDUS project: MUltimodal Neuroprosthesis for daily Upper limb Support. J Neuroeng Rehabil 10:66
Schabowsky CN, Godfrey SB, Holley RJ et al. (2010) Development and pilot testing of HEXORR: Hand EXOskeleton Rehabilitation Robot. J NeuroEngineering Rehabil 7: 36
Langhorne P, Bernhardt J, Kwakkel G (2011) Stroke rehabilitation. The Lancet 377:1693–1702
Gandolla M, Ferrante S, Molteni F et al. (2014) Re-thinking the role of motor cortex: Context-sensitive motor outputs? Neuroimage 91:366-374
Gandolla M, Ward NS, Molteni F et al. (2015) The Neural Correlates of Long-Term Carryover following Functional Electrical Stimulation for Stroke. Neural Plast 501:509267
Gandolla M, Molteni F, Ward NS et al. (2015) Validation of a quantitative single-subject based evaluation for rehabilitation-induced improvement Assessment. Ann Biomed Eng 43:2686-98
Fligge N, Urbanek H, Van der Smagt P (2013) Relation between object properties and EMG during reaching to grasp. J Electromyogr Kinesiol 23:402–410
Muceli S, Farina D (2012) Simultaneous and proportional estimation of hand kinematics from EMG during mirrored movements at multiple degrees-of-freedom. IEEE Trans Neural Syst Rehabil Eng 20: 371–378
Pedrocchi A, Ferrante S, De Momi E et al. (2006) Error mapping controller: A closed loop neuroprosthesis controlled by artificial neural networks. J Neuroeng Rehabil 3:25
Cavanagh PR, Komi PV (1979) Electromechanical delay in human skeletal muscle under concentric and eccentric contractions.” , Eur J Appl Physiol Occup Physiol 42:159–163
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Gandolla, M., Ferrante, S., Baldassini, D., Cotti Cottini, M., Seneci, C., Pedrocchi, A. (2016). Artificial Neural-Network EMG Classifier for Hand Movements Prediction. In: Kyriacou, E., Christofides, S., Pattichis, C. (eds) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-32703-7_123
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DOI: https://doi.org/10.1007/978-3-319-32703-7_123
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