- Farina D, Aszmann O. Bionic limbs: clinical reality and academic promises. Science translational medicine. 2014;6(257):257ps12. doi: 10.1126/scitranslmed.3010453.
- Scheme E, Englehart K. Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. Journal of Rehabilitation Research & Development. 2011;48(6):643-59. doi: 10.1682/jrrd.2010.09.0177. PMID: 21938652.
- Jiang N, Dosen S, Muller K-R, Farina D. Myoelectric control of artificial limbs—is there a need to change focus?[In the spotlight]. IEEE Signal Processing Magazine. 2012;29(5):150-2. doi: 10.1109/MSP.2012.2203480.
- Ameri A, Akhaee MA, Scheme E, Englehart K. Real-time, simultaneous myoelectric control using a convolutional neural network. PloS one. 2018;13(9):e0203835. doi: 10.1371/journal.pone.0203835. PMID: 30212573. Pubmed PMCID: PMC6136764.
- Ameri A, Englehart KB, Parker PA, editors. A comparison between force and position control strategies in myoelectric prostheses. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; San Diego, CA, USA: 2012. P. 1342-5. doi: 10.1109/EMBC.2012.6346186.
- Ameri A, Scheme EJ, Englehart KB, Parker PA, editors. Bagged regression trees for simultaneous myoelectric force estimation. 2014 22nd Iranian Conference on Electrical Engineering (ICEE); Tehran, Iran: IEEE; 2014. P. 2000-3. doi: 10.1109/IranianCEE.2014.6999871.
- Englehart K, Hudgins B. A robust, real-time control scheme for multifunction myoelectric control. IEEE transactions on biomedical engineering. 2003;50(7):848-54. doi: 10.1109/TBME.2003.813539.
- Bengio Y, Courville A, Vincent P. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence. 2013;35(8):1798-828. doi: 10.1109/TPAMI.2013.50.
- Hudgins B, Parker P, Scott RN. A new strategy for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering. 1993;40(1):82-94. doi: 10.1109/10.204774.
- Atzori M, Cognolato M, Müller H. Deep learning with convolutional neural networks applied to electromyography data: A resource for the classification of movements for prosthetic hands. Frontiers in neurorobotics. 2016;10:9. doi: 10.3389/fnbot.2016.00009.
- Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE. 1998;86(11):2278-324. doi: 10.1109/5.726791.
- Prahm C, Paassen B, Schulz A, Hammer B, Aszmann O. Transfer learning for rapid re-calibration of a myoelectric prosthesis after electrode shift. Converging clinical and engineering research on neurorehabilitation II: Springer, Cham; 2017. p. 153-7. doi: 10.1007/978-3-319-46669-9_28.
- Du Y, Jin W, Wei W, Hu Y, Geng W. Surface EMG-based inter-session gesture recognition enhanced by deep domain adaptation. Sensors. 2017;17(3):458. doi: 10.3390/s17030458.
|