Final Report Summary - AMYO (Advanced Myoelectric Control of Prosthetic Systems)
To achieve that, we analyzed the current academic state-of-the art in myoelectric control, and identified the hurdles for clinical application. This analysis revealed the lack of robustness as the main limiting factor, and the need to improve the three most important elements of the EMG-based man-machine interface: signal acquisition, signal processing, and training of users. Therefore, we developed new signal acquisition methods based on HD-EMG electrodes and silicone based electrodes. We also developed advanced signal processing methods based on neurophysiological modelling and machine learning principles to achieve intuitive, proportional and simultaneous control of two DOF. Furthermore, we developed a novel training system based on performance and psychometric measures. To test our methods for real clinical applicability, we used standardized, state-of-the-art performance evaluation methods and extended them to account for multifunctional prostheses. Our results on intact-limb subjects and subjects with limb deficiency showed considerable improvements compared to the state-of-the art. We are confident that we made a large step closer to a clinically and commercially viable man-machine interface for multifunctional prosthesis. Moreover, the knowledge acquired and the results achieved in this project will help us to extend myoelectric control to other assistive devices such as orthotics, exoskeletal devices, mobility solution, and neurostimulation devices.
Selected scientific publications:
Jiang, N., Dosen, S., Müller, K.-R. Farina, D., (2012). Myoelectric Control of Artificial Limbs: Is There the Need for a Change of Focus? IEEE Signal Processing Magazine, Vol. 29, No. 5, 148-152.
Amsuess, S., Goebel, P. M., Jiang, N., Graimann, B., Farina, D., (2014). Self-Correcting Pattern Recognition System of Surface EMG Signals for Upper Limb Prosthesis Control. Biomedical Engineering, IEEE Transactions on, 61(4), 1167–1176.
Amsuess, S., Goebel, P., Graimann, B., Farina, D., (2014). A Multi-Class Proportional Myocontrol Algorithm for Upper Limb Prosthesis Control : Validation in Real-Life Scenarios on Amputees. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, PP(99), 1–10.
Jiang, N., Rehbaum, H., Vujaklija, I., Graimann, B., Farina, D., (2014). Intuitive , Online , Simultaneous , and Proportional Myoelectric Control Over Two Degrees-of-Freedom in Upper Limb Amputees. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 22(3), 501–510.
Jiang, N., Vujaklija, I., Rehbaum, H., Graimann, B., Farina, D., (2014). Is Accurate Mapping of EMG Signals on Kinematics Needed for Precise Online Myoelectric Control ? Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 22(3), 549–558.
Hahne, J. M., Bießmann, F., Jiang, N., Rehbaum, H., Farina, D., … Parra, L. C. (2014). Linear and Nonlinear Regression Techniques for Simultaneous and Proportional Myoelectric Control. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 22(2), 269–279.
Farina, D., Jiang, N., Rehbaum, H., Holobar, A., Graimann, B., Aszmann, O., (2014). The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges. IEEE Transactions on Neural System and Rehabilitation Engineering, 22(4), 797–809.