Laboratory Heads: Dr. Strahinja Dosen and Dr. Silvia Muceli
At the Myoelectric Control and Sensory Feedback Laboratory, we are working towards the development of closed-loop prosthetic systems implementing the concept of sensory-motor integration, which we believe is instrumental for a more efficient use of the prosthesis as well as for the embodiment of the device into a body-scheme of the user.
The research activities are directed towards three main directions.
First, we are developing methods for the simultaneous and proportional control of multiple degrees of freedom based on surface EMG. This includes robust control for the prosthetic and robotic systems with multiple grasp types, active wrist and/or individually controllable fingers. Our aim is to utilize multimodal information for smarter, context adaptable control as well as to develop online training paradigms to facilitate (co)adaptation between the user and the system.
Moreover, we would like to understand the role of multimodal sensory feedback in the normal human motor control. The insight into the mechanisms of human sensory processing represents a foundation and inspiration for the development of rehabilitation methods with an emphasis on the role of sensory information: 1) sensory substitution to provide feedback in prosthetic devices, and 2) the use of augmented/enhanced feedback for neuromuscular rehabilitation. To this aim, we are investigating the properties of the human manual control when using tactile feedback to operate different real and simulated systems (e.g., prosthesis, inverted pendulum). We also evaluate novel sensing technologies (sensorized finger, artificial skin) and aim at developing new interfaces and methods to provide tactile stimulation (e.g., flexible matrices, hybrid stimulators).
Finally, we are developing novel control methods for rehabilitation devices that are based on controllers enhanced with additional sensors to operate the systems autonomously and intelligently. Sensor fusion is employed to implement robust automatic control which decreases the burden from the user. We are currently experimenting with the use of artificial vision, inertial measurement units, and touch sensors for the automatic control of robotic prosthetic hands.
Kapelner T, Jiang N, Holobar A, Vujaklija I, Roche AD, Farina D, Aszmann OC. Motor Unit Characteristics after Targeted Muscle Reinnervation. PLoS One. 11(2):e0149772, 2016
Aszmann OC, Roche AD, Salminger S, Paternostro-Sluga T, Herceg M, Sturma A, Hofer C, Farina D. Bionic reconstruction to restore hand function after brachial plexus injury: a case series of three patients. Lancet. 385(9983):2183-9, 2015
Markovic M, Dosen S, Popovic D, Graimann B, Farina D. Sensor fusion and computer vision for context-aware control of a multi degree-of-freedom prosthesis. J Neural Eng. 12(6):066022, 2015
Dosen S, Markovic M, Hartmann C, Farina D. Sensory feedback in prosthetics: a standardized test bench for closed-loop control. IEEE Trans Neural Syst Rehabil Eng. 23(2):267-76, 2015
Ninu A, Dosen S, Muceli S, Rattay F, Dietl H, Farina D. Closed Loop Control of Grasping with Hand Prostheses: Which are the Relevant Feedback Variables for Force Control? IEEE Trans Neural Syst Rehabil Eng, 22(5): 1041-52, 2014
Muceli S, Jiang N, Farina D. Extracting Signals Robust to Electrode Number and Shift for Online Simultaneous and Proportional Myoelectric Control by Factorization Algorithms. IEEE Trans Neural Syst Rehabil Eng, 22(3): 623-33, 2014
Hahne JM, Biessmann F, Jiang N, Rehbaum H, Farina D, Meinecke FC, Muller KR, Parra LC. Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control. IEEE Trans Neural Syst Rehabil Eng. 22(2):269-79, 2014