NeuroMuscular Human-Machine Interface (NM-HMI)
Our research aims to develop an innovative neuromuscular human-machine interface (NM-HMI) to control a wearable powered orthosis for the lower limb. This research will help support and improve disabled people’s physical capabilities by enhancing and expanding their rehabilitation process through the aid of advanced computer-based systems. Our goal is to help to realize the next generation’s robotics rehabilitation.When a person wants to move, nerve signals are sent from the brain to muscles via motor neurons. Small electrical signals, called electromyographic (EMG) signals, are generated in the muscles and induce contraction. EMG signals can be detected on the surface of the skin through sensors attached on the body. We want to use EMG signals to first establish a communication channel between the human and a generic computer system. The possibility of extrapolating information from EMG signals will allow the development of an innovative NM-HMI that can be used for a different number of purposes that range from the study of human’s neuromuscular activity to the development of man-in-the-loop robotics applications.
A novel neuromusculoskeletal model of the human lower extremity is therefore developed:
Collaborators
- Massimo Sartori, Department of Neurorehabilitaion Engineering, Georg-August University, Göttingen, Germany
- David G. Lloyd, School of Sport Science, Exercise and Health, University of Western Australia
Host
University of Padua, DTG and IASLab and BEM Laboratory@DEI
University of Western Australia, School of Sport Science, Exercise and Health
References
- M. Sartori, M. Reggiani, DG. Lloyd, Enrico Pagello. EMG-driven forward-dynamic estimation of muscle force and joint moment about multiple degrees of freedom in the human lower extremity. Journal of Engineering in Medicine, Part H, Special Issue on Lower Limb Musculoskeletal Modelling, Under Review.
- M. Sartori, M. Reggiani, T. VD. Bogert, DG. Lloyd. Estimation of musculotendon kinematics in large musculoskeletal models using multidimensional B-Splines. Journal of Biomechanics, Under Review.
- M. Sartori, M. Reggiani, D.G. Lloyd and E. Pagello A neuromusculoskeletal model of the human lower limb:Towards EMG-driven actuation of multiple joints in powered orthoses, Rehabilitation Robotics (ICORR2011), International Conference on, To appear.
- M. Sartori, M. Reggiani, D.G. Lloyd and E. Pagello. An EMG-driven musculoskeletal model of the human lower limb for the estimation of muscle forces and moments at the hip, knee and ankle joints in vivo. In Biomechanical Simulation of Humans and Bio-Inspired Humanoids (BH)2, Workshop on, November 2010.
- M. Sartori, M. Reggiani, E. Ceseracciu, Z. Sawacha, , D.G. Lloyd, C. Cobelli and E. Pagello. Enhancing human-machine interfaces through EMG-driven modeling. In Future Trends in Rehabiliation Robotics, IEEE BIOROB Workshop on, September 2010.
- M. Sartori, D.G. Lloyd, M. Reggiani and E. Pagello. Fast operation of anatomical and stiff tendon neuromuscular models in EMG-driven modeling. In Robotics and Automation (ICRA). IEEE International Conference on, pages 2228 – 2234, May 2010.
- M. Sartori, D.G. Lloyd, M. Reggiani, E. Ceseracciu, Z. Sawacha, E. Pagello and C. Cobelli. On the enhancement of EMG-driven neuromuscular models for the runtime control of powered orthosis. In Workshop CORNER, December 2009.
- M. Sartori, D.G. Lloyd, M. Reggiani and E. Pagello. A stiff tendon neuromusculoskeletal model of the knee. In Advanced Robotics and its Social Impacts (ARSO), IEEE Workshop on, November 2009.
- M. Sartori, M. Reggiani, G. Chemello and E. Pagello. Scaling tendons preserving the consistency between the EMG-to-activation relationship. In 7th Australasian Biomechanics Conference (ABC 7), November 2009.
- M. Sartori, M. Reggiani, C. Mezzato and E. Pagello. A lower limb EMG-driven biomechanical model for applications in rehabilitation robotics. In Advanced Robotics (ICAR). International Conference on, June 2009.
- M. Sartori, G. Chemello, M. Reggiani and E. Pagello. Control of a virtual leg via EMG signals from four thigh muscles. In Intelligent Autonomous Systems (IAS), International Conference on, July 2008.
- M. Sartori, G. Chemello, and E. Pagello. A 3D Virtual Model of the Knee Driven by EMG Signals. In Proceedings of the 10th Congress of the Italian Association of Artificial Intelligence (AI*IA), 2007.
Current Students
Claudio PizzolatoSecond-cycle degree course (MSc level) in Mechatronic Engineering
University of Padua
December 2010-
