Society aging is a concerning phenomena in many modern countries. It increases the incidence rate of age-related neuromuscular movement and will result in excessive economic pressures on health-care systems worldwide. One potential solution is to develop smart Medical Cyber-physical Systems (CPSs) that are capable of: (i) Providing safe, optimal, effective and affordable means of neuro-rehabilitation, and; (ii) Assisting patients in performing activities of daily living. Bio-signal processing and machine learning solutions are in the heart of the above-mentioned Medical CPSs, and are essential to guarantee compatible, intelligent, compliant, appropriate, safe, and adaptive interaction between the human-in-need (i.e., patient) and the CPS (e.g., a rehabilitative robotic arm, a powered prosthetic device, or an active exoskeleton). The spirit and wide scope of the bio-signal processing and machine learning applications in Medical CPSs and in particular rehabilitation and assistive systems calls for a focused investigation of state-of-the-art techniques to further advance this field.
Imperial College London
Alpha motor neurons receive synaptic input that they convert into the ultimate neural code of movement -- the neural drive to muscles. Recently, the interfacing (with non-invasive or implanted bioelectrodes) and processing methods for the identification of the activity of motor neuron pools from interference electromyogram (EMG) signals have been advanced substantially. In the past decade, these methods have indeed allowed the precise measure of the discharge timings of tens to hundreds of motor neurons concurrently during natural movements. This has been achieved by the developments of blind source separation methods applied to densely sample EMG signals. The neural population analysis that these methods enable has opened new perspectives in the study of the neural control of movement. Moreover, it now offers the possibility of decoding the output of the spinal circuits for man-machine interfacing. In this view, the muscles act as biological amplifiers of the neural code of motor neuron pools, either through natural innervation or surgical targeted reinnervation. The muscle electrical activity is then decoded to extract the neural code that is mapped into commands for external devices. This combination of surgical procedures, advanced recording and decoding, and mapping into effective commands constitutes a direct interface with the efferent nerve activity. The talk will overview the technology and signal processing for motor neuron interfacing and its potential for Neurotechnology applications, with special emphasis on upper limb prostheses.
Dario Farina is the Chair in Neurorehabilitation Engineering at Imperial College London. Before joining Imperial College, he was Full Professor at Aalborg University, Aalborg, Denmark util 2010, when he was appointed as a full professor and founding chair of the Department of NeuroRehabilitation Engineering, University Medical Center Gottingen, Georg-August University, Germany. He founded and directed the Institute of Neurorehabilitation Systems (2010-2016) until he moved to Imperial College London as Chair in Neurorehabilitation Engineering. His research focuses on biomedical signal processing, Neurorehabilitation technology, and neural control of movement. Within these areas, he has (co)-authored approximately 400 papers in peer-reviewed Journals and >500 conference abstract and papers. He has been the President of the International Society of Electrophysiology and Kinesiology (ISEK) (2012-2014) and is currently the Editor-in-Chief of the official Journal of this Society, the Journal of Electromyography and Kinesiology. He is also currently an Editor for IEEE Transactions on Biomedical Engineering and the Journal of Physiology, and previously covered editorial roles in several other Journals.
|Tuesday, November 27|
|09:40 - 10:40|
|DL DL-BIO.1: Dario Farina: "Man-Machine Interfacing by Decoding Spinal Motor Neuron Behavior from High-Density EMG"|
|11:00 - 12:30|
|BIO-L.1: Signal Processing for Rehabilitation & Assistive Systems|
|14:00 - 15:30|
|BIO-L.2: Signal Processing for Wearable Health Technologies|
|15:50 - 17:20|
|BIO-L.3: Neural Signal Processing and BCI Systems|
|Wednesday, November 28|
|11:00 - 12:30|
|BIO-L.4: Bio-signal Processing & Machine Learning for MCPS|
|14:00 - 15:30|
|BIO-L.5: Biomedical Image Processing I|
|15:50 - 17:20|
|BIO-L.6: Biomedical Image Processing II|
Submissions are welcome on topics including:
Prospective authors are invited to submit full-length papers (up to 4 pages for technical content including figures and possible references, and with one additional optional 5th page containing only references) and extended abstracts (up to 2 pages, for paper-less industry presentations and Ongoing Work presentations).. Manuscripts should be original (not submitted/published anywhere else) and written in accordance with the standard IEEE double-column paper template. Accepted full-length papers will be indexed on IEEE Xplore. Accepted abstracts will not be indexed in IEEE Xplore, however the abstracts and/or the presentations will be included in the IEEE SPS SigPort. Accepted papers and abstracts will be scheduled in lecture and poster sessions.
|Paper Submission Deadline|
|Review Results Announced||September 7, 2018|
|Camera-Ready Papers Due||September 24, 2018|
|November 5, 2018||Hotel Room Reservation Deadline|