Muhammad Asad and S.M. Masadur Al-Arif who are pursuing PhD degrees in the Computer Vision Group within the Research Centre for Machine Learning have received Best Paper Awards for research presented in two leading conferences this year.
In June 2016, Muhammad received the Best Paper Award at the 2nd Workshop on Observing and Understanding Hands in Action, part of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, for the paper “Learning Marginalization through Regression for Hand Orientation Inference”, co-authored by Dr Gregory Slabaugh.
The paper presented a novel machine learning model that enables a computer to understand and describe the orientation of a human hand by looking at colour images of it.
In September 2016, Arif received the best paper award at the Fourth MICCAI Workshop on Computational Methods and Clinical Applications for Spine Imaging, part of the 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Athens, Greece, for the paper “Global Localization and Orientation of the Cervical Spine in X-ray Imaging”, co-authored by M. Gundry, K. Knapp, and G. Slabaugh.
The paper presented a machine learning framework capable of teaching the computer to locate the spinal column in X-ray images. It learns what the spinal column looks like from expert annotated example X-ray images and is able to locate the spine on unseen images.
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Date & Time: Monday 28 Nov at 11 am
Venue: A110 (College Building)
TITLE: Sub-chronic recordings for myoelectric control of prostheses: paving the way for a better understanding of the language of the brain
ABSTRACT: Every day somewhere globally, at least one person undergoes amputation of part or entire upper limb. An estimated prevalence of 11.6 per 100,000 has been reported in Norway. The social and economic burden of limb deficiency has led to development of artificial means to replace missing functions. Among the available solutions, the most advanced in clinics are those based on active control using the electric activities (electromyographic, EMG, signals) produced by the remnant muscles. The use of EMG to control prosthetic limbs dates back to more than 50 years. Progress has been made in this period, mainly in the laboratory settings. However, even though multi-dexterous prosthetic hands (e.g. DEKA Hand) are now available, the control has remained limited to the original solutions in the clinics. The use of machine learning (ML), rather than direct control, has attempted to advance the control possibilities of the users but it is limited by unsatisfactory robustness to non-stationarities (e.g. changes in electrode positions and skin-electrode interface). Robustness is the key characteristics of any clinical solution. Very advanced control systems that allow a substantial functional benefit for short-term, laboratory tests, cannot be translated into clinical solutions if their performance worsens over time. This presentation gives an overview of our clinical and laboratory understanding of the language of the brain (“Brainish”) in myoelectric control of prostheses. Furthermore, preliminary results of sub-chronic experiments (seven days) are presented together with future perspectives.
Ernest Nlandu Kamavuako received the Master and Ph.D. degrees in biomedical engineering from Aalborg University, Aalborg, Denmark, in 2006 and 2010. Since 2014, he has been an Associate Professor at the Department of Health Science and Technology, Aalborg University, Denmark with excellent teaching and supervision skills. In 2015, he was named teacher of the year by the students of the study board for health technology and Sport science. From 2007 to 2008, he was a Research Scholar in the Biomedical Department, IUPUI, Indianapolis, USA. From 2012 to 2013, he was a Postdoctoral Fellow at the Institute of Biomedical Engineering, University of New Brunswick, Canada. He has a good publication record with main research interests related to the use of invasive recordings in the control of upper limb prostheses. Other research interests include muscle recovery functions following electrical stimulation, applied signal processing and the application of near-infrared spectroscopy and EEG for brain–computer interfaces.