145 results found
Gottesman O, Johansson F, Komorowski M, et al., 2019, Guidelines for reinforcement learning in healthcare, NATURE MEDICINE, Vol: 25, Pages: 16-18, ISSN: 1078-8956
Komorowski M, Celi L, Badawi O, et al., 2018, The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care, NATURE MEDICINE, Vol: 24, Pages: 1716-+, ISSN: 1078-8956
Cunningham J, Hapsari A, Guilleminot P, et al., 2018, The Supernumerary Robotic 3 <sup>rd</sup> Thumb for Skilled Music Tasks
© 2018 IEEE. Wearable robotics bring the opportunity to augment human capability and performance, be it through prosthetics, exoskeletons, or supernumerary robotic limbs. The latter concept allows enhancing human performance and assisting them in daily tasks. An important research question is, however, whether the use of such devices can lead to their eventual cognitive embodiment, allowing the user to adapt to them and use them seamlessly as any other limb of their own. This paper describes the creation of a platform to investigate this. Our supernumerary robotic 3 rd thumb was created to augment piano playing, allowing a pianist to press piano keys beyond their natural hand-span; thus leading to functional augmentation of their skills and the technical feasibility to play with 11 fingers. The robotic finger employs sensors, motors, and a human interfacing algorithm to control its movement in real-time. A proof of concept validation experiment has been conducted to show the effectiveness of the robotic finger in playing musical pieces on a grand piano, showing that naive users were able to use it for 11 finger play within a few hours.
Woods B, Subramanian M, Shafti A, et al., 2018, Mecbanomyograpby Based Closed-Loop Functional Electrical Stimulation Cycling System, Pages: 179-184, ISSN: 2155-1774
© 2018 IEEE. Functional Electrical Stimulation (FES) systems are successful in restoring motor function and supporting paralyzed users. Commercially available FES products are open loop, meaning that the system is unable to adapt to changing conditions with the user and their muscles which results in muscle fatigue and poor stimulation protocols. This is because it is difficult to close the loop between stimulation and monitoring of muscle contraction using adaptive stimulation. FES causes electrical artefacts which make it challenging to monitor muscle contractions with traditional methods such as electromyography (EMG). We look to overcome this limitation by combining FES with novel mechanomyographic (MMG) sensors to be able to monitor muscle activity during stimulation in real time. To provide a meaningful task we built an FES cycling rig with a software interface that enabled us to perform adaptive recording and stimulation, and then combine this with sensors to record forces applied to the pedals using force sensitive resistors (FSRs); crank angle position using a magnetic incremental encoder and inputs from the user using switches and a potentiometer. We illustrated this with a closed-loop stimulation algorithm that used the inputs from the sensors to control the output of a programmable RehaStim 1 FES stimulator (Hasomed) in real-time. This recumbent bicycle rig was used as a testing platform for FES cycling. The algorithm was designed to respond to a change in requested speed (RPM) from the user and change the stimulation power (% of maximum current mA) until this speed was achieved and then maintain it.
Ortega P, Colas C, Faisal AA, 2018, Compact Convolutional Neural Networks for Multi-Class, Personalised, Closed-Loop EEG-BCI, Pages: 136-141, ISSN: 2155-1774
© 2018 IEEE. For many people suffering from motor disabilities, assistive devices controlled with only brain activity are the only way to interact with their environment . Natural tasks often require different kinds of interactions, involving different controllers the user should be able to select in a self-paced way. We developed a Brain-Computer Interface (BCI) allowing users to switch between four control modes in a self-paced way in real-time. Since the system is devised to be used in domestic environments in a user-friendly way, we selected non-invasive electroencephalographic (EEG) signals and convolutional neural networks (CNNs), known for their ability to find the optimal features in classification tasks. We tested our system using the Cybathlon BCI computer game, which embodies all the challenges inherent to real-time control. Our preliminary results show that an efficient architecture (SmallNet), with only one convolutional layer, can classify 4 mental activities chosen by the user. The BCI system is run and validated online. It is kept up-to-date through the use of newly collected signals along playing, reaching an online accuracy of 47.6% where most approaches only report results obtained offline. We found that models trained with data collected online better predicted the behaviour of the system in real-time. This suggests that similar (CNN based) offline classifying methods found in the literature might experience a drop in performance when applied online. Compared to our previous decoder of physiological signals relying on blinks, we increased by a factor 2 the amount of states among which the user can transit, bringing the opportunity for finer control of specific subtasks composing natural grasping in a self-paced way. Our results are comparable to those showed at the Cybathlon's BCI Race but further improvements on accuracy are required.
Maymó MR, Shafti A, Faisal AA, 2018, Fast Orient: Lightweight Computer Vision for Wrist Control in Assistive Robotic Grasping
© 2018 IEEE. Wearable and Assistive robotics for human grasp support are broadly either tele-operated robotic arms or act through orthotic control of a paralyzed user's hand. Such devices require correct orientation for successful and efficient grasping. In many human-robot assistive settings, the end-user is required to explicitly control the many degrees of freedom making effective or efficient control problematic. Here we are demonstrating the off-loading of low-level control of assistive robotics and active orthotics, through automatic end-effector orientation control for grasping. This paper describes a compact algorithm implementing fast computer vision techniques to obtain the orientation of the target object to be grasped, by segmenting the images acquired with a camera positioned on top of the end-effector of the robotic device. The rotation needed that optimises grasping is directly computed from the object's orientation. The algorithm has been evaluated in 6 different scene backgrounds and end-effector approaches to 26 different objects. 94.8% of the objects were detected in all backgrounds. Grasping of the object was achieved in 91.1% of the cases and has been evaluated with a robot simulator confirming the performance of the algorithm.
Lin C-H, Faisal AA, 2018, Decomposing sensorimotor variability changes in ageing and their connection to falls in older people, SCIENTIFIC REPORTS, Vol: 8, ISSN: 2045-2322
Subramanian M, Shafti A, Faisal A, Mechanomyography based closed-loop Functional Electrical Stimulation cycling system, BioRob 2018- IEEE International Conference on Biomedical Robotics and Biomechatronics
Auepanwiriyakul C, Harston A, Orlov P, et al., 2018, Semantic Fovea: Real-time annotation of ego-centric videos with gaze context
© 2018 Copyright held by the owner/author(s). Visual context plays a crucial role in understanding human visual attention in natural, unconstrained tasks - the objects we look at during everyday tasks provide an indicator of our ongoing attention. Collection, interpretation, and study of visual behaviour in unconstrained environments therefore is necessary, however presents many challenges, requiring painstaking hand-coding. Here we demonstrate a proof-of-concept system that enables real-time annotation of objects in an egocentric video stream from head-mounted eye-tracking glasses. We concurrently obtain a live stream of user gaze vectors with respect to their own visual field. Even during dynamic, fast-paced interactions, our system was able to recognise all objects in the user’s field-of-view with moderate accuracy. To validate our concept, our system was used to annotate an in-lab breakfast scenario in real time.
Orlov P, Shafti A, Auepanwiriyakul C, et al., 2018, A Gaze-contingent Intention Decoding Engine for human augmentation
© 2018 Copyright held by the owner/author(s). Humans process high volumes of visual information to perform everyday tasks. In a reaching task, the brain estimates the distance and position of the object of interest, to reach for it. Having a grasp intention in mind, human eye-movements produce specific relevant patterns. Our Gaze-Contingent Intention Decoding Engine uses eye-movement data and gaze-point position to indicate the hidden intention. We detect the object of interest using deep convolution neural networks and estimate its position in a physical space using 3D gaze vectors. Then we trigger the possible actions from an action grammar database to perform an assistive movement of the robotic arm, improving action performance in physically disabled people. This document is a short report to accompany the Gaze-contingent Intention Decoding Engine demonstrator, providing details of the setup used and results obtained.
Cunningham J, Hapsari A, Guilleminot P, et al., The supernumerary robotic 3rd thumb for skilled music tasks, The 7th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, Publisher: IEEE
Wearable robotics bring the opportunity to augmenthuman capability and performance, be it through prosthetics,exoskeletons, or supernumerary robotic limbs. The latterconcept allows enhancing human performance and assistingthem in daily tasks. An important research question is, however,whether the use of such devices can lead to their eventualcognitive embodiment, allowing the user to adapt to them anduse them seamlessly as any other limb of their own. This paperdescribes the creation of a platform to investigate this. Oursupernumerary robotic 3rd thumb was created to augment pianoplaying, allowing a pianist to press piano keys beyond theirnatural hand-span; thus leading to functional augmentation oftheir skills and the technical feasibility to play with 11 fingers.The robotic finger employs sensors, motors, and a humaninterfacing algorithm to control its movement in real-time. Aproof of concept validation experiment has been conducted toshow the effectiveness of the robotic finger in playing musicalpieces on a grand piano, showing that naive users were able touse it for 11 finger play within a few hours.
Ruiz Maymo M, Shafti S, Faisal AA, FastOrient: lightweight computer vision for wrist control in assistive robotic grasping, The 7th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, Publisher: IEEE
Wearable and Assistive robotics for human graspsupport are broadly either tele-operated robotic arms or actthrough orthotic control of a paralyzed user’s hand. Suchdevices require correct orientation for successful and efficientgrasping. In many human-robot assistive settings, the end-useris required to explicitly control the many degrees of freedommaking effective or efficient control problematic. Here we aredemonstrating the off-loading of low-level control of assistiverobotics and active orthotics, through automatic end-effectororientation control for grasping. This paper describes a compactalgorithm implementing fast computer vision techniques toobtain the orientation of the target object to be grasped, bysegmenting the images acquired with a camera positioned ontop of the end-effector of the robotic device. The rotation neededthat optimises grasping is directly computed from the object’sorientation. The algorithm has been evaluated in 6 differentscene backgrounds and end-effector approaches to 26 differentobjects. 94.8% of the objects were detected in all backgrounds.Grasping of the object was achieved in 91.1% of the casesand has been evaluated with a robot simulator confirming theperformance of the algorithm.
Ponferrada EG, Sylaidi A, Aldo Faisal A, 2018, Data-efficient motor imagery decoding in real-time for the cybathlon brain-computer interface race, Pages: 21-32
Copyright © 2018 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved Neuromotor diseases such as Amyotrophic Lateral Sclerosis or Multiple Sclerosis affect millions of people throughout the globe by obstructing body movement and thereby any instrumental interaction with the world. Brain Computer Interfaces (BCIs) hold the premise of re-routing signals around the damaged parts of the nervous system to restore control. However, the field still faces open challenges in training and practical implementation for real-time usage which hampers its impact on patients. The Cybathlon Brain-Computer Interface Race promotes the development of practical BCIs to facilitate clinical adoption. In this work we present a competitive and data-efficient BCI system to control the Cybathlon video game using motor imageries. The platform achieves substantial performance while requiring a relatively small amount of training data, thereby accelerating the training phase. We employ a static band-pass filter and Common Spatial Patterns learnt using supervised machine learning techniques to enable the discrimination between different motor imageries. Log-variance features are extracted from the spatio-temporally filtered EEG signals to fit a Logistic Regression classifier, obtaining satisfying levels of decoding accuracy. The systems performance is evaluated online, on the first version of the Cybathlon Brain Runners game, controlling 3 commands with up to 60.03% accuracy using a two-step hierarchical classifier.
Liu Y, Gottesman O, Raghu A, et al., 2018, Representation Balancing MDPs for Off-Policy Policy Evaluation, 32nd Conference on Neural Information Processing Systems (NIPS), Publisher: NEURAL INFORMATION PROCESSING SYSTEMS (NIPS), ISSN: 1049-5258
Parbhoo S, Gottesman O, Ross AS, et al., 2018, Improving counterfactual reasoning with kernelised dynamic mixing models., PLoS One, Vol: 13
Simulation-based approaches to disease progression allow us to make counterfactual predictions about the effects of an untried series of treatment choices. However, building accurate simulators of disease progression is challenging, limiting the utility of these approaches for real world treatment planning. In this work, we present a novel simulation-based reinforcement learning approach that mixes between models and kernel-based approaches to make its forward predictions. On two real world tasks, managing sepsis and treating HIV, we demonstrate that our approach both learns state-of-the-art treatment policies and can make accurate forward predictions about the effects of treatments on unseen patients.
Peng X, Ding Y, Wihl D, et al., 2018, Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning., Pages: 887-896
Sepsis is the leading cause of mortality in the ICU. It is challenging to manage because individual patients respond differently to treatment. Thus, tailoring treatment to the individual patient is essential for the best outcomes. In this paper, we take steps toward this goal by applying a mixture-of-experts framework to personalize sepsis treatment. The mixture model selectively alternates between neighbor-based (kernel) and deep reinforcement learning (DRL) experts depending on patient's current history. On a large retrospective cohort, this mixture-based approach outperforms physician, kernel only, and DRL-only experts.
Xiloyannis M, Gavriel C, Thomik AAC, et al., 2017, Gaussian Process Autoregression for Simultaneous Proportional Multi-Modal Prosthetic Control With Natural Hand Kinematics, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, Vol: 25, Pages: 1785-1801, ISSN: 1534-4320
Iyer R, Ungless MA, Faisal AA, 2017, Calcium-activated SK channels control firing regularity by modulating sodium channel availability in midbrain dopamine neurons, SCIENTIFIC REPORTS, Vol: 7, ISSN: 2045-2322
Kotti M, Duffell LD, Faisal AA, et al., 2017, Detecting knee osteoarthritis and its discriminating parameters using random forests, MEDICAL ENGINEERING & PHYSICS, Vol: 43, Pages: 19-29, ISSN: 1350-4533
Makin TR, de Vignemont F, Faisal AA, 2017, Neurocognitive barriers to the embodiment of technology, NATURE BIOMEDICAL ENGINEERING, Vol: 1, ISSN: 2157-846X
Pedotti A, Azevedo L, Faisal A, 2017, Foreword, Pages: VII-VIII
Noronha B, Dziemian S, Zito GA, et al., 2017, "Wink to grasp" - comparing Eye, Voice & EMG gesture control of grasp with soft-robotic gloves, International Conference on Rehabilitation Robotics (ICORR), Publisher: IEEE, Pages: 1043-1048, ISSN: 1945-7898
Maimon-Dror RO, Fernandez-Quesada J, Zito GA, et al., 2017, Towards free 3D end-point control for robotic-assisted human reaching using binocular eye tracking, International Conference on Rehabilitation Robotics (ICORR), Publisher: IEEE, Pages: 1049-1054, ISSN: 1945-7898
Faisal AA, Neishabouri A, 2016, Fundamental Constraints on the Evolution of Neurons, The Wiley-Blackwell Handbook of Evolutionary Neuroscience, Pages: 153-172, ISBN: 9781119994695
© 2017 John Wiley & Sons, Ltd. All rights reserved. This chapter focuses on two fundamental constraints that apply to any form of information processing system, be it a cell, a brain or a computer: Noise (random variability) and Energy (metabolic demand). It shows how these two constraints are fundamentally limited by the basic biophysical properties of the brain's building blocks (protein, fats, and salty water) and link nervous system structure to function. The understanding of the interdependence of information and energy has profoundly influenced the development of efficient telecommunication systems and computers. Noise diminishes the capacity to receive, process, and direct information, the key tasks of the brain. Investing in the brain's design can reduce the effects of noise, but this investment often increases energetic requirements, which is likely to be evolutionary unfavourable. The stochasticity of the system becomes critical when its inherent randomness makes it operationally infeasible, that is, when random action potential (APs) become as common as evoked APs.
Makin T, de Vignemont F, Faisal AA, Neurocognitive considerations to the embodiment of technology, Nature Biomedical Engineering, ISSN: 2157-846X
By exploiting robotics and information technology, teams of biomedical engineers are enhancing human sensory and motor abilities. Such augmentation technology ― to be worn, implanted or ingested ― aims to both restore and improve existing human capabilities (such as faster running, via exoskeletons), and to add new ones (for example, a ‘radar sense’). The development of augmentation technology is driven by rapid advances in human–machine interfaces, energy storage and mobile computing. Although engineers are embracing body augmentation from a technical perspective, little attention has been devoted to how the human brain might support such technological innovation. In this Comment, we highlight expected neurocognitive bottlenecks imposed by brain plasticity, adaptation and learning that could impact the design and performance of sensory and motor augmentation technology. We call for further consideration of how human–machine integration can be best achieved.
Corrales-Carvajal VM, Faisal AA, Ribeiro C, 2016, Internal states drive nutrient homeostatis by modulating exploration-exploitation trade-off, ELIFE, Vol: 5, ISSN: 2050-084X
Bergsma A, Lobo-Prat J, Vroom E, et al., 2016, 1st Workshop on Upper-Extremity Assistive Technology for People with Duchenne: State of the art, emerging avenues, and challenges: April 27th 2015, London, United Kingdom., Neuromuscul Disord, Vol: 26, Pages: 386-393
Lorenz R, Monti RP, Violante IR, et al., 2016, The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI, NEUROIMAGE, Vol: 129, Pages: 320-334, ISSN: 1053-8119
Faisal AA, 2016, Action Grammars - Extraction, recognition and prediction of movement primitives in tool-making, 85th Annual Meeting of the American-Association-of-Physical-Anthropologists, Publisher: WILEY-BLACKWELL, Pages: 141-141, ISSN: 0002-9483
Lorenz R, Monti RP, Hampshire A, et al., 2016, Towards tailoring non-invasive brain stimulation using real-time fMRI and Bayesian optimization, 6th International Workshop on Pattern Recognition in Neuroimaging (PRNI), Publisher: IEEE, Pages: 49-52, ISSN: 2330-9989
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