Do robotic and non-robotic arm movement training drive motor recovery after stroke by a common neural mechanism? Experimental evidence and a computational model.

Different dose-matched, upper extremity rehabilitation training techniques, including robotic and non-robotic techniques, can result in similar improvement in movement ability after stroke, suggesting they may elicit a common drive for recovery. Here we report experimental results that support the hypothesis of a common drive, and develop a computational model of a putative neural mechanism for the common drive. We compared weekly motor control recovery during robotic and unassisted movement training techniques after chronic stroke (n = 27), as assessed with quantitative measures of strength, speed, and coordination. The results showed that recovery in both groups followed an exponential time course with a time constant of about 4-5 weeks. Despite the greater range and speed of movement practiced by the robot group, motor recovery was very similar between the groups. The premise of the computational model is that improvements in motor control are caused by improvements in the ability to activate spared portions of the damaged corticospinal system, as learned by a biologically plausible search algorithm. Robot-assisted and unassisted training would in theory equally drive this search process

Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System

Recent advancement in predictive machine learning has led to its application in various use cases in manufacturing. Most research focused on maximising predictive accuracy without addressing the uncertainty associated with it. While accuracy is important, focusing primarily on it poses an overfitting danger, exposing manufacturers to risk, ultimately hindering the adoption of these techniques. In this paper, we determine the sources of uncertainty in machine learning and establish the success criteria of a machine learning system to function well under uncertainty in a cyber-physical manufacturing system (CPMS) scenario. Then, we propose a multi-agent system architecture which leverages probabilistic machine learning as a means of achieving such criteria. We propose possible scenarios for which our architecture is useful and discuss future work. Experimentally, we implement Bayesian Neural Networks for multi-tasks classification on a public dataset for the real-time condition monitoring of a hydraulic system and demonstrate the usefulness of the system by evaluating the probability of a prediction being accurate given its uncertainty. We deploy these models using our proposed agent-based framework and integrate web visualisation to demonstrate its real-time feasibility

The politics of heroes through the prism of popular heroism

This is the author accepted manuscript. The final version is available from Palgrave Macmillan via the DOI in this record.In modern day Britain, the discourse of national heroification is routinely utilised by politicians, educationalists and cultural industry professionals, whilst also being a popular concept to describe deserving ‘do-gooders’ who contribute to British society in a myriad of ways. We argue that although this heroification discourse is enacted as a discursive device of encouraging politically and morally desirable behaviour, it is dissociated from the largely under-explored facets of contemporary popular heroism. To compensate for this gap, this paper explores public preferences for heroes using survey data representative of British adults. This analysis demonstrates a conceptual stretching in the understanding of heroism, and allows identifying age- and gender-linked dynamics which effect public choices of heroes. In particular, we demonstrate that age above all determines the preference for having a hero, but does not explain preferences for specific hero-types. The focus on gender illustrates that the landscape of popular heroism reproduces a male-dominated bias which exists in the wider political and cultural heroification discourse. Simultaneously, our study shows that if national heroification discourse in Britain remains male-centric, the landscape of popular heroism is characterised by a gendered trend towards privatisation of heroes being particularly prominent amongst women. In the conclusion, this paper argues for a conceptual revision and re-gendering of the national heroification discourse as a step towards both empirically grounded, and age- and gender-sensitive politics of heroes and heroines.AHR

Some Key Problems for Robot-Assisted Movement Therapy Research: A Perspective from the University of California at Irvine

The field of robot-assisted movement therapy grew rapidly over the past ten years. In this paper we discuss three problems that the field will likely need to address in order to continue to flourish. These problems are to: 1) define the specific benefits of robotic actuation 2) increase the magnitude of functional benefits of robotic training; and 3) identify the mechanisms of motor learning in robot-manipulated environments. We review recent research in our laboratory that is addressing these problems. These projects are identifying motor learning tasks that robotic assistance can enhance, developing non-robotic technology when appropriate, and optimizing the forms of robotic assistance to the motor learning properties of humans

Comparison of 3D, Assist-as-Needed Robotic Arm/Hand Movement Training Provided with Pneu-WREX to Conventional Table Top Therapy Following Chronic Stroke

ObjectiveRobot-assisted movement training can help individuals with stroke reduce arm and hand impairment, but robot therapy is typically only about as effective as conventional therapy. Refining the way that robots assist during training may make them more effective than conventional therapy. Here we measured the therapeutic effect of a robot that required individuals with a stroke to achieve virtual tasks in three dimensions against gravity.DesignThe robot continuously estimated how much assistance patients needed to perform the tasks and provided slightly less assistance than needed in order to reduce patient slacking. Individuals with a chronic stroke (n = 26, baseline upper extremity Fugl-Meyer score = 23 ± 8) were randomized into two groups and underwent 24 one hour training sessions over 2 months. One group received the assist-as-needed robot training and the other received conventional table top therapy with the supervision of a physical therapist.ResultsTraining helped both groups significantly reduce their motor impairment, as measured by the primary outcome measure, the Fugl-Meyer score, but the improvement was small (3.0 ± 4.9 points for robot therapy, versus 0.9 ± 1.7 for conventional therapy). There was a trend for greater reduction for the robot trained group (p = 0.07). The robot group largely sustained this gain at the three-month follow-up. The robot-trained group also experienced significant improvements in Box and Blocks score and hand grip strength, while the control group did not, but these improvements were not sustained at follow-up. In addition, the robot-trained group showed a trend toward greater improvement in sensory function, as measured by the Nottingham Sensory Test (p = 0.06).ConclusionsThese results suggest that, in patients with chronic stroke and moderate-severe deficits, assisting in three dimensional virtual tasks with an assist-as-needed controller may make robotic training more effective than conventional table top training