The automated box and blocks test an autonomous assessment method of gross manual dexterity in stroke rehabilitation

Traditional motor assessment is carried out by clinicians using standard clinical tests in order to have objectivity in the evaluation, but this manual procedure is liable to the observer subjectivity. In this article, an automatic assessment system based on the Box and Blocks Test (BBT) of manual dexterity is presented. Also, the automatic test administration and the motor performance of the user is addressed. Through cameras RGB-D the execution of the test and the patient's movements are monitored. Based on colour segmentation, the cubes displaced by the user are detected and the traditional scoring is automatically calculated. Furthermore, a pilot trial in a hospital environment was conducted, to compare the automatic system and its e ectiveness with respect to the traditional one. The results support the use of automatic assessment methods of motor functionality, which in combination with robotic rehabilitation systems, could address an autonomous and objective rehabilitation process.The research leading to these results has received funding from the ROBOHEALTH-A project (DPI2013-47944-C4-1-R) funded by Spanish Ministry of Economy and Competitiveness and from the RoboCity2030-III-CM project (S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

Artificial Intelligence and the Internet of Things

"Through algorithms and artificial intelligence (AI), objects and digital services now demonstrate new skills they did not have before, right up to replacing human activity through pre-programming or by making their own decisions. As part of the internet of things, AI applications are already widely used today, for example in language processing, image recognition and the tracking and processing of data. This policy brief illustrates the potential negative and positive impacts of AI and reviews related policy strategies adopted by the UK, US, EU, as well as Canada and China. Based on an ethical approach that considers the role of AI from a democratic perspective and considering the public interest, the authors make policy recommendations that help to strengthen the positive impact of AI and to mitigate its negative consequences.

Artificial Intelligence and the Internet of Things

"Through algorithms and artificial intelligence (AI), objects and digital services now demonstrate new skills they did not have before, right up to replacing human activity through pre-programming or by making their own decisions. As part of the internet of things, AI applications are already widely used today, for example in language processing, image recognition and the tracking and processing of data. This policy brief illustrates the potential negative and positive impacts of AI and reviews related policy strategies adopted by the UK, US, EU, as well as Canada and China. Based on an ethical approach that considers the role of AI from a democratic perspective and considering the public interest, the authors make policy recommendations that help to strengthen the positive impact of AI and to mitigate its negative consequences.

Risk-Based Triggering of Bio-inspired Self-preservation to Protect Robots from Threats

Safety in autonomous systems has been mostly studied from a human-centered perspective. Besides the loads they may carry, autonomous systems are also valuable property, and self-preservation mechanisms are needed to protect them in the presence of external threats, including malicious robots and antagonistic humans. We present a biologically inspired risk-based triggering mechanism to initiate self-preservation strategies. This mechanism considers environmental and internal system factors to measure the overall risk at any moment in time, to decide whether behaviours such as fleeing or hiding are necessary, or whether the system should continue on its task. We integrated our risk-based triggering mechanism into a delivery rover that is being attacked by a drone and evaluated its effectiveness through systematic testing in a simulated environment in Robot Operating System (ROS) and Gazebo, with a variety of different randomly generated conditions. We compared the use of the triggering mechanism and different configurations of self-preservation behaviours to not having any of these. Our results show that triggering self-preservation increases the distance between the drone and the rover for many of these configurations, and, in some instances, the drone does not catch up with the rover. Our study demonstrates the benefits of embedding risk awareness and self-preservation into autonomous systems to increase their robustness, and the value of using bio-inspired engineering to find solutions in this area

Calibration of sound source localisation for robots using multiple adaptive filter models of the cerebellum

The aim of this research was to investigate the calibration of Sound Source Localisation (SSL) for robots using the adaptive filter model of the cerebellum and how this could be automatically adapted for multiple acoustic environments. The role of the cerebellum has mainly been identified in the context of motor control, and only in recent years has it been recognised that it has a wider role to play in the senses and cognition. The adaptive filter model of the cerebellum has been successfully applied to a number of robotics applications but so far none involving auditory sense. Multiple models frameworks such as MOdular Selection And Identification for Control (MOSAIC) have also been developed in the context of motor control, and this has been the inspiration for adaptation of audio calibration in multiple acoustic environments; again, application of this approach in the area of auditory sense is completely new. The thesis showed that it was possible to calibrate the output of an SSL algorithm using the adaptive filter model of the cerebellum, improving the performance compared to the uncalibrated SSL. Using an adaptation of the MOSAIC framework, and specifically using responsibility estimation, a system was developed that was able to select an appropriate set of cerebellar calibration models and to combine their outputs in proportion to how well each was able to calibrate, to improve the SSL estimate in multiple acoustic contexts, including novel contexts. The thesis also developed a responsibility predictor, also part of the MOSAIC framework, and this improved the robustness of the system to abrupt changes in context which could otherwise have resulted in a large performance error. Responsibility prediction also improved robustness to missing ground truth, which could occur in challenging environments where sensory feedback of ground truth may become impaired, which has not been addressed in the MOSAIC literature, adding to the novelty of the thesis. The utility of the so-called cerebellar chip has been further demonstrated through the development of a responsibility predictor that is based on the adaptive filter model of the cerebellum, rather than the more conventional function fitting neural network used in the literature. Lastly, it was demonstrated that the multiple cerebellar calibration architecture is capable of limited self-organising from a de-novo state, with a predetermined number of models. It was also demonstrated that the responsibility predictor could learn against its model after self-organisation, and to a limited extent, during self-organisation. The thesis addresses an important question of how a robot could improve its ability to listen in multiple, challenging acoustic environments, and recommends future work to develop this ability

End Effector for Robotic Strawberry Picker Final Design Review

In this report, we have outlined the background of the problem and need for a solution to an automated form of strawberry harvesting. The report includes our research findings, defines the scope and objectives for this project, and documents our complete design process. Also included is our final, completed prototype, and a description of the manufacturing, design verification and testing process. Also included is our conclusions and recommendations for further improvement on future iterations

Design, fabrication and stiffening of soft pneumatic robots

Although compliance allows the soft robot to be under-actuated and generalise its control, it also impacts the ability of the robot to exert forces on the environment. There is a trade-off between robots being compliant or precise and strong. Many mechanisms that change robots' stiffness on demand have been proposed, but none are perfect, usually compromising the device's compliance and restricting its motion capabilities. Keeping the above issues in mind, this thesis focuses on creating robust and reliable pneumatic actuators, that are designed to be easily manufactured with simple tools. They are optimised towards linear behaviour, which simplifies modelling and improve control strategies. The principle idea in relation to linearisation is a reinforcement strategy designed to amplify the desired, and limit the unwanted, deformation of the device. Such reinforcement can be achieved using fibres or 3D printed structures. I have shown that the linearity of the actuation is, among others, a function of the reinforcement density and shape, in that the response of dense fibre-reinforced actuators with a circular cross-section is significantly more linear than that of non-reinforced or non-circular actuators. I have explored moulding manufacturing techniques and a mixture of 3D printing and moulding. Many aspects of these techniques have been optimised for reliability, repeatability, and process simplification. I have proposed and implemented a novel moulding technique that uses disposable moulds and can easily be used by an inexperienced operator. I also tried to address the compliance-stiffness trade-off issue. As a result, I have proposed an intelligent structure that behaves differently depending on the conditions. Thanks to its properties, such a structure could be used in applications that require flexibility, but also the ability to resist external disturbances when necessary. Due to its nature, individual cells of the proposed system could be used to implement physical logic elements, resulting in embodied intelligent behaviours. As a proof-of-concept, I have demonstrated use of my actuators in several applications including prosthetic hands, octopus, and fish robots. Each of those devices benefits from a slightly different actuation system but each is based on the same core idea - fibre reinforced actuators. I have shown that the proposed design and manufacturing techniques have several advantages over the methods used so far. The manufacturing methods I developed are more reliable, repeatable, and require less manual work than the various other methods described in the literature. I have also shown that the proposed actuators can be successfully used in real-life applications. Finally, one of the most important outcomes of my research is a contribution to an orthotic device based on soft pneumatic actuators. The device has been successfully deployed, and, at the time of submission of this thesis, has been used for several months, with good results reported, by a patient