Enabling self-directed computer use for individuals with cerebral palsy: a systematic review of available assistive devices and technologies

Aim  The purpose of this study was to systematically review published evidence on the development, use, and effectiveness of devices and technologies that enable or enhance self-directed computer access by individuals with cerebral palsy (CP). Methods  Nine electronic databases were searched using keywords ‘computer’, ‘software’, ‘spastic’, ‘athetoid’, and ‘cerebral palsy’; the reference lists of articles thus identified were also searched. Thirty articles were selected for review, with 23 reports of development and usability testing of devices and seven evaluations of algorithms to increase computer recognition of input and cursor movements. Results  Twenty-four studies had fewer than 10 participants with CP, with a wide age range of 5 to 77 years. Computer task performance was usually tested, but only three groups sought participant feedback on ease and comfort of use. International standards exist to evaluate effectiveness of non-keyboard devices, but only one group undertook this testing. None of the study designs were higher than American Academy for Cerebral Palsy and Developmental Medicine level IV. Interpretation  Access solutions for individuals with CP are in the early stages of development. Future work should include assessment of end-user comfort, effort, and performance as well as design features. Engaging users and therapists when designing and evaluating technologies to enhance computer access may increase acceptance and improve performance

The Morse Code Room: Applicability of the Chinese Room Argument to Spiking Neural Networks

The Chinese room argument (CRA) was first stated in 1980. Since then computer technologies have improved and today spiking neural networks (SNNs) are “arguably the only viable option if one wants to understand how the brain computes.” (Tavanei et.al. 2019: 47) SNNs differ in various important respects from the digital computers the CRA was directed against. The objective of the present work is to explore whether the CRA applies to SNNs. In the first chapter I am going to discuss computationalism, the Chinese room argument and give a brief overview over spiking neural networks. The second chapter is going to be considered with five important differences between SNNs and digital computers: (1) Massive parallelism, (2) subsymbolic computation, (3) machine learning, (4) analogue representation and (5) temporal encoding. I am going to finish by concluding that, besides minor limitations, the Chinese room argument can be applied to spiking neural networks.:1 Introduction 2 Theoretical background 2.I Strong AI: Computationalism 2.II The Chinese room argument 2.III Spiking neural networks 3 Applicability to spiking neural networks 3.I Massive parallelism 3.II Subsymbolic computation 3.III Machine learning 3.IV Analogue representation 3.V Temporal encoding 3.VI The Morse code room and its replies 3.VII Some more general considerations regarding hardware and software 4 ConclusionDas Argument vom chinesischen Zimmer wurde erstmals 1980 veröffentlicht. Seit dieser Zeit hat sich die Computertechnologie stark weiterentwickelt und die heute viel beachteten gepulsten neuronalen Netze ähneln stark dem Aufbau und der Arbeitsweise biologischer Gehirne. Gepulste neuronale Netze unterscheiden sich in verschiedenen wichtigen Aspekten von den digitalen Computern, gegen die die CRA gerichtet war. Das Ziel der vorliegenden Arbeit ist es, zu untersuchen, ob das Argument vom chinesischen Zimmer auf gepulste neuronale Netze anwendbar ist. Im ersten Kapitel werde ich den Computer-Funktionalismus und das Argument des chinesischen Zimmers erörtern und einen kurzen Überblick über gepulste neuronale Netze geben. Das zweite Kapitel befasst sich mit fünf wichtigen Unterschieden zwischen gepulsten neuronalen Netzen und digitalen Computern: (1) Massive Parallelität, (2) subsymbolische Berechnung, (3) maschinelles Lernen, (4) analoge Darstellung und (5) zeitliche Kodierung. Ich werde schlussfolgern, dass das Argument des chinesischen Zimmers, abgesehen von geringfügigen Einschränkungen, auf gepulste neuronale Netze angewendet werden kann.:1 Introduction 2 Theoretical background 2.I Strong AI: Computationalism 2.II The Chinese room argument 2.III Spiking neural networks 3 Applicability to spiking neural networks 3.I Massive parallelism 3.II Subsymbolic computation 3.III Machine learning 3.IV Analogue representation 3.V Temporal encoding 3.VI The Morse code room and its replies 3.VII Some more general considerations regarding hardware and software 4 Conclusio

GPU Computing for Cognitive Robotics

This thesis presents the first investigation of the impact of GPU computing on cognitive robotics by providing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amounts of computational power, which was until recently provided mostly by standard CPU processors. CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into a highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. This impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This thesis presents several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity enabling the conducting of the novel experiments described herein.European Commission Seventh Framework Programm

A Framework for Coupled Simulations of Robots and Spiking Neuronal Networks

Bio-inspired robots still rely on classic robot control although advances in neurophysiology allow adaptation to control as well. However, the connection of a robot to spiking neuronal networks needs adjustments for each purpose and requires frequent adaptation during an iterative development. Existing approaches cannot bridge the gap between robotics and neuroscience or do not account for frequent adaptations. The contribution of this paper is an architecture and domain-specific language (DSL) for connecting robots to spiking neuronal networks for iterative testing in simulations, allowing neuroscientists to abstract from implementation details. The framework is implemented in a web-based platform. We validate the applicability of our approach with a case study based on image processing for controlling a four-wheeled robot in an experiment setting inspired by Braitenberg vehicles

Vector Signal Processors in Data Compression and Image Processing

The objective is to evaluate the applicability of the Vector Signal Processor to real time signal processing for data compression or manipulation. Particular emphasis has been placed on its role as a co-processor and the contribution that it might be expected to make during joint activities with the host. These activities would have the combination used as the embedded computing subsystem of a FAX machine or as an image processing unit in desk top publishing. In these cases the hypothesis is that the Vector Signal Processor would act as an accelerator for many computationally intensive applicable processes. After a review of current data compression techniques and of specialised architectures which may also be appropriate it is concluded that the Vector Signal Processor is the best option available. The operational details are then discussed. In order to be able to approximately compare experimental results with other workers a benchmarking exercise is undertaken. Following this is the core of the study which details schemes for data compression of data sources involving character symbols, line drawings, and grey scale pictures. This involves pattern matching and substitution,Transform coding and quadtrees. New encoding procedures are suggested based on Morse code for the secondary encoding of symbols and on Delta modulation for quadtrees. Image entity manipulation is discussed followed by some speculative work on neural networks and error control coding. It is concluded that some processes are well served by the Vector Signal Processor but that the lack of conditional decision making and the difficulty of performing certain arithmetic functions make the processor unwieldy in its necessary host interactions

Spatio-temporal map maintenance for extending autonomy in long-term mobile robotic tasks

Working in hazardous environments requires routine inspections in order to meet safety standards. Dangerous quantities of nuclear contamination can exist in infinitesimally small volumes. In order to confidently inspect a nuclear environment for radioactive sources, especially those which emit alpha radiation, technicians must carefully maintain detectors at a consistent velocity and distance from a source. Technicians must also take careful records of which areas have been surveyed or not are important so that no area is left unmonitored. This is a difficult, exhausting task when the coverage area is larger than an office space. An autonomous mobile robotic platform with Complete Coverage Path Planning (CCPP) can reduce dangerous exposure to humans and provide better information for Radiological Control Technicians (RCT). The developed robotic system - or RCTbot - is designed for long-term deployment with little human correction, intervention, or maintenance required. To do this, the RCTbot creates a map of the environment, continually updates it based on multiple sensor inputs, and searches its map for contamination. In nuclear environments, the areas of interest often remain spatially constant throughout the duration of an inspection and are considered temporally static. The RCTbot monitors temporally static environments but adapts to dynamic changes over time. It then uses its sensor data to update and maintain its map so no manual human intervention is necessary. The spatio-temporal map maintenance (STMM) is agnostic to the survey type, so the RCTbot system is viable for application domain other than nuclear.Mechanical Engineerin

A Predictive Fuzzy-Neural Autopilot for the Guidance of Small Motorised Marine Craft

This thesis investigates the design and evaluation of a control system, that is able to adapt quickly to changes in environment and steering characteristics. This type of controller is particularly suited for applications with wide-ranging working conditions such as those experienced by small motorised craft. A small motorised craft is assumed to be highly agile and prone to disturbances, being thrown off-course very easily when travelling at high speed 'but rather heavy and sluggish at low speeds. Unlike large vessels, the steering characteristics of the craft will change tremendously with a change in forward speed. Any new design of autopilot needs to be to compensate for these changes in dynamic characteristics to maintain near optimal levels of performance. This study identities the problems that need to be overcome and the variables involved. A self-organising fuzzy logic controller is developed and tested in simulation. This type of controller learns on-line but has certain performance limitations. The major original contribution of this research investigation is the development of an improved self-adaptive and predictive control concept, the Predictive Self-organising Fuzzy Logic Controller (PSoFLC). The novel feature of the control algorithm is that is uses a neural network as a predictive simulator of the boat's future response and this network is then incorporated into the control loop to improve the course changing, as well as course keeping capabilities of the autopilot investigated. The autopilot is tested in simulation to validate the working principle of the concept and to demonstrate the self-tuning of the control parameters. Further work is required to establish the suitability of the proposed novel concept to other control