572 research outputs found

    Evaluation of proportional and discrete shared control paradigms for low resolution user inputs

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    Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

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    In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

    Explainable shared control in assistive robotics

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    Shared control plays a pivotal role in designing assistive robots to complement human capabilities during everyday tasks. However, traditional shared control relies on users forming an accurate mental model of expected robot behaviour. Without this accurate mental image, users may encounter confusion or frustration whenever their actions do not elicit the intended system response, forming a misalignment between the respective internal models of the robot and human. The Explainable Shared Control paradigm introduced in this thesis attempts to resolve such model misalignment by jointly considering assistance and transparency. There are two perspectives of transparency to Explainable Shared Control: the human's and the robot's. Augmented reality is presented as an integral component that addresses the human viewpoint by visually unveiling the robot's internal mechanisms. Whilst the robot perspective requires an awareness of human "intent", and so a clustering framework composed of a deep generative model is developed for human intention inference. Both transparency constructs are implemented atop a real assistive robotic wheelchair and tested with human users. An augmented reality headset is incorporated into the robotic wheelchair and different interface options are evaluated across two user studies to explore their influence on mental model accuracy. Experimental results indicate that this setup facilitates transparent assistance by improving recovery times from adverse events associated with model misalignment. As for human intention inference, the clustering framework is applied to a dataset collected from users operating the robotic wheelchair. Findings from this experiment demonstrate that the learnt clusters are interpretable and meaningful representations of human intent. This thesis serves as a first step in the interdisciplinary area of Explainable Shared Control. The contributions to shared control, augmented reality and representation learning contained within this thesis are likely to help future research advance the proposed paradigm, and thus bolster the prevalence of assistive robots.Open Acces

    Shared Control for Wheelchair Interfaces

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    Independent mobility is fundamental to the quality of life of people with impairment. Most people with severe mobility impairments, whether congenital, e.g., from cerebral palsy, or acquired, e.g., from spinal cord injury, are prescribed a wheelchair. A small yet significant number of people are unable to use a typical powered wheelchair controlled with a joystick. Instead, some of these people require alternative interfaces such as a head- array or Sip/Puff switch to drive their powered wheelchairs. However, these alternative interfaces do not work for everyone and often cause frustration, fatigue and collisions. This thesis develops a novel technique to help improve the usability of some of these alternative interfaces, in particular, the head-array and Sip/Puff switch. Control is shared between a powered wheelchair user, using an alternative interface and a pow- ered wheelchair fitted with sensors. This shared control then produces a resulting motion that is close to what the user desires to do but a motion that is also safe. A path planning algorithm on the wheelchair is implemented using techniques in mo- bile robotics. Afterwards, the output of the path planning algorithm and the user’s com- mand are both modelled as random variables. These random variables are then blended in a joint probability distribution where the final velocity to the wheelchair is the one that maximises the joint probability distribution. The performance of the probabilistic approach to blending the user’s inputs with the output of a path planner, is benchmarked against the most common form of shared control called linear blending. The benchmarking consists of several experiments with end users both in a simulated world and in the real-world. The thesis concludes that probabilistic shared control provides safer motion compared with the traditional shared control for difficult tasks and hard-to-use interfaces

    Goal-recognition-based adaptive brain-computer interface for navigating immersive robotic systems

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    © 2017 IOP Publishing Ltd. Objective. This work proposes principled strategies for self-adaptations in EEG-based Brain-computer interfaces (BCIs) as a way out of the bandwidth bottleneck resulting from the considerable mismatch between the low-bandwidth interface and the bandwidth-hungry application, and a way to enable fluent and intuitive interaction in embodiment systems. The main focus is laid upon inferring the hidden target goals of users while navigating in a remote environment as a basis for possible adaptations. Approach. To reason about possible user goals, a general user-agnostic Bayesian update rule is devised to be recursively applied upon the arrival of evidences, i.e. user input and user gaze. Experiments were conducted with healthy subjects within robotic embodiment settings to evaluate the proposed method. These experiments varied along three factors: the type of the robot/environment (simulated and physical), the type of the interface (keyboard or BCI), and the way goal recognition (GR) is used to guide a simple shared control (SC) driving scheme. Main results. Our results show that the proposed GR algorithm is able to track and infer the hidden user goals with relatively high precision and recall. Further, the realized SC driving scheme benefits from the output of the GR system and is able to reduce the user effort needed to accomplish the assigned tasks. Despite the fact that the BCI requires higher effort compared to the keyboard conditions, most subjects were able to complete the assigned tasks, and the proposed GR system is additionally shown able to handle the uncertainty in user input during SSVEP-based interaction. The SC application of the belief vector indicates that the benefits of the GR module are more pronounced for BCIs, compared to the keyboard interface. Significance. Being based on intuitive heuristics that model the behavior of the general population during the execution of navigation tasks, the proposed GR method can be used without prior tuning for the individual users. The proposed methods can be easily integrated in devising more advanced SC schemes and/or strategies for automatic BCI self-adaptations
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