Assistive Robot Arm Controlled by a P300-based Brain Machine Interface for Daily Activities

This work proposes an assistive system for everyday activities composed by a brain machine interface (BMI) based on P300 to choose a predefined task, a robot arm to perform the chosen task, and a stereo vision subsystem developed with two cameras for object recognition and coordinates calculation. The system was tested with eight healthy subjects; its results were greater BMI accuracies, lower 3D coordinates calculation error, and lower task execution time than similar systems. However, it should be tested with disabled subjects to provide more reliable end-user results. Regardless, this system is suitable to assist healthy subjects for performing reaching task to grasp objects in daily activities, and the intuitive interface would be useful for disabled subjects

EDAN - An EMG-controlled Daily Assistant To Help People With Physical Disabilities

Injuries, accidents, strokes, and other diseases can significantly degrade the capabilities to perform even the most simple activities in daily life. A large share of these cases involves neuromuscular diseases, which lead to severely reduced muscle function. However, even though affected people are no longer able to move their limbs, residual muscle function can still be existent. Previous work has shown that this residual muscular activity can suffice to apply an EMG-based user interface. In this paper, we introduce DLR's robotic wheelchair EDAN (EMG-controlled Daily Assistant), which is equipped with a torque-controlled, eight degree-of-freedom light-weight arm and a dexterous, five-fingered robotic hand. Using electromyography, muscular activity of the user is measured,processed and utilized to control both the wheelchair and the robotic manipulator. This EMG-based interface is enhanced with shared control functionality to allow for efficient and safe physical interaction with the environment

A Comprehensive Analysis on EEG Signal Classification Using Advanced Computational Analysis

Electroencephalogram (EEG) has been used in a wide array of applications to study mental disorders. Due to its non-invasive and low-cost features, EEG has become a viable instrument in Brain-Computer Interfaces (BCI). These BCI systems integrate user\u27s neural features with robotic machines to perform tasks. However, due to EEG signals being highly dynamic in nature, BCI systems are still unstable and prone to unanticipated noise interference. An important application of this technology is to help facilitate the lives of the tetraplegic through assimilating human brain impulses and converting them into mechanical motion. However, BCI systems are remarkably challenging to implement as recorded brain signals can be unreliable and vary in pattern throughout time. In the initial work, a novel classifier structure is proposed to classify different types of imaginary motions (left hand, right hand, and imagination of words starting with the same letter) across multiple sessions using an optimized set of electrodes for each user. The proposed technique uses raw brain signals obtained utilizing 32 electrodes and classifies the imaginary motions using Artificial Neural Networks (ANN). To enhance the classification rate and optimize the set of electrodes of each subject, a majority voting system combining a set of simple ANNs is used. This electrode optimization technique achieved classification accuracies of 69.83%, 94.04% and 84.56% respectively for the three subjects considered in this work. In the second work, the signal variations are studied in detail for a large EEG dataset. Using the Independent Component Analysis (ICA) with a dynamic threshold model, noise features were filtered. The data was classified to a high precision of more than 94% using artificial neural networks. A decreased variance in classification validated both, the effectiveness of the proposed dynamic threshold systems and the presence of higher concentrations of noise in data for specific subjects. Using this variance and classification accuracy, subjects were separated into two groups. The lower accuracy group was found to have an increased variance in classification. To confirm these results, a Kaiser windowing technique was used to compute the signal-to-noise ratio (SNR) for all subjects and a low SNR was obtained for all EEG signals pertaining to the group with the poor data classification. This work not only establishes a direct relationship between high signal variance, low SNR, and poor signal classification but also presents classification results that are significantly higher than the accuracies reported by prior studies for the same EEG user dataset

Training Needs and Development of Online AT Training for Healthcare Professionals in UK and France

Background: Assistive Technology (AT) solutions for people with disabilities has become part of mainstream care provision. Despite advantages of AT on offer, abandonment and non-compliance are challenges for healthcare professionals (HCPs), introducing this technology to clients. Studies of abandonment reveal that 1/3 of all devices provided to service users end up stored unused. Key need is training to make informed decisions about AT tailored to individual needs and circumstances. In an online survey undertaken by the ADAPT project, HPCs identified AT training needs and barriers. Currently, a programme is being developed aimed at introducing AT concepts and enhancing practices to a wide range of HCPs. Method: Survey questions explored gaps, availability, qualifications and barriers to AT training in England and France. A series of consultation meetings with ADAPT partners took place. An advisory group consisting of longstanding AT users and their formal/informal carers and HCPs (occupational therapist, speech and language therapist, psychologist and biomedical engineer) contributed to the discussions on survey findings, development and evaluation of AT training for HCPs, key content areas and means of delivery. Key results: HCPs had no AT specific qualifications (UK 94.6%; FR 81.3%) nor in-service AT training (UK 65.1%; FR 66.4%). They either did not know of AT courses (UK 63.3%) or knew that none existed (FR 72.5%). Barriers to AT training were mainly local training (UK 62.7%, FR 50%) and funding (UK 62.7%, FR 55.7%). Some training priorities were clearer for French HCPs – overall knowledge of AT devices (82.1%, UK 45.8%), customization of AT (65.3%, UK 30.1%), assessing patient holistically (53.4%, UK 25.3%), educating patient/carers (56.5%, UK 28.3%) (p < 0.001). Variances may be due to differing country-specific HCP education approach. A third of both groups highlighted also abandonment, client follow-up, powered wheelchair training and prescribing AT. To bridge gaps in knowledge and identified training needs of HCPs, the online interactive training programme starts by introducing foundations of AT, including definitions, types/uses of AT, legislation/policies and AT in practice. More specialist units build and expand on specific areas, e.g. AT for mobility, communication, assessment and evidence-based practice. The biopsychosocial model of Health and World Health Organisation’s (WHO) International Classification of Functioning, Disability and Health (ICF) framework underpin development of content. ICF shifts focus from disability to health and functioning, in line with a social model of rehabilitation. E-learning comprises existing videos, AT textbook material and bespoke animated presentations. Selfassessment and evaluation of training are embedded and learners receive certificate of completion. Training was piloted to a group of HCPs trainees and postregistration HCPs who commented on relevance of AT content, clarity, accessibility of presentation, and usefulness. Users found training very useful, especially legislation/policies and AT literature. Conclusion: Overall, survey results suggest that both UK and French HCPs’ training on AT solutions is limited and highly variable. There is need for crosschannel AT professional competencies, availability of work-based training and funding support. Development of online, interactive training aims to increase professional confidence and competence in this area as well as the evidence base for AT

A Survey of Assistive Technology (AT) Knowledge and Experiences of Healthcare Professionals in the UK and France: Challenges and Opportunities for Workforce Development

Background: Assistive Technologies (AT) in healthcare can increase independence and quality of life for users. Concurrently, new AT devices offer opportunities for individualised care solutions. Nonetheless, AT remains under-utilised and is poorly integrated in practice by healthcare professionals (HCPs). Although occupational therapists (OTs), physiotherapists and speech and language therapists (SLTs) consider that AT solutions can offer problem-solving approaches to personalised care, they have a lesser understanding of application of AT in their practice. In this paper, we report findings of a survey on AT knowledge and experiences of HCPs in UK and France. Training needs also explored in the survey are presented in a separate paper on development of online training for the ADAPT project. Method: A survey of 37 closed/open questions was developed in English and French by a team of healthcare researchers. Content was informed by published surveys and studies. Email invitations were circulated to contacts in Health Trusts in UK and France ADAPT regions and the survey was hosted on an online platform. Knowledge questions addressed AT understanding and views of impact on user’s lives. Experience questions focussed on current practices, prescription, follow-up, abandonment and practice standards. 429 HCPs completed the survey (UK = 167; FR = 262) between June and November 2018. Key results: Participants were mainly female (UK 89.2%; FR 82.8%) and qualified 10+ years (UK 66.5%; FR 62.2%). A key group in both countries were OTs (UK 34.1%; FR 46.6%), with more physiotherapists and SLTs in UK (16.8%, 16.8%; vs. FR 6.5%, 2.3%), and more nurses in France (22.1% Vs. UK 10.8%). More HCPs were qualified to degree level in France (75.2%; UK 48.5%, p < 0.001). In terms of knowledge, all HCPs agreed that AT helps people complete otherwise difficult or impossible tasks (UK 86.2%; FR 94.3%) and that successful AT adoption always depends on support from carers, family and professionals (UK 52.7%; FR 66.2%). There were some notable differences between countries that require further exploration. For example, more French HCPs thought that AT is provided by trial and error (84.7%, UK 45.5%, p < 0.001), while more UK HCPs believed that AT promotes autonomous living (93.4%; FR 42.8%, p < 0.001). Also, more French HCPs considered that AT refers exclusively to technologically advanced electronic devices (71.8%, UK 28.8%, p <0.001). In both countries, top AT prescribers were OTs, physiotherapists and SLTs. Respondents had little/no knowledge in comparing/choosing AT (UK 86.8%; FR 76.7%) and stated they would benefit from interdisciplinary clinical standards (UK 80.8%; FR 77.1%). A third of HCPs did not know if AT users had access to adequate resources/support (UK 34.1%; FR 27.5%) and rated themselves as capable to monitor continued effective use of AT (UK 38.9%; FR 34.8%). Conclusion: Knowledge and application of AT was varied between the two countries due to differences in health care provision and support mechanisms. Survey findings suggest that HCPs recognised the value of AT for users’ improved care, but had low confidence in their ability to choose appropriate AT solutions and monitor continued use, and would welcome AT interdisciplinary clinical standards

A Literature Review of the Challenges Encountered in the Adoption of Assistive Technology (AT) and Training of Healthcare Professionals

Background: Long-term disabilities often result in loss of autonomy and social interaction. Accordingly, there is a demand for Assistive Technology (AT) devices to enable individuals to live independently for as long as possible. However, many people experience difficulties in obtaining and using AT. This paper presents findings from a narrative literature review undertaken as part of the development of AT training for healthcare professionals, one of the work areas of the ADAPT project (Assistive Devices for Empowering Disabled People through Robotic Technologies), funded by EU INTERREG France (Channel) England. The results of the review informed the design of a survey of healthcare professionals regarding their views and experiences of AT and the development of AT training. Method: The review sought to understand challenges encountered in the adoption and use of AT as well as how training of healthcare professionals in AT takes place. A narrative approach was adopted as the most appropriate way to synthesise published literature on this topic and describe its current state-of-art. Narrative reviews are considered an important educational tool in continuing professional development. An initial search was conducted via databases in the UK and France, including CINAHL, Academic Search Index, Social Sciences Citation Index, BDSP (Base de données en Santé Publique), Documentation EHESP/MSSH (Ecole des Hautes Etudes en Santé Publique/Maison des Sciences Sociales et Handicap), Cairn, Google Scholar and Pubmed. Inclusion criteria for the review included: covering issues relating to AT provision and training, English or French language, and published from 1990 onwards. Application of these criteria elicited 79 sources, including journal papers (48), reports (11), online sources (11), books (6) and conference papers (3). Sources were thematically analysed to draw out key themes. Key results: The majority of papers were from USA and Canada (27), then UK (20) and France (19). Others were from Europe (7), Australia (3), country unknown (2), and one joint UK/France publication. The main source of literature was journal papers (48), of which the most common types were practice reports (18), evaluation surveys (10) and qualitative studies (9). The review uncovered a number of key challenges related to the adoption of devices, including: difficulty defining AT across disciplines, lack of knowledge of healthcare professionals and users, obtrusiveness and stigmatisation AT users can experience when using devices, and shortfalls in communication amongst professional groups and between professionals and users. These issues can lead to abandonment of AT devices. Furthermore, substantial barriers to healthcare professionals exist, including inconsistent provision and quality of training, lack of evaluation of training, lack of resources and funding, shortage of qualified professionals to teach, and the increasingly rapid development of the technologies. Conclusion: Support, training and education for prescribers, distributors, users, and their carers is vital in the adoption and use of AT. Evidence indicates a need for comprehensive education in the AT field, as well as ongoing assessment, updates and evaluation which is embedded in programmes

Automatic motion of manipulator using sampling based motion planning algorithms - application in service robotics

The thesis presents new approaches for autonomous motion execution of a robotic arm. The calculation of the motion is called motion planning and requires the computation of robot arm's path. The text covers the calculation of the path and several algorithms have been therefore implemented and tested in several real scenarios. The work focuses on sampling based planners, which means that the path is created by connecting explicitly random generated points in the free space. The algorithms can be divided into three categories: those that are working in configuration space(C-Space)(C- Space is the set of all possible joint angles of a robotic arm) , the mixed approaches using both Cartesian and C-Space and those that are using only the Cartesian space. Although Cartesian space seems more appropriate, due to dimensionality, this work illustrates that the C-Space planners can achieve comparable or better results. Initially an enhanced approach for efficient collision detection in C-Space, used by the planners, is presented. Afterwards the N dimensional cuboid region, notated as Rq, is defined. The Rq configures the C-Space so that the sampling is done close to a selected, called center, cell. The approach is enhanced by the decomposition of the Cartesian space into cells. A cell is selected appropriately if: (a) is closer to the target position and (b) lies inside the constraints. Inverse kinematics(IK) are applied to calculate a centre configuration used later by the Rq. The CellBiRRT is proposed and combines all the features. Continuously mixed approaches that do not require goal configuration or an analytic solution of IK are presented. Rq regions as well as Cells are also integrated in these approaches. A Cartesian sampling based planner using quaternions for linear interpolation is also proposed and tested. The common feature of the so far algorithms is the feasibility which is normally against the optimality. Therefore an additional part of this work deals with the optimality of the path. An enhanced approach of CellBiRRT, called CellBiRRT*, is developed and promises to compute shorter paths in a reasonable time. An on-line method using both CellBiRRT and CellBiRRT* is proposed where the path of the robot arm is improved and recalculated even if sudden changes in the environment are detected. Benchmarking with the state of the art algorithms show the good performance of the proposed approaches. The good performance makes the algorithms suitable for real time applications. In this work several applications are described: Manipulative skills, an approach for an semi-autonomous control of the robot arm and a motion planning library. The motion planning library provides the necessary interface for easy use and further development of the motion planning algorithms. It can be used as the part connecting the manipulative skill designing and the motion of a robotic arm