Motor Disabled People Research Articles

State-Based Decoding of Continuous Hand Movements Using EEG Signals

Recently, the advent of the non-invasive brain-computer interface (BCI) for continuous decoding of upper limb motions opens a new horizon for motor-disabled people. However, the performance of discrete-decoding BCIs based on discriminating different brain states are still more robust. In this study, we aimed to cascade a discrete state decoder with a continuous decoder to enhance the prediction of hand trajectories. EEG data were recorded from nine healthy subjects performing a center-out task with four orthogonal targets on the horizontal plane. The pre-movement data of each trial has been used for training a binary discrete decoder which identifies the axis of the movement based on common spatial pattern (CSP) features. Two non-parametric continuous decoders based on Gaussian process regression (GPR) have been designed for continuous decoding of hand movements along each axis using the envelope features of EEG signals in six frequency bands. In addition to those four principal orthogonal targets, some targets at random directions on the horizontal plane were recorded to evaluate the generalizability of the proposed model. The discrete decoder attained the average binary classification of 97.1% for discriminating movement along the x-axis and y-axis. The proposed state-based method achieved the mean correlation coefficient of 0.54 between actual and predicted trajectories for principal targets over all subjects. The trajectories of random targets were also decoded with a mean correlation of 0.37. The generalizability of the proposed paradigm proved by the findings of this study could open new possibilities in developing novel types of neuroprostheses for rehabilitation purposes.

Eyes can draw: A high-fidelity free-eye drawing method with unimodal gaze control

EyeCompass is a novel free-eye drawing system enabling high-fidelity and efficient free-eye drawing through unimodal gaze control, addressing the bottlenecks of gaze-control drawing. EyeCompass helps people to draw using only their eyes, which is of value to people with motor disabilities. Currently, there is no effective gaze-control drawing application due to multiple challenges including involuntary eye movements, conflicts between visuomotor transformation and ocular observation, gaze trajectory control, and inherent eye-tracking errors. EyeCompass addresses this using two initial gaze-control drawing mechanisms: brush damping dynamics and the gaze-oriented method. The user experiments compare the existing gaze-control drawing method and EyeCompass, showing significant improvements in the drawing performance of the mechanisms concerned. The field study conducted with motor-disabled people produced various creative graphics and indicates good usability of the system. Our studies indicate that EyeCompass is a high-fidelity, accurate, feasible free-eye drawing method for creating artistic works via unimodal gaze control.

Unsupervised feature extraction with autoencoders for EEG based multiclass motor imagery BCI

Decoding of motor imagery (MI) from Electroencephalogram (EEG) is an important component of BCI system that helps motor-disabled people interact with the outside world via external devices. One of the main issues associated with the multiclass classification of MI based EEG is the informative confusion due to non-stationary characteristics of EEG data. In this work, an innovative idea of transforming EEG signal into a new domain, weight vector of autoencoder, unsupervised neural network, is proposed for the first time to solve that confusion. These weight vectors are optimized according to that particular EEG signal. The features: autoregressive coefficients (ARs), Shannon entropy (SE) and wavelet leader were extracted from the weight vector. A rectangular windowing-based feature extraction technique is implemented to capture the local features of the EEG data. Finally, extracted features were used in the support vector machine (SVM) as a classifier network. The proposed method is implemented on two openly available EEG dataset (BCI competition-III and Competition-IV) to validate the effectiveness and superiority of the proposed methodology over the newly reported methods. For four-class EEG based MI classification, the proposed technique has achieved an average test accuracy of 95.33% and 97% for dataset-IIIa from BCI-III and dataset-IIa from BCI-IV respectively. The experimental results reveal that, the proposed technique is a promising solution to improve the decoding performance of BCIs.

Towards an Accessible Use of a Brain-Computer Interfaces-Based Home Care System through a Smartphone.

This study proposes a home care system (HCS) based on a brain-computer interface (BCI) with a smartphone. The HCS provides daily help to motor-disabled people when a caregiver is not present. The aim of the study is two-fold: (1) to develop a BCI-based home care system to help end-users control their household appliances, and (2) to assess whether the architecture of the HCS is easy for motor-disabled people to use. A motion-strip is used to evoke event-related potentials (ERPs) in the brain of the user, and the system immediately processes these potentials to decode the user's intentions. The system, then, translates these intentions into application commands and sends them via Bluetooth to the user's smartphone to make an emergency call or to execute the corresponding app to emit an infrared (IR) signal to control a household appliance. Fifteen healthy and seven motor-disabled subjects (including the one with ALS) participated in the experiment. The average online accuracy was 81.8% and 78.1%, respectively. Using component N2P3 to discriminate targets from nontargets can increase the efficiency of the system. Results showed that the system allows end-users to use smartphone apps as long as they are using their brain waves. More important, only one electrode O1 is required to measure EEG signals, giving the system good practical usability. The HCS can, thus, improve the autonomy and self-reliance of its end-users.

Detection of Stressful Situations Using GSR While Driving a BCI-controlled Wheelchair.

This paper analyzes the galvanic skin response (GSR) recorded from healthy and motor disabled people while steering a robotic wheelchair (RobChair ISR-UC prototype), to infer whether GSR can help in the recognition of stressful situations. Seven healthy individuals and six individuals with motor disabilities were asked to drive the RobChair by means of a brain-computer interface in indoor office environments, including complex scenarios such as passing narrow doors, avoiding obstacles, and with situations of unexpected trajectories of the wheelchair (controlled by an operator without users knowledge). All these driving situations can trigger emotional arousals such as anxiety and stress. A method called feature-based peak detection (FBPD) was proposed for automatic detection of skin conductance response (SCR) which proved to be very effective compared to the state-of-the-art methods. We found that SCR was elicited in 100% of the occurrences of collisions (lateral scrapings) and 94% of unexpected trajectories.

Improving The Performance of Motor Imagery Based Brain-Computer Interface Using Phase Space Reconstruction.

In recent decades, motor imagery (MI) based brain-computer interface (BCI) is served as a control system or rehabilitation tool for motor disabled people. But it has limited applications because of its lower classification performance (classification accuracy, Cohen's kappa coefficient and etc.). The performance depends on the feature extraction techniques and extraction of relevant features from the brain is challenging task. The existing techniques have low classification performance and are computationally inefficient. This paper introduces phase space reconstruction (PSR) to detect various MI activities and improve the performance of the system. First, raw signals were decomposed into multiple frequency sub-bands using filter bank technique. Second, PSR was applied to each sub-band and dynamical behavior of the brain activities has been analyzed. The optimal parameters (time delay and embedding dimension) of PSR were calculated by average mutual information (AMI) and false nearest neighbors (FNN) methods. The time delay and embedding dimension extracted significant features related to MI activities. The significant features were fed into multi-class support vector machine (SVM) and performance of the classifier was evaluated. The performance of the system is based on classification accuracy (%CA) and Cohen's kappa coefficient (K). The proposed algorithm and classifier were tested on BCI competition-2005, MI dataset-III-a. The results show that the proposed technique increases the classification accuracy by 3.7% and achieved higher performance (%CA = 89.20% and K= 0.85).

A Review of Error-Related Potential-Based Brain–Computer Interfaces for Motor Impaired People

Regaining the lost functionality of limbs is the top priority for people with motor skills impairment as it directly affects their ability to execute activities of daily living and hence, worsens their quality of life. In the last two decades, a great deal of research has focused on error-related potential (ErrP) based brain-computer interfaces (BCIs). Many applications have been developed to assist motor-impaired people in their rehabilitation and among these are robots, spellers, gesture recognition systems, and brain-controlled wheelchairs. In this paper, we present a review of various ErrP based BCI that can potentially aid motor-disabled people in their rehabilitation and execution of their daily activities. First, we describe the ErrP phenomenon and its characteristics followed by a comprehensive application-driven discussion on ErrP based rehabilitation and assistive strategies for motor-impaired people, including studies conducted since the inception of ErrP to the current state-of-the-art applications. Lastly, we discuss the potential issues and challenges being faced by current state-of-the-art applications as well as important future pathways and research directions that might be adopted for advanced ErrP-BCIs used in clinical settings.

Towards an accessible use of smartphone-based social networks through brain-computer interfaces

This study presents an asynchronous P300-based Brain–Computer Interface (BCI) system for controlling social networking features of a smartphone. There are very few BCI studies based on these mobile devices and, to the best of our knowledge, none of them supports networking applications or are focused on an assistive context, failing to test their systems with motor-disabled users. Therefore, the aim of the present study is twofold: (i) to design and develop an asynchronous P300-based BCI system that allows users to control Twitter and Telegram in an Android device; and (ii) to test the usefulness of the developed system with a motor-disabled population in order to meet their daily communication needs. Row-col paradigm (RCP) is used in order to elicitate the P300 potentials in the scalp of the user, which are immediately processed for decoding the user’s intentions. The expert system integrates a decision-making stage that analyzes the attention of the user in real-time, providing a comprehensive and asynchronous control. These intentions are then translated into application commands and sent via Bluetooth to the mobile device, which interprets them and provides visual feedback to the user. During the assessment, both qualitative and quantitative metrics were obtained, and a comparison among other state-ofthe-art studies was performed as well. The system was tested with 10 healthy control subjects and 18 motor-disabled subjects, reaching average online accuracies of 92.3% and 80.6%, respectively. Results suggest that the system allows users to successfully control two socializing features of a smartphone, bridging the accessibility gap in these trending devices. Our proposal could become a useful tool within households, rehabilitation centers or even companies, opening up new ways to support the integration of motor-disabled people, and making an impact in their quality of life by improving personal autonomy and self-dependence.

Development of a Gesture-Based Game Applying Participatory Design to Reflect Values of Manual Wheelchair Users

Wheelchair users have been benefited from Natural User Interface (NUI) games because gesture-based applications can help motor disabled people. Previous work showed that considering values and the social context of these users improve game enjoyment. However, the literature lacks on studies that address games as a tool to approach personal values of people with physical disabilities. Participatory design encompasses techniques that allow absorbing and reflecting values of users into technologies. We developed a gesture-based game using participatory design addressing values of wheelchair users. To manage the development of our game, we permitted creativity and flexibility to the designers. Our design is aligned to the Game SCRUM and make use of concepts from the Creative Process. The products of each stage of the design that we applied are both a gesture-based game and its evaluation. We tested the enjoyment (immersion, difficult while playing, etc.) of users for the game that we developed thought game-based quantitative and qualitative analyses. Our results indicate that the game was able to provide a satisfactory entertaining experience to the users.

How to make an electronic library accessible

Purpose The purpose of this paper is to identify important principles which should be applied to electronic library websites to make them usable for all people. Design/methodology/approach The goal of this paper was to make the simplified user interface of Hungarian Electronic Library (VMEK) more accessible and usable by leveraging the latest technologies, standards and recommendations. Vision-impaired and motor-disabled people were also involved in brainstorming and collecting ideas during the design phase and in testing the implemented website. Findings This paper showed that the perspicuity of the Web page is greatly improved by semantically correct HTML codes, clearly defined links and alt attributes, hotkeys and typographic principles. Practical implications The paper presents the design principles of electronic library Web pages which can be applied by Web developers and content managers. The paper identifies design principles, which improve the perspicuity of user interfaces to a great extent (especially in the case of blind users); draws attention to the typographic principles, which promote reading and understanding documents; and recommends guidelines for developing electronic library home pages and managing the content of these home pages. Originality/value This paper bridges the gap between the information and library science field and the Web accessibility and usability field. Based on brainstorming results where people with various kinds of disabilities were involved, the paper gives 11 recommendations which should be taken into account while designing and developing electronic library websites to ensure equal access to their services and documents.

Brain-Computer Interface (BCI) Development for Motor Disabled People Integration in the Manufacturing SME

The motor disability is a limitation for people who suffer to get a job and be integrated into the country's industries. A lot of tools have been developed for rehabilitation or improving their motor skills, but not to be included in the industries. This paper presents a proposal for the motor disabled people inclusion through the development of a brain-computer interface (BCI) that, based on a device that captures real-time electroencephalogram (EEG) brainwave, can take decisions or activities without the need of movement. This research was conducted with various people in small and medium enterprises (SMEs) in the manufacturing sector, obtaining satisfactory results. Experimental data show the accuracy of the device, from the training stage to the testing stage.

Using Augmented Reality and Internet of Things to improve accessibility of people with motor disabilities in the context of Smart Cities

Smart Cities need to be designed to allow the inclusion of all kinds of citizens. For instance, motor disabled people like wheelchair users may have problems to interact with the city. Internet of Things (IoT) technologies provide the tools to include all citizens in the Smart City context. For example, wheelchair users may not be able to reach items placed beyond their arm’s length, limiting their independence in everyday activities like shopping, or visiting libraries. We have developed a system that enables wheelchair users to interact with items placed beyond their arm’s length, with the help of Augmented Reality (AR) and Radio Frequency Identification (RFID) technologies. Our proposed system is an interactive AR application that runs on different interfaces, allowing the user to digitally interact with the physical items on the shelf, thanks to an updated inventory provided by an RFID system. The resulting experience is close to being able to browse a shelf, clicking on it and obtaining information about the items it contains, allowing wheelchair users to shop independently, and providing autonomy in their everyday activities. Fourteen wheelchair users with different degrees of impairment have participated in the study and development of the system. The evaluation results show promising results towards more independence of wheelchair users, providing an opportunity for equality improvement.

Conscious design and new technologies for the optimization of therapeutic space

The project Casa pre-dimissioni is aimed at facilitating the recovery of the daily life by whom suffers of disability, adapting domestic spaces to the specific needs of use. The project was developed, under the aegis of the EU research programme DE3207 FACILE: Support tools for housing design and management, integrated with telematics system and services, devoted to disabled and elderly people (TIDE Program, European Commission DG XIII). The main aim of the project was to define and to implement systems that are integrated with information and communications technologies, and with the home-automation products and equipment designed to improve the quality of life of elderly people and of motor-disabled people (paraplegics and cognitive impaired people) in their homes.

Brain-Machine Interfaces: The Perception-Action Closed Loop: A Two-Learner System

A brain-machine interface (BMI) is about transforming neural activity into action and sensation into perception (Figure 1). In a BMI system, neural signals recorded from the brain are fed into a decoding algorithm that translates these signals into motor outputs to control a variety of practical devices for motor-disabled people [1]-[5]. Feedback from the prosthetic device, conveyed to the user either via normal sensory pathways or directly through brain stimulation, establishes a closed control loop.

Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain–computer interface

Motor imagery (MI) tasks classification provides an important basis for designing brain–computer interface (BCI) systems. If the MI tasks are reliably distinguished through identifying typical patterns in electroencephalography (EEG) data, a motor disabled people could communicate with a device by composing sequences of these mental states. In our earlier study, we developed a cross-correlation based logistic regression (CC-LR) algorithm for the classification of MI tasks for BCI applications, but its performance was not satisfactory. This study develops a modified version of the CC-LR algorithm exploring a suitable feature set that can improve the performance. The modified CC-LR algorithm uses the C3 electrode channel (in the international 10–20 system) as a reference channel for the cross-correlation (CC) technique and applies three diverse feature sets separately, as the input to the logistic regression (LR) classifier. The present algorithm investigates which feature set is the best to characterize the distribution of MI tasks based EEG data. This study also provides an insight into how to select a reference channel for the CC technique with EEG signals considering the anatomical structure of the human brain. The proposed algorithm is compared with eight of the most recently reported well-known methods including the BCI III Winner algorithm. The findings of this study indicate that the modified CC-LR algorithm has potential to improve the identification performance of MI tasks in BCI systems. The results demonstrate that the proposed technique provides a classification improvement over the existing methods tested.

Disability management in agriculture in developing countries

International Journal of Therapy and RehabilitationVol. 20, No. 6 OpinionDisability management in agriculture in developing countriesGajendra Narayan karnaGajendra Narayan karnaSearch for more papers by this authorGajendra Narayan karnaPublished Online:29 Sep 2013https://doi.org/10.12968/ijtr.2013.20.6.278AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References Allen PB, Frick MJ, Field WE (1995) The safety education training needs of farmers and ranchers with physical disabilities. J Agricult Edu 36 (3): 50–6 Crossref, Google ScholarBaldwin ML, Johnson WG (1994) Labor market discrimination against men with disabilities. J Human Resource 29 : 1–19 Crossref, Google ScholarBonnet M (1997) Special: Empowering the rural disabled in Asia and the Pacific--Motor disabled people in the agricultural and rural sector in Cambodia. www.fao.org/sd/PPdirect/PPan0011.htm (accessed 28 May 2013) Google ScholarFilmer D (2008) disability, poverty, and schooling in developing countries: results from 14 household surveys. World Bank Econom Rev 22 (1): 141–163 Crossref, Google ScholarFood and Agricultural Organization of the United Nations (2003) Addressing Extension and Training Needs of Farmers with Physical Disabilities: A Case Study of the Islamic Republic of Iran. Rome, Italy Google ScholarMathew SN, Field WE, French BF (2011) Secondary injury potential of assistive technologies used by farmers with disabilities: findings from case studies. J Agromedicine 16 (3): 210–25 Crossref, Google ScholarMcNamara J, Duane DJ, Whelan S, Connolly L (2007) Preliminary investigation of the incidence and impact of disability on irish farms. Assoc Agricult Extension Edu 14 (2): 21–33 Google ScholarThe Hindu (2013) Symposium on disability management in farming. http://tinyurl.com/osct5r3 (accessed 28 May 2013) Google ScholarUnited Nations (2006) UN Enable: World Programme of Action Concerning Disabled Persons. New York, US Google ScholarWorld Health Organization (2011) World Report on Disability. Geneva, Switzerland Google Scholar FiguresReferencesRelatedDetails 1 June 2013Volume 20Issue 6ISSN (online): 1759-779X Metrics History Published online 29 September 2013 Published in print 1 June 2013 Information© MA Healthcare LimitedPDF download

Real Time Break Point Detection Technique (RBPD) in Computer Mouse Trajectory

This paper presents a new technique for detecting break points in computer mouse trajectories called Real time Break Point Detection (RBPD). The mouse trajectory from motor-disabled people contains a lot of break points. RBPD can detect these break points by adapting to the user’s tremor level. To test the effectiveness of this new technique, an experimental study involving five participants suffering from Parkinson’s disease (PD) was conducted. The test results showed that RBPD technique is effectiveness in detecting break points. DOI: http://dx.doi.org/10.11591/telkomnika.v11i5.2507 Full Text: PDF

A gyro sensor based computer mouse with a USB interface: A technical aid for motor-disabled people

The aim of this study was to develop an alternative computer mouse for disabled persons. The mouse developed is a flexible input device with a multi-click function, which can be attached to a selec ...

The voice recognition system as a way of accessing the computer for people with physical standards as usual

This article introduces the results of an evaluation of a voice recognition system used as a tool for computer access in dictation activities. The software was applied to three groups: motor-disabled people suffering severe speech disorders; motor-disabled people without speech difficulties and a t hird, control group of non-disabled people. We found evidence that the tool results are acceptable, as task performance improves as both the subject and the system receive training. The system showed similar performance for the three groups. It also had an intrinsic rehabilitating value for those with speech disorders, as the system's feedback forced them to improve their pronunciation.

SVM-based recursive feature elimination to compare phase synchronization computed from broadband and narrowband EEG signals in Brain–Computer Interfaces

To allow motor-disabled people for communication, Brain–Computer Interfaces (BCIs) are being developed. Such a communication does not depend on the brain's normal output pathways of peripheral nerves and muscles, but is based on analysis of recorded brain activity. In this paper, we compare the performance of power features and phase locking values (PLVs) computed from broadband and narrowband filtered EEG signals for discriminating 3 mental tasks in the framework of a BCI. EEG signals were recorded from 5 subjects while performing the 3 mental tasks left- and right-hand movement imagination and word generation. To reduce the total amount of features, the most discriminative features were selected in a 2-step feature selection procedure by SVM-based recursive feature elimination. Significance tests demonstrated that band power features were more discriminative when they were computed in the narrower frequency band 8–12 Hz. In case of PLV features, the discrimination of mental tasks was significantly better when they were computed from the broader 8–30 Hz frequency band, as compared to the narrower bands 8–12, 13–18 and 19–30 Hz.