Assistive Technologies for Individuals with a Disability from a Neurological Condition: A Narrative Review on the Multimodal Integration.

A wide range of obstacles faced by people with disabilities, such as visual impairment, motor disability, and communication difficulties, have shown significant promise for being addressed by computer vision. The state-of-the-art in computer vision-based assistive technology is examined in this report along with major future research topics and obstacles. In particular, this study explores how computer vision can be used for object recognition, navigation, facial recognition, sign language interpretation, and gesture-based control interfaces. It also discusses the benefits and drawbacks of various methodologies and technologies and offers examples of how computer vision can be incorporated into current assistive technologies to boost their efficacy. The ethical and privacy issues surrounding the use of computer vision in assistive technologies are covered in this study effort. The study also highlights the need for protocol standardization, better user-centered design, and the assessment of real-world effectiveness as future research objectives for improving the use of computer vision in assistive technology. Overall, this paper sheds light on how computer vision might completely alter the world of assistive technologies for people with impairments.

Neurological disorders such as Parkinson's disease, stroke, and spinal cord injury can pose significant threats to human mortality, morbidity, and functional independence. Brain-Computer Interface (BCI) technology, which facilitates direct communication between the brain and external devices, emerges as an innovative key to unlocking neurological conditions, demonstrating significant promise in this context. This comprehensive review uniquely synthesizes the latest advancements in BCI research across multiple neurological disorders, offering an interdisciplinary perspective on both clinical applications and emerging technologies. We explore the progress in BCI research and its applications in addressing various neurological conditions, with a particular focus on recent clinical studies and prospective developments. Initially, the review provides an up-to-date overview of BCI technology, encompassing its classification, operational principles, and prevalent paradigms. It then critically examines specific BCI applications in movement disorders, disorders of consciousness, cognitive and mental disorders, as well as sensory disorders, highlighting novel approaches and their potential impact on patient care. This review reveals emerging trends in BCI applications, such as the integration of artificial intelligence and the development of closed-loop systems, which represent significant advancements over previous technologies. The review concludes by discussing the prospects and directions of BCI technology, underscoring the need for interdisciplinary collaboration and ethical considerations. It emphasizes the importance of prioritizing bidirectional and high-performance BCIs, areas that have been underexplored in previous reviews. Additionally, we identify crucial gaps in current research, particularly in long-term clinical efficacy and the need for standardized protocols. The role of neurosurgery in spearheading the clinical translation of BCI research is highlighted. Our comprehensive analysis presents BCI technology as an innovative key to unlocking neurological disorders, offering a transformative approach to diagnosing, treating, and rehabilitating neurological conditions, with substantial potential to enhance patients' quality of life and advance the field of neurotechnology.

Terminal neurological conditions can affect millions of people worldwide and hinder them from doing their daily tasks and movements normally. Brain computer interface (BCI) is the best hope for many individuals with motor deficiencies. It will help many patients interact with the outside world and handle their daily tasks without assistance. Therefore, machine learning-based BCI systems have emerged as non-invasive techniques for reading out signals from the brain and interpreting them into commands to help those people to perform diverse limb motor tasks. This paper proposes an innovative and improved machine learning-based BCI system that analyzes EEG signals obtained from motor imagery to distinguish among various limb motor tasks based on BCI competition III dataset IVa. The proposed framework pipeline for EEG signal processing performs the following major steps. The first step uses a meta-heuristic optimization technique, called the whale optimization algorithm (WOA), to select the optimal features for discriminating between neural activity patterns. The pipeline then uses machine learning models such as LDA, k-NN, DT, RF, and LR to analyze the chosen features to enhance the precision of EEG signal analysis. The proposed BCI system, which merges the WOA as a feature selection method and the optimized k-NN classification model, demonstrated an overall accuracy of 98.6%, outperforming other machine learning models and previous techniques on the BCI competition III dataset IVa. Additionally, the EEG feature contribution in the ML classification model is reported using Explainable AI (XAI) tools, which provide insights into the individual contributions of the features in the predictions made by the model. By incorporating XAI techniques, the results of this study offer greater transparency and understanding of the relationship between the EEG features and the model’s predictions. The proposed method shows potential levels for better use in controlling diverse limb motor tasks to help people with limb impairments and support them while enhancing their quality of life.

Introduction: Assistive technology refers to a broad range of products, systems, and services designed to assist individuals to perform a particular task. These assistive devices aid neurologically impaired individuals in carrying out their activities within the community. Some examples of assistive technologies are mobility aids such as wheel chairs, walkers, canes,crutches, prosthetic and orthotic devices. Aim: To identify and highlight the role of assistive devices in enhancing mobility, Quality of Life (QOL) and functional independence in neurological conditions. Materials and Methods: In this study, the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) was used. Because of their credibility and reliability, databases such as Google Scholar, Science Direct, PubMed, and Research Gate were utilised. Nevertheless, none of the databases, such as Science Direct and Research Gate, are comprehensive or entirely thorough. Result: Assistive devices are frequently utilised in the rehab ilitation process for neurological conditions. For stroke patients, wheelchairs, walkers, and crutches are mainly utilized for mobility and everyday activities. Individuals with cerebral palsy also utilize wheelchairs, walkers, and crutches to enhance quality of life. Certain assistive devices serve both therapeutic functions and modifi cations for activities of daily living, potentially enhancing and sustaining quality of life. Conclusion: Assistive devices aid a physiotherapist in reaching optimal objectives during rehabilitation for various neurological conditions. Various assistive devices were utilized for both treatment purposes and modifi cations to activities of daily living, enhancing and preserving their quality of life. However, understanding the correct usage of these devices is crucial, along with addressing the signifi cant concerns regarding their overuse and inappropriate application. Such misuse can result in increased dependency among neurological patients, ultimately hindering their rehabilitation progress.

There is growing evidence that assistive digital technology can enhance quality of life (QOL) for individuals with various forms of cognitive impairment, including dementia. Assess whether the use of a visual mapping software program to manage activities of daily living would have a positive impact on QOL scores and on cognitive scores in a group of dementia residents in an adult living community. We compared quality-of-life scores and cognitive function scores before and after using the assistive technology for three months. 1. QOL scores significantly improved in the memory impaired residents, as measured by a self-report questionnaire. 2. Caregivers also reported significantly improved QOL scores in the residents, and the caregivers reported more improved scores than the residents did. 3. Net Promoter Scores for residents and caregivers showed that using visual maps was highly satisfying; they would continue using this technology. 4. Memory-impaired residents showed significantly improved scores in cognitive areas reflecting improved ability to focus and pay attention. In addition to the positive findings in QOL and cognition, assistive technologies applied to dementia care are easy to access, easy to use, have little risk of side effects, and are relatively low in cost.

The number of research papers in the area of Brain Computer Interface (BCI) assistive technologies is increasing rapidly. In addition, there is a possibility that some prosthetic models based on BCI will soon be available on the market. However, the acceptance and the degree of information that lay people have about this kind of technology is still unclear. The objective of this study was to investigate the diffusion of this top-of-the-edge technology and its acceptance by society. We developed a structured questionnaire and we applied it, in a single day, to passersby in downtown of an urban city with over one million people in Southeastern Brazil. The results showed that almost a third of 336 interviewees (mean age of 37 year-old, range from 15 to 89 years) had never heard about such technology. Most (89 %) of the other two thirds that had already heard of it affirmed that BCI based technologies would help in cases of incapacitating physical disabilities. No association was found when confronting the pattern of the answers with demographical data (p>O.05). We conclude that there is a positive attitude towards assistive technology by the society. The majority of the interviewees claimed to know about it, but it seems that the knowledge is superficial and based on broad science diffusion media. The literature on BCI acceptance is still very limited. Approximation between the scientific community and the end users is advised to bring adequate information and to decrease the degree of fantasy naturally related to this area, avoiding future false expectations.

Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

In recent years, with the rapid development of Brain-Computer Interface (BCI) and Artificial Intelligence (AI) technologies, a trend of increasing integration between the two has emerged. In some practical applications, BCI has been combined with AI to enhance the overall performance of systems, including usability, user experience, and satisfaction, especially in terms of intelligent capabilities. However, this technological integration also introduces new or exacerbates existing ethical risks, such as neural privacy breaches, cross-domain misuse, and unclear system responsibility attribution. This paper discusses the novel or more severe ethical challenges arising from the fusion of BCI and AI technologies, as well as measures and strategies to address these ethical issues, calling for the establishment of more comprehensive ethical guidelines and governance frameworks. It is hoped that this paper will contribute to a deeper understanding and reflection on the ethical risks and corresponding regulations related to the integration of BCI and AI technologies.

Purpose Housing adaptation enables the elderly and disabled people to live healthy, productive, independent, and dignified lives by making it easier to carry out everyday tasks in a safer environment. Assistive technology enables them to perform tasks that would otherwise be difficult or impossible to achieve independence. Therefore, the new guidance for local authorities in England on Disabled Facilities Grants (DFG) recommends assistive and smart technology as part of a DFG award package to maximise the benefits of home adaptations. Through a baseline study, this paper examines the disabled and elderly peoples’ level of digital engagement, the challenges and the impact of smart devices on their daily living and well-being. Design/methodology/approach A baseline study approach was used through the co-production of knowledge. The study relies on a questionnaire survey and the Most Significant Change (MSC) research technique through structured interviews. A percentage of the total approach is used in analysing the quantitative data by comparing the pre- and post-baseline results. Findings The major challenges the clients face are Wi-Fi connectivity, system compatibility issues, and skills in navigating different applications and operating systems. The pilot study demonstrates that assistive technology supports independent and healthy living and promotes a secure and safe environment. It also promotes social inclusion and improves the elderly and disabled people’s comfort and engagement. However, the results show that the overall impact is minimal; therefore, a longitudinal study is needed to understand the impacts over a long period. Practical implications The findings will help policymakers to understand the impacts of assistive technology on improving the elderly and disabled people’s quality of life. Originality/value A comparable baseline study was carried out to serve as a reference point for other similar future studies. The paper also highlights the challenges faced by the elderly and disabled people, their digital skill level and the impact of smart devices on their daily living and well-being.

Brain-Computer Interfaces (BCI), allowing to operate the enviroment without muscular activation, can be used as assistive technology as well as neurorehabilitation tool. A BCI exploits brain signal changes induced by event-related potentials (P300) or by voluntary control of the user (Slow Cortical Potentials or Motor Imagery) and this can be achieved using a classifier. A key feature for BCI systems is user-classifier mutual learning, which can be modulated by feedback. There is experimental evidence that feedback can have a positive as well as a negative effect and this is relevant in using BCI both as assistive technology and as neurorehabilitation tool. The aim of this study was to investigate a possible sequence effect on overall accuracy in administering a first training session without feedback and then a second one with feedback. Moreover, we evaluated also a gender effect. We considered a sample of sixteen subjects (ten males), using an EEG-based BCI and executing a Motor Imagery task. Our results, combined with data from literature, indicate the presence of a “cognitive carry-over” effect for continous feedback in motor imagery, with gender differences not statistically significant. Such a cognitive effect means that the presence or absence of feedback changes the cognitive status of the BCI user and thus the information embedded in the features the classifier uses. Therefore, there should be a sort of cognitive coherence in BCI-training and the assessment of user attitude toward feedback could improve BCI usefulness and effectiveness.

Patients suffering from life-changing disability due to Spinal Cord Injury (SCI) increasingly benefit from assistive robotics technology. The field of brain-computer interfaces (BCIs) has started to develop mature assistive applications for those patients. Nonetheless, noninvasive BCIs still lack accurate control of external devices along several degrees of freedom (DoFs). Unobtrusiveness, portability, and simplicity should not be sacrificed in favor of complex performance and user acceptance should be a key aim among future technological directions. In our study 10 subjects with SCI (one complete) and 10 healthy controls were recruited. In a single session they operated two anthropomorphic 8-DoF robotic arms via wireless commercial BCI, using kinesthetic motor imagery to perform 32 different upper extremity movements. Training skill and BCI control performance were analyzed with regard to demographics, neurological condition, independence, imagery capacity, psychometric evaluation, and user perception. Healthy controls, SCI subgroup with positive neurological outcome, and SCI subgroup with cervical injuries performed better in BCI control. User perception of the robot did not differ between SCI and healthy groups. SCI subgroup with negative outcome rated Anthropomorphism higher. Multi-DoF robotics control is possible by patients through commercial wireless BCI. Multiple sessions and tailored BCI algorithms are needed to improve performance.

Brain-computer interfaces (BCIs) have emerged as transformative tools for translating users’ neural signals into commands for external devices. The urgent need for innovative treatments to enhance upper limb motor function in stroke survivors is underscored by the limitations of traditional rehabilitation methods. The development of communication and control technology for individuals with severe neuromuscular diseases, particularly stroke patients, is centered on utilizing electroencephalographic (EEG) signals to accurately decode users’ intentions and operate external devices. Two healthy subjects and a stroke patient were enrolled to acquire EEG signals using the EMOTIV EPOC+ sensor. The experimental procedure involved recording five actions for both motor imagery and facial expression signals to control the 3D-printed upper limb exoskeleton. EEGLAB and BCILAB software were used for preprocessing and classification. The results showed successful EEG-based control of the exoskeleton, representing a significant advancement in assistive technology for individuals with motor impairments. The support vector machine (SVM) classifier achieved higher accuracy in both offline and online modes for both motor imaginary and facial expression tasks. The conclusion highlights the appropriateness of using EEGLAB for offline EEG data analysis and BCILAB for both offline and online analysis and classification. The integration of servo motors in the exoskeleton, allowing movements in five Degrees of Freedom (DOF), positions it as an effective rehabilitation solution for individuals with upper limb impairments.

Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are promising techniques for these applications due to their non-invasiveness, portability, low cost, and relatively high spatial resolution. However, real-time processing of fNIRS/DOT data remains a significant challenge as it requires establishing a baseline of the measurement, simultaneously performing real-time motion artifact (MA) correction across all channels, and (in the case of DOT) addressing the time-consuming process of image reconstruction. This study proposes a real-time processing system for fNIRS/DOT that integrates baseline calibration, denoising autoencoder (DAE) based MA correction model with a sliding window strategy, and a pre-calculated inverse Jacobian matrix to streamline the reconstructed 3D brain hemodynamics. The DAE model was trained on an extensive whole-head high-density DOT (HD-DOT) dataset and tested on separate motor imagery dataset augmented with artificial MA. The system demonstrated the capability to simultaneously process approximately 750 channels in real-time. Our results show that the DAE-based MA correction method outperformed traditional MA correction in terms of mean squared error and correlation to the known MA-free data while maintaining low latency, which is critical for effective BCI and NFB applications. The system’s high-channel, real-time processing capability provides channel-wise oxygenation information and functional 3D imaging, making it well-suited for fNIRS/DOT applications in BCI and NFB, particularly in movement-intensive scenarios such as motor rehabilitation and assistive technology for mobility support.

Argumentaire

Assistive Device With Conventional, Alternative, and Brain-Computer Interface Inputs to Enhance Interaction With the Environment for People With Amyotrophic Lateral Sclerosis: A Feasibility and Usability Study