Amin Mohamed, Hamza Abdelmoreed, Mohamed Ehab
et al.
Low back pain (LBP) is a pervasive global health challenge, affecting approximately 80% of adults and frequently progressing into chronic or recurrent episodes. While exercise therapy is a primary clinical intervention, traditional at-home programs suffer from low adherence rates and the absence of professional supervision. This study introduces TOSHFA, an accessible mobile VR-based rehabilitation system that bridges this gap by combining computer vision with affordable hardware. The system utilizes a laptop webcam to perform real-time pose estimation via the MediaPipe framework, tracking 33 skeletal landmarks to provide immediate biofeedback. This data is streamed via low-latency UDP protocols to a smartphone mounted in a cardboard-style VR headset, where patients interact with a gamified 3D environment. A pilot study with 20 participants evaluated the system's performance and user engagement. Quantitative results yielded a mean System Usability Scale (SUS) score of 47.4, indicating marginal usability and a need for interface optimization. However, Game Experience Questionnaire (GEQ) data revealed high scores in positive affect and enjoyment, suggesting that the gamification elements--such as coin rewards and streak tracking--successfully maintained user motivation despite technical friction. These findings validate the feasibility of a smartphone-based tele-rehabilitation model and establish a technical foundation for future clinical trials involving multi-exercise protocols.
Aartjan ter Haar, Sander R. Hilberink, Alice Schippers
Research has largely focussed on public perceptions of people with disabilities, particularly in the media. However, there is a lack of studies exploring how individuals with disabilities themselves experience public disability representation. This scoping review examines the lived experiences of individuals with disabilities regarding such representations. Eligible studies were peer-reviewed, published between January 1948 and July 2024, and presented qualitative findings on experienced public disability representation. A systematic search was conducted across PubMed, Scopus, ProQuest, OVID, EBSCO, and Web of Science using variations of the terms ’representation’ and ‘disability’, yielding 32 relevant studies. Data were analysed using both deductive and inductive methods. Deductive thematic analysis aligned the findings with two established media disability representation typologies: traditional and contemporary. Inductive thematic analysis identified three key themes—ignorance, incapability, and otherness—that illustrate how individuals with disabilities experience public representations. Overall, the review found that despite the absence of comparative studies, individuals with diverse disabilities report similar, predominantly negative experiences of public disability representation. Future studies should adopt an intersectional, cross-disability framework to better capture the experiences of people with disabilities, including those with intellectual disabilities.
Vocational rehabilitation. Employment of people with disabilities
Background Persons with disabilities in South Africa experience challenges in accessing training and employment opportunities. Objective This study aimed to determine the role of specialised recruitment agencies by exploring activities they engage in and services provided to employers and job seekers with a disability. Method Twenty-five specialised recruitment agencies were interviewed through semi-structured individual telephonic interviews. A qualitative research design was employed. Data were transcribed verbatim and analysed thematically. Findings It was found that the SRAs assume four roles: recruitment, placement, trainer, and consultation. Within these roles, various services were provided, which were related to recruitment, placement, and support in equity compliance. These roles assumed by SRAs largely depended on the size of the organisation Conclusion The four roles collectively foster a supportive environment for employees with disabilities. Training dispels misconceptions, and pre- and post-employment support ensures access and accommodations. It is evident from agencies that assumed all four roles that successful placement of persons with disabilities was dependent on the provision of support within all four roles.
Long-term unemployment (LTU) is a challenge for both jobseekers and public employment services. Statistical profiling tools are increasingly used to predict LTU risk. Some profiling tools are opaque, black-box machine learning models, which raise issues of transparency and fairness. This paper investigates whether interpretable models could serve as an alternative, using administrative data from Switzerland. Traditional statistical, interpretable, and black-box models are compared in terms of predictive performance, interpretability, and fairness. It is shown that explainable boosting machines, a recent interpretable model, perform nearly as well as the best black-box models. It is also shown how model sparsity, feature smoothing, and fairness mitigation can enhance transparency and fairness with only minor losses in performance. These findings suggest that interpretable profiling provides an accountable and trustworthy alternative to black-box models without compromising performance.
Yingqiang Gao, Kaede Johnson, David Froehlich
et al.
Automatic text simplification (ATS) aims to enhance language accessibility for various target groups, particularly persons with intellectual disabilities. Recent advancements in generative AI, especially large language models (LLMs), have substantially improved the quality of machine-generated text simplifications, thereby mitigating information barriers for the target group. However, existing LLM-based ATS systems do not incorporate preference feedback on text simplifications during training, resulting in a lack of personalization tailored to the specific needs of target group representatives. In this work, we extend the standard supervised fine-tuning (SFT) approach for adapting LLM-based ATS models by leveraging a computationally efficient LLM alignment technique -- direct preference optimization (DPO). Specifically, we post-train LLM-based ATS models using human feedback collected from persons with intellectual disabilities, reflecting their preferences on paired text simplifications generated by mainstream LLMs. Furthermore, we propose a pipeline for developing personalized LLM-based ATS systems, encompassing data collection, model selection, SFT and DPO post-training, and evaluation. Our findings underscore the necessity of active participation of target group persons in designing personalized AI accessibility solutions aligned with human expectations. This work represents a step towards personalizing inclusive AI systems at the target-group level, incorporating insights not only from text simplification experts but also from target group persons themselves.
The striking alignment between large language models (LLMs) and human brain activity positions them as powerful models of healthy cognition. This parallel raises a fundamental question: if LLMs can model the intact brain, can we lesion them to simulate the linguistic deficits of the injured brain? In this work, we introduce a methodology to model aphasia - a complex language disorder caused by neural injury - by selectively disabling components in a modular Mixture-of-Experts (MoE) language model. We simulate distinct aphasia subtypes, validate their linguistic outputs against real patient speech, and then investigate functional recovery by retraining the model's remaining healthy experts. Our results demonstrate that lesioning functionally-specialized experts for syntax or semantics induces distinct impairments that closely resemble Broca's and Wernicke's aphasia, respectively. Crucially, we show that freezing the damaged experts and retraining the intact ones on conversational data restores significant linguistic function, demonstrating a computational analogue for rehabilitation. These findings establish modular LLMs as a powerful and clinically-relevant potential framework for modeling the mechanisms of language disorders and for computationally exploring novel pathways for therapy.
Virtual Reality (VR) is often described as the "ultimate empathy machine," framing disability as an experience to be simulated through such technologies, which can reduce disability to a spectacle of pity or inspiration. In response, we present Waiting for Hands (WfH), an interactive eXtended Reality (XR) installation that critiques this logic by: (1) repurposing interaction norms in XR through the creation of Alternative Controllers, and (2) staging an absurd XR performance using the built controllers to disrupt sentimentalized disability narratives. The performance involves eight people: two XR participants on stage and six audience members watching a projected documentary about Hema Kumari, an Indian singer living with Rheumatoid Arthritis. The XR users partially obscure the film, drawing attention through strange mouth and hand movements performed in XR. This creates a layered experience that disrupts direct engagement with Hema's story and introduces uncertainty. While XR is often seen as a fully immersive, sensory-dominant medium, this piece subverts that framing by using XR to produce absurdity and alienation. By challenging empathy-driven and pitiable narratives of disability, we ask what ethical stance an XR performance can take to attune participants to non-normative embodiment while resisting spectacle.
Abstract Background Evaluation of vocational rehabilitation program requires understanding the perspectives of clients and vocational rehabilitation providers (VRP). These providers can offer critical insights into service operations and identify barriers to care. This study aimed to understand the perspectives of VRP in a local vocational rehabilitation program towards feedback they received from clients, with a goal to understand how the program can be improved further. Methods This was a qualitative study. We conducted focus group discussions with VRP. We analyzed the qualitative data using inductive thematic analysis. Findings We recruited 14 VRP (median age = 33 years). Each VRP participated in two focus group sessions. We identified seven major themes from the sessions: (i) commitment to client-centered care, (ii) benefits of multi-disciplinary team, (iii) inclusion of long-term care planning, (iv) need for inter-agency collaboration, (v) emphasis on education, training and awareness, (vi) opportunities for expansion, and (vi) constraints. These represent the potential areas to improve the vocational rehabilitation program as well as challenges to the return-to-work process of people with disabilities. Conclusions The VRP identified program- and system-level areas for improvement. Program-level improvements include long-term care planning, inter-agency collaboration, and education and training for all stakeholders involved in the return-to-work process. Beyond the program, the service providers acknowledged that the changing job market globally, technological advancements, and adoption of technology could influence the return-to-work process of people with disabilities (PWD). At the system-level, the service providers highlighted that social attitudes or policies could also impact on the employment of PWD. These factors may require whole-of-system efforts to create a more inclusive society that supports PWD to gain employment. Key messages • Vocational rehabilitation providers should be aware of the factors that contribute to PWD returning to work. • Policies may also need to be streamlined to encourage the return-to-work process of PWD.
Abstract Objectives To assess the effectiveness, cost-effectiveness and return on investment of individual placement and support (IPS) implemented through a reimbursement strategy on a nationwide scale compared with traditional vocational rehabilitation (TVR) regarding sustainable participation in competitive employment in individuals with severe mental illness receiving sickness or disability benefits. Methods An observational study was conducted using administrative data regarding all Dutch individuals receiving sickness or disability benefits in the period 2012–2019. Exact matching and difference-in-difference fixed-effect estimations were performed to handle the non-randomised nature of the data. The matched sample consisted of 863 IPS and 16 466 TVR participants. The primary effect measure was the proportion of individuals having worked for at least 48 hours per month in competitive employment (ie, for 12 hours or more per week); the proportion of individuals having worked in competitive employment for at least 1 hour per month was also evaluated. Cost-effectiveness and return on investment were assessed from the societal perspective (intervention, sickness/disability benefit and healthcare costs) and payer perspective (sickness/disability benefit costs). Results IPS led to a statistically significant higher probability of being competitively employed for at least 12 hours per week of 3.7% points (95% CI 0.8% to 6.7%) to 7.5% points (95% CI 3.8% to 11.3%) and of being competitively employed for at least 1 hour per month of 4.7% points (95% CI 1.6% to 7.7%) to 8.9% points (95% CI 5.2 to 12.6%) from 6 to 36 months after starting the intervention. From the societal and payer perspective, IPS was—on average—less costly and more effective than TVR and return-on-investment estimates showed that IPS was—on average—cost saving (eg, societal perspective: ∆C: −364 (95% CI −3977 to 3249); ∆E: 0.104 (95% CI 0.046 to 0.164); benefit–cost ratio: 2.1 (95% CI −14.8 to 19.1)), but the uncertainty surrounding these estimates was large. Conclusions IPS implemented through a reimbursement strategy on a nationwide scale is more effective and potentially cost-effective than TVR in people with severe mental illness receiving sickness or disability benefits. Based on these results, the implementation of IPS by a wide scale reimbursement strategy could be promoted to enhance sustainable participation in competitive employment in these individuals. Future economic evaluations should strive for a more robust sample size and a long follow-up period.
Maram Fahaad Almufareh, Samabia Tehsin, Mamoona Humayun
et al.
Autism spectrum disorder (ASD) is a mental condition that affects people’s learning, communication, and expression in their daily lives. ASD usually makes it difficult to socialize and communicate with others, and also sometimes shows repetition of certain behaviors. ASD can be a cause of intellectual disability. ASD is a big challenge in neural development, specially in children. It is very important that it is identified at an early stage for timely guidance and intervention. This research identifies the application of deep learning and vision transformer (ViT) models for classification of facial images of autistic and non-autistic children. ViT models are powerful deep learning models used for image classification tasks. This model applies transformer architectures to analyze input image patches and connect the information to achieve global-level information. By employing these techniques, this study aims to contribute toward early ASD detection. ViT models are showing good results in identifying facial features associated with ASD, leading toward early diagnostics. Results show the ViT model’s capability in distinguishing the faces of autistic and non-autistic children.
Vocational rehabilitation. Employment of people with disabilities
Hafsa Wajih, Umul Baneen, Syeda Laiba Fatima
et al.
OBJECTIVES:
The objectives of the study were to determine balance and flexibility in young adults between the age group of 18 to 25 years.
METHODOLOGY:
It was a comparative cross-sectional study design. Ethical Review Committee approved the study after reviewing. The data was collected from a total of n = 370 healthy young adults after taking informed consent from the participants. Participants were divided into 2 groups through WHO activeness criteria. Y – Balance test was used to measure balance while sit and reach test and static flexibility tests were used to measure flexibility.
RESULTS:
The data was analyzed on SPSS version 21.0 and significance value was selected to be α = 0.05. Participants taken were from the age category of 18 to 25 years. Normality test was applied which showed majority of the data in non-normally distributed (p-value of Leg Length Composite Score, Right Leg Composite Score and Static Flexibility Score is less than 0.05). So, Mann Whitney U test was applied and results were computed. The results showed insignificant difference in Y – balance scores (Left Leg Composite Score p = 0.464, Right Leg Composite Score p = 0.780) and sit and reach scores (p = 0.093) of inactive individuals as compared to active individuals. Moreover, there was statistically insignificant difference in static flexibility (p = 0.879) of both groups with active participants against inactive participants.
CONCLUSION:
It was concluded that physical activity has no significant effect on balance and flexibility in young adults. However, taking participants of equal weight and height in active against inactive group may improve the results or by using quantitative tools to measure balance and flexibility and by using another method to screen out participants to be included in active and inactive groups the results could be improved further.
KEYWORDS:
Balance, Flexibility, Physical Activity, Young Adults
Vocational rehabilitation. Employment of people with disabilities, Therapeutics. Psychotherapy
Hassan Sarwar Hassan Sarwar, Anna Zaheer, Sahar Fatima
Background: A new air-borne pandemic COVID-19 had resulted in a large number of morbidity and deaths. Post-traumatic stress disorder used to begin three months after its origin and probably lasts for 6 months.
Objective: To determine the association of factors with post-traumatic stress disorder in COVID-19 survivors after getting normal confirmed by COVID19 negative test done through RTPCR diagnostic testing.
Methodology: According to Epitool a total of 165 COVID-19 survivors participated in this cross-sectional study. The non-probability convenient sampling approach was utilized. Demographic data was recorded using a self-made proforma while evaluation of post-traumatic stress disorder was done through IES-R scale.
Results: A total of 165 Covid-19 survivors took part in study, comprised upon 81 (49.1%) of men and 84 (50.9%) of females. The majority of COVID-19 survivors socioeconomically were from middle class in number of 148 (89.7%). About 66 (40.0%) encountered high impact post-traumatic stress disorder while surviving corona virus. COVID-19 survivors had trouble sleeping, being woken without cause, or over-slept, about 114 (69.1%) favored it whereas 51 (30.9%) opposed it. While 85 (51.5%) of COVID-19 survivors felt chest pain, tightness in chest or shortness of breath like symptoms after battling disease whereas 80 (48.5%) survivors had no impact.
Conclusion: The study determined that majority of COVID-19 survivors suffered from post-traumatic stress disorder had stronger association with the factors like nervousness, anxiousness and panic like stuff after surviving pandemic along with trouble in sleeping, staying asleep, awakened without reason or had overslept. While in comparison moderate association was configured between respiratory like symptoms including chest discomfort, heart beating and post-traumatic stress disorder in survivors who had battled out COVID-19.
Keywords: COVID-19, Post-Traumatic Stress Disorder, Sleep discomfort, Reverse Transcriptase Polymerase Chain Reaction, SARS-CoV-2.
Vocational rehabilitation. Employment of people with disabilities, Therapeutics. Psychotherapy
Background: Technical Vocational Education and Training (TVET) in South Africa is often viewed as the ‘Cinderella’ of higher education, with many matriculating students choosing mainstream universities instead. This preference stems from stigma and misconceptions that label TVET students – often from poorer working-class backgrounds – as less intelligent than their university peers. The lived experiences of students with physical and learning disabilities in these institutions are particularly underexplored.
Objectives: This study aimed to illuminate the experiences of students with disabilities at five TVET colleges in Gauteng, South Africa.
Method: Using a phenomenological approach, the study conducted story exercises and individual interviews with a convenience sample of 40 students with disabilities.
Results: The findings reveal that TVET education predominantly serves black students from marginalised backgrounds, with students with disabilities facing significant barriers in accessing both the curriculum and the physical environments of the colleges. Despite numerous challenges, a resilient narrative emerged among these students, rooted in African traditional values.
Conclusion: This article contributes to knowledge on disability inclusion in higher education by showcasing the challenges and resilience of students with disabilities in South Africa’s TVET system.
Contribution: The study employed innovative methodologies, such as picture stories, to co-create knowledge with students living with disabilities.
Vocational rehabilitation. Employment of people with disabilities, Communities. Classes. Races
Truck transportation remains the dominant mode of US freight transportation because of its advantages, such as the flexibility of accessing pickup and drop-off points and faster delivery. Because of the massive freight volume transported by trucks, understanding the effects of population and employment characteristics on truck flows is critical for better transportation planning and investment decisions. The US Federal Highway Administration published a truck travel origin-destination data set as part of the Next Generation National Household Travel Survey program. This data set contains the total number of truck trips in 2020 within and between 583 predefined zones encompassing metropolitan and nonmetropolitan statistical areas within each state and Washington, DC. In this study, origin-destination-level truck trip flow data was augmented to include zone-level population and employment characteristics from the US Census Bureau. Census population and County Business Patterns data were included. The final data set was used to train a machine learning algorithm-based model, Extreme Gradient Boosting (XGBoost), where the target variable is the number of total truck trips. Shapley Additive ExPlanation (SHAP) was adopted to explain the model results. Results showed that the distance between the zones was the most important variable and had a nonlinear relationship with truck flows.
This study seeks to identify the potential role of AI-driven assistive technologies in enhancing access to libraries for persons with varying degrees of challenges. Traditional libraries pose a problem to many users with vision and mobility, among other conditions related to physical and infirmities. This mixed-methods research approach will examine ways in which AI-powered assistive tools and applications associated with text-to-speech, navigation systems, and personalized assistants are revolutionizing library services through a literature review, survey methods, interviews, and case studies. Our findings suggest that these technologies greatly increase the autonomy and participation of people with physical disabilities, providing personalized support and access to a wide range of resources. From this, some key findings have been deduced from the research, showing a strong impact on user experience and efficiency in services, while at the same time bringing out important considerations related to privacy and ethical implementation. This study highlights the central role of AI in making library settings more inclusive, thereby allowing equal access to knowledge and participation in the community. Such insight thus serves professionals working in libraries, policymakers, and technology developers for innovations to occur uninterruptedly, with future research directions proposed that would refine such technologies, especially toward the special needs of diverse populations. By adopting AI, libraries could uphold their mission of providing equal access to knowledge through full and equal participation of all persons, regardless of any type of physical ability, in the learning and community activities carried out by the library. This study paves the way for future innovations in creating more accessible and inclusive library spaces.
Niklas Kueper, Su Kyoung Kim, Elsa Andrea Kirchner
Background: For an individualized support of patients during rehabilitation, learning of individual machine learning models from the human electroencephalogram (EEG) is required. Our approach allows labeled training data to be recorded without the need for a specific training session. For this, the planned exoskeleton-assisted rehabilitation enables bilateral mirror therapy, in which movement intentions can be inferred from the activity of the unaffected arm. During this therapy, labeled EEG data can be collected to enable movement predictions of only the affected arm of a patient. Methods: A study was conducted with 8 healthy subjects and the performance of the classifier transfer approach was evaluated. Each subject performed 3 runs of 40 self-intended unilateral and bilateral reaching movements toward a target while EEG data was recorded from 64 channels. A support vector machine (SVM) classifier was trained under both movement conditions to make predictions for the same type of movement. Furthermore, the classifier was evaluated to predict unilateral movements by only beeing trained on the data of the bilateral movement condition. Results: The results show that the performance of the classifier trained on selected EEG channels evoked by bilateral movement intentions is not significantly reduced compared to a classifier trained directly on EEG data including unilateral movement intentions. Moreover, the results show that our approach also works with only 8 or even 4 channels. Conclusion: It was shown that the proposed classifier transfer approach enables motion prediction without explicit collection of training data. Since the approach can be applied even with a small number of EEG channels, this speaks for the feasibility of the approach in real therapy sessions with patients and motivates further investigations with stroke patients.
Introduction. Military personnel who became disabled as a result of injury, illness or injury found themselves in a situation of need to restore their ability to work in working conditions available to them due to their health status, and to be included in work activities, taking into account their specific psychophysiological features. Goal. The article is aimed at identifying common and stable characteristics of persons with disabilities acquired during combat operations and special operations in 2022-2024. The article is also aimed at studying the needs of these people for vocational education and professional retraining, taking into account the identified characteristics. Materials and methods. The study is based on the analysis of a questionnaire survey of 23 people who participated in a special military operation and received a disability. The questionnaire survey was conducted in person on a voluntary basis. The research results were processed using statistical methods. Results and discussion. As the analysis showed, a high level of motivation to work prevails among disabled combatants. It has been established that every third combat disabled person is not ready to change profession, which significantly complicates his opportunities for further employment and successful work. Conclusion. Creating conditions for organizing vocational education and professional retraining of combat disabled people must be combined with measures of professional and psychological rehabilitation of this category of people, including helping a person understand the need to change his profession and forming value and ideological attitudes that contribute to his activity towards self-realization in the professional sphere.