搜索结果：Disability and rehabilitation

As large language models (LLMs) become increasingly integrated into hiring processes, concerns about fairness have gained prominence. When applying for jobs, companies often request/require demographic information, including gender, race, and disability or veteran status. This data is collected to support diversity and inclusion initiatives, but when provided to LLMs, especially disability-related information, it raises concerns about potential biases in candidate selection outcomes. Many studies have highlighted how disability can impact CV screening, yet little research has explored the specific effect of voluntarily disclosed information on LLM-driven candidate selection. This study seeks to bridge that gap. When candidates shared identical gender, race, qualifications, experience, and backgrounds, and sought jobs with minimal employment rate gaps between individuals with and without disabilities (e.g., Cashier, Software Developer), LLMs consistently favored candidates who disclosed that they had no disability. Even in cases where candidates chose not to disclose their disability status, the LLMs were less likely to select them compared to those who explicitly stated they did no

Purpose. This paper examines the extent to which job satisfaction, requests for accommodations, and the likelihood of a request being granted vary by disability status. We further analyze whether being granted workplace accommodations moderates the relationship between work satisfaction and disability. Methods. We use a novel survey of healthcare workers centered on disability status, perceptions of work experiences, and the provision of accommodations. The data are used in a descriptive analysis and multiple regressions to examine the moderating effect of accommodations on the relationship between disability and indicators related to job satisfaction. Results. Results show that people with disabilities have more negative perceptions of their work experiences than people without disabilities. Although people with disabilities are more likely to request accommodations than people without disabilities, they are equally likely to have their requests wholly or partly granted. Regression results indicate that the negative relationships between disability status and most measures of work experience are largely eliminated when accounting for the disposition of accommodation requests. The

Background: Many workers with disabilities face negative stereotypical attitudes, pay gaps, and a lack of respect in the workplace, contributing to substantially lower job satisfaction compared to people without disabilities. Work from home may help to increase job satisfaction for people with disabilities. Objective: This study analyzes how different measures of job satisfaction vary between people with and without disabilities, and the extent to which working from home moderates the relationship between disability and job satisfaction. Methods: We use multivariable regression analysis to examine if the ability to work from home moderates the relationship between disability and indicators related to job satisfaction. The dataset draws on a novel survey of healthcare professionals. Results: Results show that people with disabilities have relatively greater turnover intentions, lower sense of organizational commitment and support, weaker perceptions of openness and inclusion in the workplace, and worse relations with management and coworkers. Regressions indicate that working from home helps to improve most perceptions of work experiences but does so more for people without disabili

Purpose: This paper examines the prevalence of long COVID across different demographic groups in the U.S. and the extent to which workers with impairments associated with long COVID have engaged in pandemic-related remote work. Methods: We use the U.S. Household Pulse Survey to evaluate the proportion of all adults who self-reported to (1) have had long COVID, and (2) have activity limitations due to long COVID. We also use data from the U.S. Current Population Survey to estimate linear probability regressions for the likelihood of pandemic-related remote work among workers with and without disabilities. Results: Findings indicate that women, Hispanic people, sexual and gender minorities, individuals without four-year college degrees, and people with preexisting disabilities are more likely to have long COVID and to have activity limitations from long COVID. Remote work is a reasonable arrangement for people with such activity limitations and may be an unintentional accommodation for some people who have undisclosed disabilities. However, this study shows that people with disabilities were less likely than people without disabilities to perform pandemic-related remote work. Conclus

Stroke represents the third cause of death and disability worldwide, and is recognised as a significant global health problem. A major challenge for stroke survivors is persistent hand dysfunction, which severely affects the ability to perform daily activities and the overall quality of life. In order to regain their functional hand ability, stroke survivors need rehabilitation therapy. However, traditional rehabilitation requires continuous medical support, creating dependency on an overburdened healthcare system. In this paper, we explore the use of egocentric recordings from commercially available smart glasses, specifically RayBan Stories, for remote hand rehabilitation. Our approach includes offline experiments to evaluate the potential of smart glasses for automatic exercise recognition, exercise form evaluation and repetition counting. We present REST-HANDS, the first dataset of egocentric hand exercise videos. Using state-of-the-art methods, we establish benchmarks with high accuracy rates for exercise recognition (98.55%), form evaluation (86.98%), and repetition counting (mean absolute error of 1.33). Our study demonstrates the feasibility of using egocentric video from s

Stroke is a leading cause of long-term disability and the second most common cause of death worldwide. Although acute treatments have advanced, recovery remains challenging and limited. Brain-computer interfaces (BCIs) have emerged as a promising tool for post-stroke rehabilitation by promoting neuroplasticity. However, clinical outcomes remain variable, and optimal protocols have yet to be established. This study explores strategies to optimize BCI-based rehabilitation by comparing motor imagery of affected hand movement versus rest, instead of the conventional left-versus-right motor imagery. This alternative aims to simplify the task and address the weak contralateral activation commonly observed in stroke patients. Two datasets, one from healthy individuals and one from stroke patients, were used to evaluate the proposed approach. The results showed improved performance using both FBCSP and EEGNet. Additionally, we investigated the impact of session duration and found that shorter training sessions produced better BCI performance than longer sessions.

General-purpose safety benchmarks for large language models do not adequately evaluate disability-related harms. We introduce DisaBench: a taxonomy of twelve disability harm categories co-created with people with disabilities and red teaming experts, a taxonomy-driven evaluation methodology that pairs benign and adversarial prompts across seven life domains, and a dataset of 175 prompts with human-annotated labels on 525 prompt-response pairs. Annotation by four evaluators with lived disability experience reveals three findings: harm rates vary sharply by disability type and will compound in non-text modalities, terminology-driven harm is culturally and temporally bound rather than universally assessable, and standard safety evaluation catches overt failures while missing the subtle harms that only domain expertise can recognize. Disability harm is simultaneously personal, intersectional, and community-defined: it cannot be isolated from the full context of who a person is, and general-purpose benchmarks systematically miss it. We will release the dataset, taxonomy, and methodology via Hugging Face and an open-source red teaming framework for direct integration into existing safety

Vision-language models (VLMs) are increasingly deployed in socially sensitive applications, yet their behavior with respect to disability remains underexplored. We study disability aware descriptions for person centric images, where models often transition from evidence grounded factual description to interpretation shift including introduction of unsupported inferences beyond observable visual evidence. To systematically analyze this phenomenon, we introduce a benchmark based on paired Neutral Prompts (NP) and Disability-Contextualised Prompts (DP) and evaluate 15 state-of-the-art open- and closed-source VLMs under a zero-shot setting across 9 disability categories. Our evaluation framework treats interpretive fidelity as core objective and combines standard text-based metrics capturing affective degradation through shifts in sentiment, social regard and response length with an LLM-as-judge protocol, validated by annotators with lived experience of disability. We find that introducing disability context consistently degrades interpretive fidelity, inducing interpretation shifts characterised by speculative inference, narrative elaboration, affective degradation and deficit oriente

Physical rehabilitation exercises suggested by healthcare professionals can help recovery from various musculoskeletal disorders and prevent re-injury. However, patients' engagement tends to decrease over time without direct supervision, which is why there is a need for an automated monitoring system. In recent years, there has been great progress in quality assessment of physical rehabilitation exercises. Most of them only provide a binary classification if the performance is correct or incorrect, and a few provide a continuous score. This information is not sufficient for patients to improve their performance. In this work, we propose an algorithm for error classification of rehabilitation exercises, thus making the first step toward more detailed feedback to patients. We focus on skeleton-based exercise assessment, which utilizes human pose estimation to evaluate motion. Inspired by recent algorithms for quality assessment during rehabilitation exercises, we propose a Transformer-based model for the described classification. Our model is inspired by the HyperFormer method for human action recognition, and adapted to our problem and dataset. The evaluation is done on the KERAAL d

Large Language Models (LLMs) are increasingly deployed across diverse domains but often exhibit disparities in how they handle real-life queries. To systematically investigate these effects within various disability contexts, we introduce \textbf{AccessEval (Accessibility Evaluation)}, a benchmark evaluating 21 closed- and open-source LLMs across 6 real-world domains and 9 disability types using paired Neutral and Disability-Aware Queries. We evaluated model outputs with metrics for sentiment, social perception, and factual accuracy. Our analysis reveals that responses to disability-aware queries tend to have a more negative tone, increased stereotyping, and higher factual error compared to neutral queries. These effects show notable variation by domain and disability type, with disabilities affecting hearing, speech, and mobility disproportionately impacted. These disparities reflect persistent forms of ableism embedded in model behavior. By examining model performance in real-world decision-making contexts, we better illuminate how such biases can translate into tangible harms for disabled users. This framing helps bridges the gap between technical evaluation and user impact, rei

One of the most frequent and severe aftermaths of a stroke is the loss of upper limb functionality. Therapy started in the sub-acute phase proved more effective, mainly when the patient participates actively. Recently, a novel set of rehabilitation and support robotic devices, known as supernumerary robotic limbs, have been introduced. This work investigates how a surface electromyography (sEMG) based control strategy would improve their usability in rehabilitation, limited so far by input interfaces requiring to subjects some level of residual mobility. After briefly introducing the phenomena hindering post-stroke sEMG and its use to control robotic hands, we describe a framework to acquire and interpret muscle signals of the forearm extensors. We applied it to drive a supernumerary robotic limb, the SoftHand-X, to provide Task-Specific Training (TST) in patients with sub-acute stroke. We propose and describe two algorithms to control the opening and closing of the robotic hand, with different levels of user agency and therapist control. We experimentally tested the feasibility of the proposed approach on four patients, followed by a therapist, to check their ability to operate th

Text-to-image generative models have made remarkable progress in producing high-quality visual content from textual descriptions, yet concerns remain about how they represent social groups. While characteristics like gender and race have received increasing attention, disability representations remain underexplored. This study investigates how people with disabilities are represented in AI-generated images by analyzing outputs from Stable Diffusion XL and DALL-E 3 using a structured prompt design. We analyze disability representations by comparing image similarities between generic disability prompts and prompts referring to specific disability categories. Moreover, we evaluate how mitigation strategies influence disability portrayals, with a focus on assessing affective framing through sentiment polarity analysis, combining both automatic and human evaluation. Our findings reveal persistent representational imbalances and highlight the need for continuous evaluation and refinement of generative models to foster more diverse and inclusive portrayals of disability.

Robotic rehabilitation can deliver high-dose gait therapy and improve motor function after a stroke. However, for many devices, high costs and lengthy setup times limit clinical adoption. Thus, we designed, built, and evaluated the Passive Mechanical Add-on for Treadmill Exercise (P-MATE), a low-cost passive end-effector add-on for treadmills that couples the movement of the paretic and non-paretic legs via a reciprocating system of elastic cables and pulleys. Two human-device mechanical interfaces were designed to attach the elastic cables to the user. The P-MATE and two interface prototypes were tested with a physical therapist and eight unimpaired participants. Biomechanical data, including kinematics and interaction forces, were collected alongside standardized questionnaires to assess usability and user experience. Both interfaces were quick and easy to attach, though user experience differed, highlighting the need for personalization. We also identified areas for future improvement, including pretension adjustments, tendon derailing prevention, and understanding long-term impacts on user gait. Our preliminary findings underline the potential of the P-MATE to provide effective

Configurable robots are made up of robotic modules that can be assembled or can configure themselves into multiple robot configurations. In this research plan, a method for upper-body rehabilitation will be discussed in the form of a modular robot with different morphologies. The advantage and superiority of designing an example of a robotic module for upper body rehabilitation is the ability to reset the modular robot system. In this research, a number of modules will be designed and implemented according to the needs of one-hand rehabilitation with different degrees of freedom. The design modules' performance and efficiency will be evaluated by simulating, making samples, and testing them. This article's research includes presenting a modular upper body rehabilitation robot in the wrist, elbow, and shoulder areas, as well as providing a suitable kinematic and dynamic model of the upper body rehabilitation robot to determine human-robot interaction forces and movement. The research also involves analyzing the mathematical model of the upper body rehabilitation robot to identify advanced control strategies that rely on force control and torque control. After reviewing the articles

Background: Telework has benefits for many people with disabilities. The pandemic may create new employment opportunities for people with disabilities by increasing employer acceptance of telework, but this crucially depends on the occupational structure. Objective: We compare people with and without disabilities in the expansion of telework as the pandemic began, and the evolution of telework during the pandemic. Methods: We use U.S. data from the American Community Survey from 2008 to 2020 and the Current Population Survey over the May 2020 to April 2022 period. Prevalence and trends are analyzed using linear probability and multinomial logit regressions. Results: While workers with disabilities were more likely than those without disabilities to telework before the pandemic, they were less likely to telework during the pandemic. The occupational distribution accounts for most of this difference. Tight labor markets, as measured by state unemployment rates, particularly favor people with disabilities obtaining telework jobs. While people with cognitive/mental health and mobility impairments were the most likely to telework during the pandemic, tight labor markets especially favor

Common disabilities like stroke and spinal cord injuries may cause loss of motor function in hands. They can be treated with robot assisted rehabilitation techniques, like continuously opening and closing the hand with help of a robot, in a cheaper, and less time consuming manner than traditional methods. Hand exoskeletons are developed to assist rehabilitation, but their bulky nature brings with it certain challenges. As soft robots use elastomeric and fabric elements rather than heavy links, and operate with pneumatic, hydraulic or tendon based rather than traditional rotary or linear motors, soft hand exoskeletons are deemed a better option in relation to rehabilitation.

Vision-based assessment can provide convenient and cost-effective evaluation in Traditional Chinese Medicine (TCM) rehabilitation training, where action quality assessment (AQA) from computer vision offers a promising solution. Existing automatic AQA frameworks for physical therapy typically rely on skeletal data captured from a single viewpoint, which is inefficient for TCM techniques such as acupuncture or Tuina that involve dense hand self-occlusion and complex hand-object interactions. To address these challenges, we propose CME-AQA, a cross-view, multimodal vision-based assessment framework that integrates visual-pose fusion to enhance understanding of environmental context and leverages both first-person and third-person videos during training to improve inference robustness. We collected two dual-view datasets, TCM-AQA61-A (Acupuncture) and TCM-AQA61-T (Tuina), each containing synchronized first-person and third-person recordings of 61 subjects with expert annotations. Experimental results show that our approach achieves superior or comparable mean performance against competitive baselines, achieving over 10% relative improvement in weighted F1 over the best competing method

Effective stroke recovery requires continuous rehabilitation integrated with daily living. To support this need, we propose a home-based rehabilitation exercise and feedback system. The system consists of (1) hardware setup with RGB-D camera and wearable sensors to capture stroke movements, (2) a mobile application for exercise guidance, and (3) an AI server for assessment and feedback. When a stroke user exercises following the application guidance, the system records skeleton sequences, which are then assessed by the deep learning model, RAST-G@ (Rehabilitation Assessment Spatio-Temporal Graph ATtention). The model employs a spatio-temporal graph convolutional network to extract skeletal features and integrates transformer-based temporal attention to figure out action quality. For system implementation, we constructed the NRC dataset, include 10 upper-limb activities of daily living (ADL) and 5 range-of-motion (ROM) collected from stroke and non-disabled participants, with Score annotations provided by licensed physiotherapists. Results on the KIMORE and NRC datasets show that RAST-G@ improves over baseline in terms of MAD, RMSE, and MAPE. Furthermore, the system provides user fe

Advanced by rich perception and precise execution, robots possess immense potential to provide professional and customized rehabilitation exercises for patients with mobility impairments caused by strokes. Autonomous robotic rehabilitation significantly reduces human workloads in the long and tedious rehabilitation process. However, training a rehabilitation robot is challenging due to the data scarcity issue. This challenge arises from privacy concerns (e.g., the risk of leaking private disease and identity information of patients) during clinical data access and usage. Data from various patients and hospitals cannot be shared for adequate robot training, further compromising rehabilitation safety and limiting implementation scopes. To address this challenge, this work developed a novel federated joint learning (FJL) method to jointly train robots across hospitals. FJL also adopted a long short-term memory network (LSTM)-Transformer learning mechanism to effectively explore the complex tempo-spatial relations among patient mobility conditions and robotic rehabilitation motions. To validate FJL's effectiveness in training a robot network, a clinic-simulation combined experiment was