Search Results (346)

Purpose: This study investigates the test–retest reliability of maximal voluntary contraction (MVC) and integrated object positioning during bimanual box lifting tasks in children with unilateral spastic cerebral palsy (USCP), using the Activities of Daily Living Test and Training Device (ADL-TTD). Materials and Methods: Utilizing an explorative cross-sectional design, the study recruited 47 children with USCP. The ADL-TTD, equipped with an Inertial Measurement Unit (IMU) for precise object positioning, measured MVC, and object position in 3D space in a cross-sectional measurement containing two measurements in a fixed time period. Results: The findings demonstrated good test–retest reliability for MVC, with an ICC_agreement of 0.95 for the mean MVC value. Additionally, good reliability was observed for object positioning in different directions measured with an IMU, with ICC_agreement ranging from 0.82 to 0.86 degrees. Regarding the standard error of measurement (SEM), the SEM_agreement for the mean MVC value was 5.94 kg, while the SEM_agreement for object positioning was 1.48, 5.39, and 3.43 degrees, respectively. Conclusions: These results indicate that the ADL-TTD demonstrates good test–retest reliability for both MVC and object positioning, making it a valuable tool for analyzing this population in cross-sectional research by providing reliable measures of task-oriented strength and object manipulation. However, the relatively high SEM_agreement, particularly in MVC, suggests that caution is needed when using this tool for repeated testing over time. This pioneering approach could significantly contribute to tailored assessment and training for children with USCP, highlighting the importance of integrating task-specific strength and positional accuracy into therapeutic interventions. Full article

Background: Standing has medical and psychosocial benefits for people with lower limb impairments; however, systemic, logistical, and economic barriers often limit opportunities to stand in daily life. This study explored how users perceive standing and standing-assistive technologies. Methods: This study used a mixed-methods approach: in-person interviews (n = 18) and a nationwide web-based survey (n = 125; 74.4% male, mean age 52.2 ± 13.9 years, diagnoses: spinal cord injury 37.6%, cerebrovascular disease 27.2%, and cerebral palsy 16.8%). Results: Participants described the psychosocial values of standing, such as feeling more confident and being able to interact with others at eye level. The web survey revealed that most participants believed that standing was beneficial for health (76.8%) and task efficiency (76.0%), although only 49.6% showed an interest in standing wheelchairs. The multivariate analysis revealed that ongoing standing training was the strongest predictor of positive perceptions of health benefits, task efficiency, and interest in standing wheelchairs. Younger participants showed a greater interest in standing wheelchairs. The reported barriers include a lack of awareness, high costs, and difficulty in accessing training. Conclusions: These findings suggest the need for a user-centered design and improved support systems to integrate standing into the daily lives of people with mobility impairments. Full article

Dementia is a neurodegenerative disorder characterized by progressive cognitive impairment that significantly affects daily living. Early detection of Alzheimer’s disease—the most common form of dementia—remains essential for prompt intervention and treatment, yet clinical diagnosis often requires extensive and resource-intensive procedures. This article explores the effectiveness of automated Natural Language Processing (NLP) methods for identifying Alzheimer’s indicators from audio transcriptions of the Cookie Theft picture description task in the PittCorpus dementia database. Five NLP approaches were compared: a classical Tf–Idf statistical representation and embeddings derived from large language models (GloVe, BERT, Gemma-2B, and Linq-Embed-Mistral), each integrated with a logistic regression classifier. Transcriptions were carefully preprocessed to preserve linguistically relevant features such as repetitions, self-corrections, and pauses. To compare the performance of the five approaches, a stratified 5-fold cross-validation was conducted; the best results were obtained with BERT embeddings (84.73% accuracy) closely followed by the simpler Tf–Idf approach (83.73% accuracy) and the state-of-the-art model Linq-Embed-Mistral (83.54% accuracy), while Gemma-2B and GloVe embeddings yielded slightly lower performances (80.91% and 78.11% accuracy, respectively). Contrary to initial expectations—that richer semantic and contextual embeddings would substantially outperform simpler frequency-based methods—the competitive accuracy of Tf–Idf suggests that the choice and frequency of the words used might be more important than semantic or contextual information in Alzheimer’s detection. This work represents an effort toward implementing user-friendly software capable of offering an initial indicator of Alzheimer’s risk, potentially reducing the need for an in-person clinical visit. Full article

Background: Art therapy was recently administered to stroke patients using immersive virtual reality technology, chosen to provide the illusion of being able to replicate an artistic masterpiece. This approach was effective in improving rehabilitative outcomes due to the so-called Michelangelo effect: patients’ interaction with artistic stimuli reduced perceived fatigue and improved performance. The aim of the present study was to investigate which factors may influence those outcomes (e.g., type of artwork, esthetic valence, perceived fatigue, clinical conditions). Methods: An observational study was conducted on 25 patients with stroke who performed the protocol of virtual art therapy (VAT). In each trial, patients were asked to rate the esthetic valence of the artworks and their perceived fatigue, whereas therapists assessed patients’ participation in the therapy (Pittsburgh Rehabilitation Participation Scale, PRPS). Moreover, before and after treatment, patients’ independence in daily living activities (Barthel Index, BI), and their upper limb functioning (Manual Muscle Test, MMT) and spasticity (Ashworth Scale, AS) were measured. Results: The after-treatment BI scores depended on the before-treatment BI score (p < 0.001) and on the PRPS score (p = 0.006), which, in turn, was increased by the subjective esthetic valence (p = 0.044). Perceived fatigue is a complex factor that may have influenced the outcomes (p = 0.049). Conclusions: There was a general effect of art in reducing fatigue and improving participation of patients during therapy. The variability observed among patients mainly depended on their clinical conditions, but also on the esthetic valence given to each artwork, that could also be intertwined with the difficulty of the task. Art therapy has a high potential to improve rehabilitation outcomes, especially if combined with new technologies, but psychometric investigation of the effects of each factor is needed to design the most effective protocols. Full article

Background/Objectives: Artificial intelligence (AI) offers opportunities to enhance healthcare accessibility for people with disabilities (PwDs). However, their application in Saudi Arabia remains limited. This study explores PwDs’ experiences with AI technologies within the Kingdom’s Vision 2030 digital health framework to inform inclusive healthcare innovation strategies. Methods: Semi-structured interviews were conducted with nine PwDs across Riyadh, Al-Jouf, and the Northern Border region between January and February 2025. Participants used various AI-enabled technologies, including smart home assistants, mobile health applications, communication aids, and automated scheduling systems. Thematic analysis following Braun and Clarke’s six-phase framework was employed to identify key themes and patterns. Results: Four major themes emerged: (1) accessibility and usability challenges, including voice recognition difficulties and interface barriers; (2) personalization and autonomy through AI-assisted daily living tasks and medication management; (3) technological barriers such as connectivity issues and maintenance gaps; and (4) psychological acceptance influenced by family support and cultural integration. Participants noted infrastructure gaps in rural areas, financial constraints, limited disability-specific design, and digital literacy barriers while expressing optimism regarding AI’s potential to enhance independence and health outcomes. Conclusions: Realizing the benefits of AI for disability healthcare in Saudi Arabia requires culturally adapted designs, improved infrastructure investment in rural regions, inclusive policymaking, and targeted digital literacy programs. These findings support inclusive healthcare innovation aligned with Saudi Vision 2030 goals and provide evidence-based recommendations for implementing AI healthcare technologies for PwDs in similar cultural contexts. Full article

Background/Objectives: Neurological disorders often result in a broad spectrum of disabilities that impact mobility, communication, cognition, and sensory processing, leading to significant limitations in independence and quality of life. Assistive technologies (ATs) offer tools to compensate for these impairments, support daily living, and improve quality of life. The World Health Organization encourages the adoption and diffusion of effective assistive technology (AT). This narrative review aims to explore the integration, benefits, and challenges of assistive technologies in individuals with neurological disabilities, focusing on their role across mobility, communication, cognitive, and sensory domains. Methods: A narrative approach was adopted by reviewing relevant studies published between 2014 and 2024. Literature was sourced from PubMed and Scopus using specific keyword combinations related to assistive technology and neurological disorders. Results: Findings highlight the potential of ATs, ranging from traditional aids to intelligent systems like brain–computer interfaces and AI-driven devices, to enhance autonomy, communication, and quality of life. However, significant barriers remain, including usability issues, training requirements, accessibility disparities, limited user involvement in design, and a low diffusion of a health technology assessment approach. Conclusions: Future directions emphasize the need for multidimensional, user-centered solutions that integrate personalization through machine learning and artificial intelligence to ensure long-term adoption and efficacy. For instance, combining brain–computer interfaces (BCIs) with virtual reality (VR) using machine learning algorithms could help monitor cognitive load in real time. Similarly, ATs driven by artificial intelligence technology could be useful to dynamically respond to users’ physiological and behavioral data to optimize support in daily tasks. Full article

Rehabilitation robotic systems have been developed to perform therapy with minimal supervision from a specialist. Hence, they require algorithms to assess and support patients’ motions. Artificial intelligence brings an opportunity to implement new exercises based on previously modelled ones. This study focuses on analysing the similarities in upper extremity movements during activities of daily living (ADLs). This research aimed to model ADLs by registering and segmenting real-life movements and dividing them into sub-tasks based on joint motions. The investigation used IMU sensors placed on the body to capture upper extremity motion. Angular measurements were converted into joint variables using Matlab computations. Then, these were divided into segments assigned to the sub-functionalities of the tasks. Further analysis involved calculating mathematical measures to evaluate the similarity between the different movements. This approach allows the system to distinguish between similar motions, which is critical for assessing rehabilitation scenarios and anatomical correctness. Twenty-two ADLs were recorded, and their segments were analysed to build a database of typical motion patterns. The results include a discussion on the ranges of motion for different ADLs and gender-related differences. Moreover, the similarities and general trends for different motions are presented. The system’s control algorithm will use these results to improve the effectiveness of robotic-assisted physiotherapy. Full article

Approximately 72% of older adults in residential care have dementia and present with different levels of functioning. People living with dementia (PLwD) may not always be facilitated to independently carry out activities of daily living (ADLs) in care, increasing the likelihood of excess disability. This study incorporated Behavioural Skills Training (BST) to train healthcare staff how to increase opportunities for independence for PLwD by using task analyses and least-to-most (L-M) prompting procedures during ADLs. Three healthcare staff, two female and one male (mean age = 42.67, SD = 16.82), participated in the intervention. The What Works Clearinghouse (WWC) Single-Case Design Technical Documentation guided the study’s design. A randomised single-case experimental (N-of-1) design was employed, using a multiple-baseline design (MBD) across participants (n = 3) for three separate ADLs. The dependent variable (DV) was the percentage of correct staff responses when implementing the L-M prompting procedure for each step during ADLs. Visual and statistical analyses demonstrated an increase in the correct use of a task analysis and L-M prompting for all three participants during the intervention compared to the baseline: for ADL1 (assistance to stand), effect sizes were d = 5.39, d = 9.38, and d = 6.79 for the three participants, respectively; for ADL2 (assistance with drinking), effect sizes were d = 3.27, d = 8.55, and d = 3.67; and for ADL3 (assistance to brush teeth), effect sizes were d = 5.99, d = 12.93, and d = 9.39. Maintenance data ranged from 70% to 100% correct responses at follow-up (mean = 93.11% SD = 7.85). Participants successfully generalised skills learned to two new ADLs (PLwD eating a meal and putting on a jumper). BST was demonstrated to be an effective training strategy to increase opportunities for independent responding for PLwD in care environments. The contingencies influencing staff behaviour require attention within the healthcare environment. Full article

Neck pain is a significant global health concern and a leading cause of disability. Conventional clinical neck assessments often rely on maximal Cervical Range of Motion (CROM) measurements, which may not accurately reflect functional limitations experienced during activities of daily living (ADLs). This study introduces a novel approach to evaluate neck functional status by employing a virtual reality (VR) environment to simulate an apple-harvesting task. Three-dimensional head kinematics were continuously recorded in 60 participants (30 with clinically significant neck pain and 30 asymptomatic) as they performed the task. Spectral analysis of the data revealed that individuals with neck pain exhibited slower head rotation speed, particularly in the transverse and frontal planes, compared to the pain-free group, as evidenced by higher spectral power in the low-frequency band [0, 0.1] Hz and lower power in the [0.1, 0.5] Hz band. Furthermore, participants with neck pain required significantly more time to complete the apple-harvesting task. The VR system demonstrated high usability (SUS score = 84.21), and no adverse effects were reported. These findings suggest that VR-based assessment during simulated ADLs can provide valuable information about the functional impact of neck pain beyond traditional CROM measurements, potentially enabling remote evaluation and personalized telerehabilitation strategies. Full article

In addition to axial motor complications such as abnormal posture, instability, falls, and gait variability, neurodegenerative diseases like Parkinsonian syndromes include executive dysfunction, Parkinson’s disease dementia, and neuropsychiatric symptoms. These motor disorders significantly affect mobility, quality of life, and well-being. Recently, physical activity of various intensities monitored both remotely and face-to-face via digital health technologies, mobile platforms, or sensory cues has gained relevance in managing idiopathic and atypical Parkinson’s disease (PD and APD). Remote monitoring solutions, including home-based digital health assessments using semi-structured activities, offer unique advantages. Real-world gait parameters like walking speed can now be continuously assessed with body-worn sensors. Developing effective strategies to slow pathological aging and mitigate neurodegenerative progression is essential. This study presents outcomes of using digital health technologies (DHTs) for remote assessment of motor function, physical activity, and daily living tasks, aiming to reduce disease progression in PD and APD. In addition to wearable inertial sensors, clinical rating scales and digital biomarkers enhance the ability to characterize and monitor motor symptoms. By reviewing recent literature, we identified emerging trends in quantifying and intervening in neurodegeneration using tools that evaluate both remote and face-to-face physical activity. Our findings confirm that DHTs offer accurate detection of motor fluctuations and support clinical evaluations. In conclusion, DHTs represent a scalable, effective strategy for improving the clinical management of PD and APD. Their integration into healthcare systems may enhance patient outcomes, support early intervention, and help delay the progression of both motor and cognitive symptoms in aging individuals. Full article

Background/Objectives: Occupational therapy (OT) plays a crucial role in addressing functional limitations and promoting independence in Parkinson’s disease (PD) patients. OT interventions target motor skills, daily activities, and engagement in meaningful tasks. Telehealth, the remote delivery of healthcare services, has expanded access to rehabilitation, including OT for PD. While several studies have examined the benefits of online OT, a comprehensive assessment of its impact on functional outcomes and quality of life (QoL) is needed. This review aimed to evaluate the effects of online OT interventions on functional outcomes and QoL of patients with PD. Methods: This review employed a systematized approach, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, though it did not constitute a full systematic review or meta-analysis. A comprehensive search was conducted across PubMed, Web of Science, Scopus, and Embase databases between August 2023 and September 2024. The search targeted randomized controlled trials (RCTs) investigating telerehabilitation interventions in OT for individuals with PD. Studies were excluded if they were not published in English, did not employ an RCT design, or lacked a focus on telerehabilitation within the scope of occupational therapy for PD. Additionally, systematic reviews, meta-analyses, qualitative studies, and studies without measurable outcomes were excluded. Nine studies met the inclusion criteria, with four involving occupational therapists directly and five evaluating interventions within the scope of OT practice. Results: The primary outcomes of this review focused on mobility improvements in PD patients, assessed through gait metrics such as gait speed, stride length, and gait variability. Secondary outcomes evaluated the impact of telerehabilitation on QoL, using tools such as the Parkinson’s Disease Questionnaire (PDQ-39) and other disease-specific instruments. The findings demonstrated that online OT interventions significantly improved motor skills, cognitive function, and activities of daily living in PD patients. Furthermore, these interventions enhanced overall well-being and QoL. The remote format fostered sustained engagement and adherence to therapy, contributing to better long-term outcomes. Conclusions: Online OT interventions show promising potential for improving functional outcomes and QoL in PD patients. These findings underscore the potential of telehealth to expand access to OT services, thereby enhancing long-term rehabilitation outcomes for this population. Full article

Background and Objectives: Brain-computer interface (BCI) technology is revolutionizing stroke rehabilitation by offering innovative neuroengineering solutions to address neurological deficits. By bypassing peripheral nerves and muscles, BCIs enable individuals with severe motor impairments to communicate their intentions directly through control signals derived from brain activity, opening new pathways for recovery and improving the quality of life. The aim of this study was to explore the beneficial effects of BCI system-based interventions on upper limb motor function and performance of activities of daily living (ADL) in stroke patients. We hypothesized that integrating BCI into occupational therapy would result in measurable improvements in hand strength, dexterity, independence in daily activities, and cognitive function compared to baseline. Materials and Methods: An observational study was conducted on 56 patients with subacute stroke. All patients received standard medical care and rehabilitation for 54 days, as part of the comprehensive treatment protocol. Patients underwent BCI training 2–3 times a week instead of some occupational therapy sessions, with each patient completing 15 sessions of BCI-based recoveriX treatment during rehabilitation. The occupational therapy program included bilateral exercises, grip-strengthening activities, fine motor/coordination tasks, tactile discrimination exercises, proprioceptive training, and mirror therapy to enhance motor recovery through visual feedback. Participants received ADL-related training aimed at improving their functional independence in everyday activities. Routine occupational therapy was provided five times a week for 50 min per session. Upper extremity function was evaluated using the Box and Block Test (BBT), Nine-Hole Peg Test (9HPT), and dynamometry to assess gross manual dexterity, fine motor skills, and grip strength. Independence in daily living was assessed using the Functional Independence Measure (FIM). Results: Statistically significant improvements were observed across all the outcome measures (p < 0.001). The strength of the stroke-affected hand improved from 5.0 kg to 6.7 kg, and that of the unaffected hand improved from 29.7 kg to 40.0 kg. Functional independence increased notably, with the FIM scores rising from 43.0 to 83.5. Cognitive function also improved, with MMSE scores increasing from 22.0 to 26.0. The effect sizes ranged from moderate to large, indicating clinically meaningful benefits. Conclusions: This study suggests that BCI-based occupational therapy interventions effectively improve upper extremity motor function and daily functions and have a positive impact on the cognition of patients with subacute stroke. Full article

Background: Activities of Daily Living (ADLs) are essential tasks performed at home and used in healthcare to monitor sedentary behavior, track rehabilitation therapy, and monitor chronic obstructive pulmonary disease. The Barthel Index, used by healthcare professionals, has limitations due to its subjectivity. Human activity recognition (HAR) is a more accurate method using Information and Communication Technologies (ICTs) to assess ADLs more accurately. This work aims to create a singular, adaptable, and heterogeneous ADL dataset that integrates information from various sources, ensuring a rich representation of different individuals and environments. Methods: A literature review was conducted in Scopus, the University of California Irvine (UCI) Machine Learning Repository, Google Dataset Search, and the University of Cauca Repository to obtain datasets related to ADLs. Inclusion criteria were defined, and a list of dataset characteristics was made to integrate multiple datasets. Twenty-nine datasets were identified, including data from various accelerometers, gyroscopes, inclinometers, and heart rate monitors. These datasets were classified and analyzed from the review. Tasks such as dataset selection, categorization, analysis, cleaning, normalization, and data integration were performed. Results: The resulting unified dataset contained 238,990 samples, 56 activities, and 52 columns. The integrated dataset features a wealth of information from diverse individuals and environments, improving its adaptability for various applications. Conclusions: In particular, it can be used in various data science projects related to ADL and HAR, and due to the integration of diverse data sources, it is potentially useful in addressing bias in and improving the generalizability of machine learning models. Full article

Computational thinking (CT), an interrelation of skills and practices, is a crucial competency that empowers individuals to tackle logical problems, enabling them to overcome various challenges in their daily lives. To help Deaf students (those with hearing loss and using sign language for communication) enhance their CT, a STEAM learning program using a physical computing tool is proposed. The learning program composes four courses: learning concepts, implementing concepts, finding solutions to real problems and developing innovations. The program engaged Deaf students from 18 Deaf schools. It is geared towards boosting students’ CT and facilitating their capacity to devise technology-based solutions. The program measured students’ CT effectiveness based on the CT framework: concepts, practices, and perspectives. The measurement encompassed multiple-choice assessments for CT concepts, task rubrics for CT practices, and interview and invention observations for CT perspectives. The program concludes with participating in a science project competition, using a physical computing tool, called KidBright, to solve real-world issues by integrating science, mathematics, and art. After completing the learning program, Deaf students demonstrated an improved understanding of CT concepts, performing high-level CT practices, and expressing strong CT perspectives. These indicate that a STEAM learning program utilizing a physical computing tool can help Deaf students enhance their computational thinking. Full article

Background/Objectives: Activities of Daily Living (ADLs) are crucial for assessing an individual’s autonomy, encompassing tasks such as eating, dressing, and moving around, among others. Predicting these activities is part of health monitoring, elderly care, and intelligent systems, improving quality of life, and facilitating early dependency detection, all of which are relevant components of personalized health and social care. However, the automatic classification of ADLs from sensor data remains challenging due to high variability in human behavior, sensor noise, and discrepancies in data acquisition protocols. These challenges limit the accuracy and applicability of existing solutions. This study details the modeling and evaluation of real-time ADL classification models based on batch learning (BL) and stream learning (SL) algorithms. Methods: The methodology followed is the Cross-Industry Standard Process for Data Mining (CRISP-DM). The models were trained with a comprehensive dataset integrating 23 ADL-centric datasets using accelerometers and gyroscopes data. The data were preprocessed by applying normalization and sampling rate unification techniques, and finally, relevant sensor locations on the body were selected. Results: After cleaning and debugging, a final dataset was generated, containing 238,990 samples, 56 activities, and 52 columns. The study compared models trained with BL and SL algorithms, evaluating their performance under various classification scenarios using accuracy, area under the curve (AUC), and F1-score metrics. Finally, a mobile application was developed to classify ADLs in real time (feeding data from a dataset). Conclusions: The outcome of this study can be used in various data science projects related to ADL and Human activity recognition (HAR), and due to the integration of diverse data sources, it is potentially useful to address bias and improve generalizability in Machine Learning models. The principal advantage of online learning algorithms is dynamically adapting to data changes, representing a significant advance in personal autonomy and health care monitoring. Full article