Search Results (90)

Smart walking aids represent a growing trend in assistive technologies designed to support individuals with mobility impairments in their daily lives and rehabilitation. Previous research has introduced sensor-integrated systems that provide user feedback to enhance safety and functional mobility. However, a comprehensive overview of their technological and functional characteristics is lacking. To address this gap, this scoping review systematically mapped the current state of research in sensor-based walking aids, focusing on device types, sensor technologies, application contexts, target populations, and reported outcomes. In addition, integrated artificial intelligence (AI)-based approaches for functional support and health monitoring were examined. Following PRISMA-ScR guidelines, 35 peer-reviewed articles were identified from three databases: ACM Digital Library, IEEE Xplore, and Web of Science. Extracted data were thematically analyzed and synthesized across device types (e.g., walking canes, crutches, walkers, rollators) and use cases, including gait training, fall prevention, and daily support. Findings show that, while many prototypes show promising features, few have been evaluated in clinical settings or over extended periods. A lack of standardized methods for sensor location assessment, often the superficial implementation of feedback modalities, and limited integration with other assistive technologies were identified. In addition, system validation and user testing lack consensus, with few long-term studies and often incomplete demographic data. Diversity in data communication approaches and the heterogeneous use of AI algorithms were also notable. The review highlights key challenges and research opportunities to guide the future development of intelligent, user-centered mobility systems. Full article

Burn injuries result in complex physiological and psychological sequelae, including hypermetabolism, muscle wasting, mobility impairment, scarring, and disrupted body image. While advances in acute care have improved survival, comprehensive rehabilitation strategies are critical for restoring function, appearance, and psychosocial well-being. Structured physical activity, including resistance and aerobic training, plays a central role in counteracting muscle atrophy, improving cardiovascular function, enhancing scar quality, and promoting psychological resilience and body image restoration. This narrative review synthesizes the current evidence on the effects of exercise-based interventions on post-burn recovery, highlighting their therapeutic mechanisms, clinical applications, and implementation challenges. In addition to physical training, emerging technologies such as virtual reality, aquatic therapy, and compression garments offer promising adjunctive benefits. Notably, artificial intelligence (AI) is gaining traction in burn rehabilitation through its integration into wearable biosensors and telehealth platforms that enable real-time monitoring, individualized feedback, and predictive modeling of recovery outcomes. These AI-driven tools have the potential to personalize exercise regimens, support remote care, and enhance scar assessment and wound tracking. Overall, the integration of exercise-based interventions with digital technologies represents a promising, multimodal approach to burn recovery. Future research should focus on optimizing exercise prescriptions, improving access to personalized rehabilitation tools, and advancing AI-enabled systems to support long-term recovery, functional independence, and positive self-perception among burn survivors. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that impairs motor function, including the fine motor control required for handwriting. Traditional diagnostic methods often lack sensitivity and objectivity in the early stages, limiting opportunities for timely intervention. There is a growing need for non-invasive, accessible tools capable of capturing subtle motor changes that precede overt clinical symptoms. Among early PD manifestations, handwriting impairments such as micrographia have shown potential as digital biomarkers. However, conventional handwriting analysis remains subjective and limited in scope. Recent advances in artificial intelligence (AI) and machine learning (ML) enable automated analysis of handwriting dynamics, such as pressure, velocity, and fluency, collected via digital tablets and smartpens. These tools support the detection of early-stage PD, monitoring of disease progression, and assessment of therapeutic response. This paper highlights how AI-enhanced handwriting analysis provides a scalable, non-invasive method to support diagnosis, enable remote symptom tracking, and personalize treatment strategies in PD. This approach integrates clinical neurology with computer science and rehabilitation, offering practical applications in telemedicine, digital health, and personalized medicine. By capturing dynamic features often missed by traditional assessments, AI-based handwriting analysis contributes to a paradigm shift in the early detection and long-term management of PD, with broad relevance across neurology, digital diagnostics, and public health innovation. Full article

Background/Objectives: Ataxic dysarthria is a speech disorder characterized by the impaired coordination of movement due to cerebellar dysfunction. Despite its clinical relevance, few studies have explored its rehabilitation. This study aimed to evaluate the applicability of IMITAF, an adaptive computer-based clinical treatment protocol originally developed to target aphasia with a novel population comprising individuals with ataxic dysarthria. The approach leverages principles of procedural motor learning. Methods: Ten patients with ataxic dysarthria due to neurodegenerative disease were retrospectively studied. All patients received approximately one month of speech–language (SL) treatment. Among them, (1) three patients (LL, MD, and BoA) adjunctively received the IMITAF treatment, forming the experimental group, and (2) the remaining seven patients did not receive IMITAF, serving as the control group. Dysarthria was assessed using the “Protocollo di Valutazione Disartria e Disfonia” (PVDD). The applicability of IMITAF was assessed through within-session performance and by direct single-case comparisons of total PVDD scores pre- and post-treatment. Additionally, multiple single-case Crawford analyses were conducted using PVDD scores and subscores to compare trained (i.e., directly targeted) and untrained abilities between the experimental and control groups Results: Patients in the IMITAF group showed improvements during exercises, with further increases in total PVDD scores post-treatment. Two patients (LL and BoA) showed significant gains, while MD’s scores remained stable. Compared to the control group, all three experimental patients demonstrated measurable improvements in trained core deficits associated with dysarthria, including phonation, articulation, intelligibility, and prosody (as assessed by PVDD). Conclusions: These findings suggest that IMITAF may offer therapeutic benefits for patients with ataxic dysarthria. By engaging a cortico-subcortical network involved in procedural motor learning, IMITAF may help mitigate speech deficits resulting from cerebellar dysfunction. This preliminary evidence supports the potential of IMITAF as a promising adjunctive tool in the rehabilitation of ataxic dysarthria. 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

Piano-based occupational therapy has emerged as an engaging and effective rehabilitation strategy for improving upper limb motor functions. However, a lack of comprehensive biomechanical modeling, objective rehabilitation assessment, and real-time fatigue monitoring has limited its clinical optimization. This study developed a comprehensive “key–finger–exoskeleton” biomechanical model based on Hill-type muscle dynamics and rigid-body kinematics. A three-dimensional muscle synergy analysis method using non-negative tensor factorization (NTF) was proposed to quantitatively assess rehabilitation effectiveness. Furthermore, a real-time Comprehensive Muscle Fatigue Index (CMFI) based on multi-muscle coordination was designed for fatigue monitoring during therapy. Experimental validations demonstrated that the biomechanical model accurately predicted interaction forces during piano-playing tasks. After three weeks of therapy, patients exhibited increased synergy modes and significantly improved similarities with healthy subjects across spatial, temporal, and frequency domains, particularly in the temporal domain. The CMFI showed strong correlation (r > 0.83, p < 0.001) with subjective fatigue ratings, confirming its effectiveness in real-time fatigue assessment and training adjustment. The integration of biomechanical modeling, synergy-based rehabilitation evaluation, and real-time fatigue monitoring offers an objective, quantitative framework for optimizing piano-based rehabilitation. These findings provide important foundations for developing intelligent, adaptive rehabilitation systems. Full article

Background/Objectives: Mild Cognitive Impairment (MCI) represents a clinical syndrome characterized by cognitive decline greater than expected for an individual’s age and education level but not severe enough to significantly interfere with daily activities, with variable trajectories that may remain stable, progress to dementia, or occasionally revert to normal cognition. This systematic review examines neurotechnological approaches to cognitive rehabilitation in MCI populations, including neuromodulation, electroencephalography (EEG), virtual reality (VR), cognitive training, physical exercise, and artificial intelligence (AI) applications. Methods: A systematic review following PRISMA guidelines was conducted on 34 empirical studies published between 2014 and 2024. Studies were identified through comprehensive database searches and included if they employed neurotechnological interventions targeting cognitive outcomes in individuals with MCI. Results: Evidence indicates promising outcomes across multiple intervention types. Neuromodulation techniques showed beneficial effects on memory and executive function. EEG analyses identified characteristic neurophysiological markers of MCI with potential for early detection and monitoring. Virtual reality enhanced assessment sensitivity and rehabilitation engagement through ecologically valid environments. Cognitive training demonstrated the most excellent efficacy with multi-domain, adaptive approaches. Physical exercise interventions yielded improvements through multiple neurobiological pathways. Emerging AI applications showed potential for personalized assessment and intervention through predictive modeling and adaptive algorithms. Conclusions: Neurotechnological approaches offer promising avenues for MCI rehabilitation, with the most substantial evidence for integrated interventions targeting multiple mechanisms. Neurophysiological monitoring provides valuable biomarkers for diagnosis and treatment response. Future research should focus on more extensive clinical trials, standardized protocols, and accessible implementation models to translate these technological advances into clinical practice. Full article

Background/Objectives: This retrospective study aimed to evaluate whether PRP infiltrations improve quadriceps muscle strength recovery following anterior cruciate ligament reconstruction (ACLR), while minimizing the recovery time required to resume daily activities and sports. Numerous studies have explored the use of platelet-rich plasma (PRP) in treating ACL injuries. PRP therapy has demonstrated high efficacy in accelerating ligament healing in animal models. However, clinical trials involving human participants have reported inconsistent results regarding the effects of PRP on ACL reconstruction outcomes. Methods: Between 2020 and 2024, a total of 68 subjects who underwent ACLR were included in the study. Participants were divided into two groups, namely a treatment group that followed a standard rehabilitation protocol and received PRP infiltrations, and a control group that followed the same protocol without PRP treatment. Muscle strength was assessed using the isometric max strength balance (IMSB) test and the concentric max strength balance (CMSB) test, both performed using the Kineo Intelligent Load device (Globus Kineo 7000, Italian Excellence, Rome, Italy). Results: The results of IMSB test showed a significant difference between treatment groups according to a two-way ANOVA test (F(1, 198) = 7.345; p = 0.0073). The PRP-treated group showed significantly higher quadriceps muscle strength at 6 months (34.9 ± 9.6 vs. 30.0 ± 8.2 kg). The CMSB test also showed a significant difference at 6 months (F(1, 198) = 5.976; p = 0.00154), with the PRP-treated group having significantly higher concentric muscle strength (35.5 ± 9.5 vs. 30.7 ± 8.5 kg). Conclusions: These findings suggest that post-ligamentoplasty PRP infiltrations may have beneficial effects on muscle strength recovery. However, further prospective studies with larger sample sizes are necessary to confirm these results. Full article

Artificial intelligence (AI) is revolutionizing the field of orthopedic bioengineering by increasing diagnostic accuracy and surgical precision and improving patient outcomes. This review highlights using AI for orthopedics in preoperative planning, intraoperative robotics, smart implants, and bone regeneration. AI-powered imaging, automated 3D anatomical modeling, and robotic-assisted surgery have dramatically changed orthopedic practices. AI has improved surgical planning by enhancing complex image interpretation and providing augmented reality guidance to create highly accurate surgical strategies. Intraoperatively, robotic-assisted surgeries enhance accuracy and reduce human error while minimizing invasiveness. AI-powered smart implant sensors allow for in vivo monitoring, early complication detection, and individualized rehabilitation. It has also advanced bone regeneration devices and neuroprosthetics, highlighting its innovation capabilities. While AI advancements in orthopedics are exciting, challenges remain, like the need for standardized surgical system validation protocols, assessing ethical consequences of AI-derived decision-making, and using AI with bioprinting for tissue engineering. Future research should focus on proving the reliability and predictability of the performance of AI-pivoted systems and their adoption within clinical practice. This review synthesizes recent developments and highlights the increasing impact of AI in orthopedic bioengineering and its potential future effectiveness in bone care and beyond. Full article

The growing application of Fiber-Reinforced Polymer (FRP) composites in rehabilitating deteriorating concrete infrastructure underscores the need for reliable, cost-effective, and automated nondestructive testing (NDT) methods. This review provides a comprehensive analysis of existing and emerging NDT techniques used to assess externally bonded FRP (EB-FRP) systems, emphasizing their accuracy, limitations, and practicality. Various NDT methods, including Ground-Penetrating Radar (GPR), Phased Array Ultrasonic Testing (PAUT), Infrared Thermography (IRT), Acoustic Emission (AE), and Impact–Echo (IE), are critically evaluated in terms of their effectiveness in detecting debonding, voids, delaminations, and other defects. Recent technological advancements, particularly the integration of artificial intelligence (AI) and machine learning (ML) in NDT applications, have significantly improved defect characterization, automated inspections, and real-time data analysis. This review highlights AI-driven NDT approaches such as automated crack detection, hybrid NDT frameworks, and drone-assisted thermographic inspections, which enhance accuracy and efficiency in large-scale infrastructure assessments. Additionally, economic considerations and cost–performance trade-offs are analyzed, addressing the feasibility of different NDT methods in real-world FRP-strengthened structures. Finally, the review identifies key research gaps, including the need for standardization in FRP-NDT applications, AI-enhanced defect quantification, and hybrid inspection techniques. By consolidating state-of-the-art research and emerging innovations, this paper serves as a valuable resource for engineers, researchers, and practitioners involved in the assessment, monitoring, and maintenance of FRP-strengthened concrete structures. Full article

The strong correlation between evoked potentials (EPs) and American Spinal Injury Association (ASIA) scores in individuals with spinal cord injury (SCI) suggests that EPs may serve as reliable predictive markers for rehabilitation progress. Numerous studies have confirmed a relationship between variations in somatosensory evoked potentials (SSEPs) and ASIA scores, especially in the early stages of SCI. Machine learning’s (ML’s) increasing importance in medicine is driven by the growing availability of health data and improved algorithms. It enables the creation of predictive models for disease diagnosis, progression prediction, personalized treatment, and improved healthcare efficiency. Data-driven approaches can significantly improve patient care, reduce costs, and facilitate personalized medicine. The meticulous analysis of medical data is crucial for timely disease identification, leading to effective symptom management and appropriate treatment. This study applies artificial intelligence to identify predictors of SCI progression, as measured by the disability index, ASIA impairment scale (AIS), and final motor recovery. We aim to clarify the prognostic role of electrophysiological testing (SSEPs, MEPs, and nerve conduction studies (NCSs)) in SCI. We analyzed data from a medical database of 123 records. We developed an ML-based intelligent system, utilizing ensemble algorithms combining decision trees and neural network approaches, to predict SCI recovery. Our evaluation showed SEP accuracies of 90% for motor recovery prediction and 80% for AIS scale determination, comparable to full electrophysiology evaluation accuracies of 93% and 89%, respectively, and generally superior results compared to MEP and NCS results. EPs emerged as the best predictors, comparable to a comprehensive electrophysiology assessment, significantly improving accuracy compared to clinical findings alone. An electrophysiological assessment, when available, increased overall accuracy for final motor recovery prediction to 93% (from a maximum of 75%) and, for ASIA score determination, to 89% (from a maximum of 66%). Further validation is needed with a larger dataset. Future research should validate that sensory electrophysiology assessment is a less expensive, portable, and simpler alternative to other prognostic tests and more effective than clinical assessments, like the AIS, biomarker for SCI, and personalized rehabilitation planning. Full article

Post-stroke finger dysfunction severely impacts patients’ daily living abilities and quality of life. Traditional rehabilitation assessment methods face challenges such as high subjectivity, insufficient precision, and difficulty in capturing subtle changes. These challenges are particularly pronounced in small-sample data scenarios, where the accuracy and robustness of assessment models are limited. This study proposes an intelligent rehabilitation assessment method tailored for small-sample scenarios, combining the rehabilitation matching value (RMV) with machine learning to address the challenges of rehabilitation assessment in such contexts. A rehabilitation matching value calculation model is constructed based on existing data, and interpolation methods are employed to expand the small-sample dataset. Machine learning models are then utilized for validation. Experimental results demonstrate that the proposed method effectively captures subtle changes in finger function, significantly improving the sensitivity and accuracy of rehabilitation assessments. This provides a scientific basis for the development of personalized rehabilitation training plans. Compared to traditional methods, the proposed approach exhibits significant advantages in flexibility, practicality, and adaptability to small-sample scenarios. Full article

Background: Velopharyngeal insufficiency (VPI) is a failure of the sphincter mechanism, causing speech patterns like hypernasality and decreased intelligibility. Causes include structural, neurologic, and mechanical issues. Treatment options include non-surgical and surgical interventions, but complications can arise. A new approach using the buccal flap (BF) has been suggested for palatal length augmentation. This systematic review assessed speech outcomes after BF palatal lengthening. Methods: A thorough investigation was conducted by methodically reviewing numerous databases, including PubMed, Scopus, Web of Science, and Embase, until December 2024. The goal of our analysis was to find studies that assess the short- and long-term efficacy of BF on speech outcomes on patients with VPI. We used the NIH Quality Assessment Tool to assess the quality of the evidence, ensuring the dependability of the results reached during these investigations. Results: This systematic review identified 23 studies (total sample size of 995) that assessed the speech outcomes of BF on VPI. The BF significantly improves speech outcomes in patients with VPI. Hypernasality improved significantly post-surgery, with outcomes measured using different scales and methods, including both subjective and objective tools. Benefits were observed within three months postoperatively, with sustained benefits up to 15 months in several studies. Speech intelligibility also improved notably, with mean differences up to 1.09 (p < 0.001). Reductions in audible nasal air emissions were observed, though some variability was noted across studies. Secondary outcomes, including better velopharyngeal closure and decreased facial grimacing, further highlight its efficacy. However, inconsistent findings for nasal turbulence and limited long-term data suggest that benefits may plateau over time. These findings support the BF as an effective intervention, though further research is needed on extended outcomes. Conclusions: BF is an effective surgical intervention for VPI, significantly improving hypernasality, speech intelligibility, and audible nasal air emissions. While benefits are evident across diverse populations, long-term outcomes and secondary features, such as nasal turbulence, show variability, emphasizing the need for individualized approaches and continued follow-up. This technique offers a reliable option for functional and speech rehabilitation, though further research is needed to optimize its long-term efficacy and broader outcomes. Full article

Falls, a major cause of injury and disability, particularly among older adults, present a significant public-health challenge. Existing methods of balance assessment often lack the sensitivity and specificity needed to identify subtle deviations from normal patterns, hindering early intervention. To address this gap, we introduced a novel artificial intelligence-based tool that leverages anomaly detection to provide a comprehensive assessment of balance performance across all age groups. This study evaluated the tool’s effectiveness in 163 individuals aged 18–85 years who were assessed using a force platform under four conditions: eyes open and eyes closed on firm and foam surfaces. Data analysis, employing an artificial neural network with 19 socio-anthropometric and postural variables, showed the tool’s exceptional accuracy (R = 0.99998) in differentiating among balance profiles. Notably, the model highlighted the significant impact of age and education on balance, with older adults demonstrating increased reliance on visual input, especially when somatosensory information was reduced on foam surfaces. In contrast, younger, more educated individuals exhibited a more integrated sensorimotor approach. These findings demonstrate that our anomaly-detection tool can identify subtle balance impairments often missed by traditional methods, offering valuable insights for personalized fall-risk assessment and intervention. This AI-based approach can provide a holistic assessment of balance, leading to more effective strategies for fall prevention and rehabilitation, particularly in aging populations. Full article

Background: Artificial intelligence (AI) and large language models (LLMs) are increasingly used in healthcare, with applications in clinical decision-making and workflow optimization. In head and neck surgery, postoperative rehabilitation is a complex, multidisciplinary process requiring personalized care. This study evaluates the feasibility of using LLMs to generate tailored rehabilitation programs for patients undergoing major head and neck surgical procedures. Methods: Ten hypothetical head and neck surgical clinical scenarios were developed, representing oncologic resections with complex reconstructions. Four LLMs, ChatGPT-4o, DeepSeek V3, Gemini 2, and Copilot, were prompted with identical queries to generate rehabilitation plans. Three senior clinicians independently assessed their quality, accuracy, and clinical relevance using a five-point Likert scale. Readability and quality metrics, including the DISCERN score, Flesch Reading Ease, Flesch–Kincaid Grade Level, and Coleman–Liau Index, were applied. Results: ChatGPT-4o achieved the highest clinical relevance (Likert mean of 4.90 ± 0.32), followed by DeepSeek V3 (4.00 ± 0.82) and Gemini 2 (3.90 ± 0.74), while Copilot underperformed (2.70 ± 0.82). Gemini 2 produced the most readable content. A statistical analysis confirmed significant differences across the models (p < 0.001). Conclusions: LLMs can generate rehabilitation programs with varying quality and readability. ChatGPT-4o produced the most clinically relevant plans, while Gemini 2 generated more readable content. AI-generated rehabilitation plans may complement existing protocols, but further clinical validation is necessary to assess their impact on patient outcomes. Full article