Search Results (47)

The lack of deep engagement strategies that include cultural contextualization in the current rehabilitation game design can result in limited user motivation and low adherence in long-term rehabilitation. Integrating cultural semantics into interactive rehabilitation design offers new opportunities to enhance user engagement and emotional resonance in digital rehabilitation therapy, especially in a deeper way rather than visually. This study introduces a framework comprising a “Rehabilitation Mechanism–Interaction Design–Cultural Feature” triadic mapping model and a structured procedure. Following the framework, a hand function rehabilitation game is designed based on Chinese string puppetry, as well body rehabilitation games based on shadow puppetry and Tai Chi. The hand rehabilitation game utilizes Leap Motion for its gesture-based input and Unity3D for real-time visual feedback and task execution. Functional training gestures such as grasping, wrist rotation, and pinching are mapped to culturally meaningful puppet actions within the game. Through task-oriented engagement and narrative immersion, the design improves cognitive accessibility, emotional motivation, and sustained participation. Evaluations are conducted from rehabilitation professionals and target users. The results demonstrate that the system is promising in integrating motor function training with emotional engagement, validating the feasibility of the proposed triadic mapping framework in rehabilitation game design. This study provides a replicable design strategy for human–computer interaction (HCI) researchers working at the intersection of healthcare, cultural heritage, and interactive media. Full article

The use of technological applications for cognitive assessment and rehabilitation is growing, yet tools specifically targeting cognition in concussed individuals remain underexplored. This rapid review examined technologies used for cognitive assessment and/or rehabilitation following concussion. Specific objectives were to identify (1) cognitive domains targeted, (2) participant populations recruited, (3) quality of assessment or therapeutic impact, and (4) user involvement in application design. A structured search across three databases yielded 16 articles analyzing 21 applications. Four (25%) focused primarily on cognition, while the remainder addressed multiple domains. Most applications assessed cognition, and study populations frequently included athletes and military members/veterans. Only two (12.5%) studies reported user feedback on application design. Findings suggest a need for broader requirements of concussed civilians to improve representativeness, and for future research to prioritize the development of applications targeting cognitive rehabilitation in concussed populations. Full article

Background/Objectives: Anorexia nervosa (AN) is marked by cognitive deficits, particularly reduced mental flexibility and weak central coherence, which may sustain the core psychopathological symptoms. While cognitive remediation therapy (CRT) has shown efficacy in improving these cognitive processes in AN, evidence on computer-based CRT remains limited. This study aims to evaluate the feasibility and efficacy of integrating computer-assisted cognitive remediation therapy (CA-CRT) into standard nutritional rehabilitation (treatment as usual, TAU) to improve the targeted cognitive and psychological parameters among inpatients with AN in a more personalized and scalable way. Methods: A multicenter randomized controlled trial (RCT) will be conducted. At least 54 participants with a diagnosis of AN will be recruited at each site and randomized into either the experimental or control group after initial screening. The intervention will last five weeks and include 15 individual CA-CRT sessions alongside 10 individual CR sessions, delivered in addition to standard care. The primary and secondary outcomes will be assessed at the end of the intervention to evaluate the changes in cognitive flexibility, central coherence, and psychological functioning. Results: Participants receiving CA-CRT are expected to develop more flexible and integrated thinking styles and achieve greater improvements in clinical outcomes compared to those receiving standard care alone, supporting a more personalized therapeutic approach. Conclusions: These findings would underscore the feasibility and clinical value of incorporating CA-CRT into standard inpatient treatment for AN. By specifically targeting cognitive inflexibility and poor central coherence in a scalable, individualized format, CA-CRT may enhance treatment effectiveness and support the development of patient-centered interventions tailored to the cognitive profiles of individuals with AN. Full article

Motor imagery (MI) is a widely used paradigm in brain–computer interface (BCI) systems, with applications in rehabilitation and neuroscience. In this study, magnetoencephalography (MEG) signals were employed to analyze MI and other mental imagery tasks. MEG provides high spatial resolution, facilitating the classification of imagery-related signals. This study aims to enhance the classification of motor and cognitive imagery (CI) tasks using a public MEG dataset including four distinct tasks: imagining the movement of hands (H) or feet (F), performing arithmetic subtraction (S), and forming words (W). MEG signals were decomposed using five signal-decomposition methods: Empirical Wavelet Transform (EWT), Maximal Overlap Discrete Wavelet Transform (MODWT), Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), and Multivariate Variational Mode Decomposition (MVMD). Feature extraction was performed using the Common Spatial Patterns (CSP), with t-test-based feature selection. Subsequently, commonly used machine learning algorithms were employed to classify MI and CI tasks. The results indicate that MVMD and MODWT achieved the highest accuracies when combined with the Artificial Neural Networks. MVMD yielded superior performances in (H and W: 79.2%; F and S: 75.8%; and F and W: 73.8%) tasks. MODWT achieved high accuracies in the H and W (75.9%) and F and W (76.3%) tasks. Overall, motor and non-motor pairs (H and W, F and W) yielded higher accuracy than the cognitive pair (W and S). Full article

Background/Objective: The use of immersive virtual reality (VR) for cognitive training in older adults has shown promising results in recent years. However, the number of well-designed studies remains limited, and variability in methodologies makes it difficult to draw generalizable conclusions. This systematic review aims to examine the effects of VR-based cognitive training in older adults, describe the technological characteristics of these interventions, identify current gaps in the literature, and suggest future research directions. Methods: Following PRISMA guidelines, a search was conducted across major databases (PubMed, PsycINFO, Scopus, ProQuest, ACM, and Web of Science) from 2018 to 2025. The database search identified 156 studies, of which 12 met the inclusion criteria after screening and eligibility assessment. Across these studies, a total of 3202 older adult participants (aged 60 years or older) were included. Interventions varied in duration from 4 to 36 sessions, targeting domains such as memory, executive function, attention, and global cognition. Most interventions were based on cognitive training, with a few employing cognitive stimulation or cognitive rehabilitation approaches. Quality was assessed using the Effective Public Health Practice Project tool. Results: Most studies reported positive effects of VR interventions on cognitive domains such as attention, executive functions, and global cognition. Fewer studies showed improvements in memory. The majority used head-mounted displays connected to computers and custom-built software, often without public access. Sample sizes were generally small, and blinding procedures were often unclear. The average methodological quality was moderate. Conclusions: Immersive VR has potential as an effective tool for cognitive training in older adults. Future research should include larger randomized controlled trials, long-term follow-up, standardized intervention protocols, and the development of accessible software to enable replication and broader application in clinical and community settings. Full article

This umbrella review aimed to identify, analyze, and synthesize the results of existing literature reviews related to patient-oriented smart applications to support healthcare provision for patients with Parkinson’s. An electronic search was conducted on Scopus, Web of Science, and PubMed, and, after screening using predefined eligibility criteria, 85 reviews were included in the umbrella review. The included studies reported on smart applications integrating wearable devices, smartphones, serious computerized games, and other technologies (e.g., ambient intelligence, computer-based objective assessments, or online platforms) to support the diagnosis and monitoring of patients with Parkinson’s, improve physical and cognitive rehabilitation, and support disease management. Numerous smart applications are potentially useful for patients with Parkinson’s, although their full clinical potential has not yet been demonstrated. This is because the quality of their clinical assessments, as well as aspects related to their acceptability and compliance with requirements from regulatory bodies, have not yet been adequately studied. Future research requires more aligned methods and procedures for experimental assessments, as well as collaborative efforts to avoid replication and promote advances on the topic. Full article

Parkinson’s disease (PD) is a progressive neurological disorder with motor and non-motor symptoms that are inadequately addressed by current pharmacological and surgical therapies. Brain–computer interfaces (BCIs), particularly those based on electroencephalography (eBCIs), provide a promising, non-invasive approach to personalized neurorehabilitation. This narrative review explores the clinical potential of BCIs in PD, discussing signal acquisition, processing, and control paradigms. eBCIs are well-suited for PD due to their portability, safety, and real-time feedback capabilities. Emerging neurophysiological biomarkers—such as beta-band synchrony, phase–amplitude coupling, and altered alpha-band activity—may support adaptive therapies, including adaptive deep brain stimulation (aDBS), as well as motor and cognitive interventions. BCIs may also aid in diagnosis and personalized treatment by detecting these cortical and subcortical patterns associated with motor and cognitive dysfunction in PD. A structured search identified 11 studies involving 64 patients with PD who used BCIs for aDBS, neurofeedback, and cognitive rehabilitation, showing improvements in motor function, cognition, and engagement. Clinical translation requires attention to electrode design and user-centered interfaces. Ethical issues, including data privacy and equitable access, remain critical challenges. As wearable technologies and artificial intelligence evolve, BCIs could shift PD care from intermittent interventions to continuous, brain-responsive therapy, potentially improving patients’ quality of life and autonomy. This review highlights BCIs as a transformative tool in PD management, although more robust clinical evidence is needed. Full article

The integration of digital serious games into speech learning (rehabilitation) has demonstrated significant potential in enhancing accessibility and inclusivity for children with speech disabilities. This review of the state of the art examines the role of serious games, Artificial Intelligence (AI), and Natural Language Processing (NLP) in speech rehabilitation, with a particular focus on interaction modalities, engagement autonomy, and motivation. We have reviewed 45 selected studies. Our key findings show how intelligent tutoring systems, adaptive voice-based interfaces, and gamified speech interventions can empower children to engage in self-directed speech learning, reducing dependence on therapists and caregivers. The diversity of interaction modalities, including speech recognition, phoneme-based exercises, and multimodal feedback, demonstrates how AI and Assistive Technology (AT) can personalise learning experiences to accommodate diverse needs. Furthermore, the incorporation of gamification strategies, such as reward systems and adaptive difficulty levels, has been shown to enhance children’s motivation and long-term participation in speech rehabilitation. The gaps identified show that despite advancements, challenges remain in achieving universal accessibility, particularly regarding speech recognition accuracy, multilingual support, and accessibility for users with multiple disabilities. This review advocates for interdisciplinary collaboration across educational technology, special education, cognitive science, and human–computer interaction (HCI). Our work contributes to the ongoing discourse on lifelong inclusive education, reinforcing the potential of AI-driven serious games as transformative tools for bridging learning gaps and promoting speech rehabilitation beyond clinical environments. 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: The sense of agency (SoA)—the subjective experience of controlling one’s own actions and their consequences—is a fundamental aspect of human cognition, volition, and motor control. Understanding how the SoA arises and is disrupted in neuropsychiatric disorders has significant implications for human–machine interface (HMI) design for neurorehabilitation. Traditional cognitive models of agency often fail to capture its full complexity, especially in dynamic and uncertain environments. Objective: This review synthesizes computational models—particularly predictive coding, Bayesian inference, and optimal control theories—to provide a unified framework for understanding the SoA in both healthy and dysfunctional brains. It aims to demonstrate how these models can inform the design of adaptive HMIs and therapeutic tools by aligning with the brain’s own inference and control mechanisms. Methods: I reviewed the foundational and contemporary literature on predictive coding, Kalman filtering, the Linear–Quadratic–Gaussian (LQG) control framework, and active inference. I explored their integration with neurophysiological mechanisms, focusing on the somato-cognitive action network (SCAN) and its role in sensorimotor integration, intention encoding, and the judgment of agency. Case studies, simulations, and XR-based rehabilitation paradigms using robotic haptics were used to illustrate theoretical concepts. Results: The SoA emerges from hierarchical inference processes that combine top–down motor intentions with bottom–up sensory feedback. Predictive coding frameworks, especially when implemented via Kalman filters and LQG control, provide a mechanistic basis for modeling motor learning, error correction, and adaptive control. Disruptions in these inference processes underlie symptoms in disorders such as functional movement disorder. XR-based interventions using robotic interfaces can restore the SoA by modulating sensory precision and motor predictions through adaptive feedback and suggestion. Computer simulations demonstrate how internal models, and hypnotic suggestions influence state estimation, motor execution, and the recovery of agency. Conclusions: Predictive coding and active inference offer a powerful computational framework for understanding and enhancing the SoA in health and disease. The SCAN system serves as a neural hub for integrating motor plans with cognitive and affective processes. Future work should explore the real-time modulation of agency via biofeedback, simulation, and SCAN-targeted non-invasive brain stimulation. Full article

Background: Psychological parameters related to alcohol dependence (AD) affect patients’ behavioral and cognitive control, decision making, impulsivity and inhibitory control. People with AD often have a chronic course with a relapse to dependent substance use even after extensive treatment. This study investigated whether the psychological parameters of patients with AD predict (a) premature termination of treatment, and/or (b) relapse into consumption of alcohol from admission until 6 weeks after discharge from an inpatient rehabilitation treatment. Methods: Participants: Alcohol-dependent patients consecutively admitted for a duration of about three months to inpatient rehabilitation treatment in a hospital specialized in substance use disorders. Craving (OCDS-G) and impulsivity (BIS-11; UPPS) were assessed with computerized questionnaires. Attentional bias and inhibitory control were measured with two computer-based experiments (dot-probe task; stop-signal task (SST)). Investigations were conducted at entry (T1); after 6 weeks (T2); and during the last two weeks of the inpatient treatment (T3). Some N = 128 patients finished the first, N = 102 the second and N = 83 the third assessments. Outcome variables were discontinuation of treatment and abstinence or relapse until follow-up 6 weeks after discharge; participants were contacted via telephone. Results: None of the variables are associated with discontinuation of treatment. Poor inhibitory control (SST) and high craving (OCDS-5) levels, measured at T1, are significantly associated with relapse. Higher impulsivity (UPPS) measured at T2 and T3 is significantly associated with relapse. Exploratory analyses showed that older age, longer inpatient treatment duration and time spent in abstinence before rehabilitation treatment were significantly associated with a reduced risk of relapse. Conclusions: Psychological parameters, craving and impulsivity levels did not predict relapse to a high degree. It is assumed that discontinuation of treatment and relapse may be associated with different issues, such as social context, and individual motivation levels. In contrast, the length of both abstinence before admission and of inpatient treatment were significantly associated with abstinence; it is here suggested that recovery time duration may be an underestimated influencing factor regarding relapse in AD patients. Full article

Background: Auditory Processing Disorder (APD) manifests as impaired auditory information processing despite normal peripheral hearing. Current clinical protocols lack standardization, hampering evidence-based intervention development. Objective: This review analyzes APD research developments from 2011 to 2025, examining diagnostic criteria, assessment protocols, and treatment effectiveness. Methods: Medline, Embase, Scopus, and Cochrane Library databases were analyzed (January 2011–January 2025), following PRISMA guidelines. Two reviewers independently screened 413 articles, with 156 meeting inclusion criteria. Analysis included chi-square tests for intervention distribution and t-tests for diagnostic comparisons (α = 0.05). Results: Among 156 studies analyzed, medical interventions were markedly underrepresented (n = 4) compared to rehabilitative approaches (n = 52; χ² = 50.28, p < 0.001). The Random Gap Detection Test and Dichotic Digits Difference Test were most frequently used (12.86% and 10.48% of studies, respectively). Cognitive skill enhancement dominated intervention strategies (52.8%), followed by computer-based rehabilitation (26.4%). Publication frequency showed consistent annual growth, peaking at 57 studies in 2024. Sample sizes were comparable between APD and CAPD studies (mean difference = 4.2 cases, t = 0.416, p = 0.679). Environmental modifications appeared in 15.1% of interventions, while speech therapy was reported in only 3.8% of studies. Conclusions: The substantial imbalance between medical and rehabilitative interventions necessitates standardized diagnostic protocols and enhanced multidisciplinary collaboration. Implementation of a hierarchical processing framework is recommended for assessment and treatment. Future research should prioritize large-scale controlled trials and unified diagnostic criteria development. Full article

Traumatic brain injury (TBI) represents a significant public health issue, causing long-term disabilities and imposing considerable socioeconomic and healthcare challenges. While advancements in acute care have improved survival rates, the demand for effective neurorehabilitation is increasing. This narrative review explores the evidence on neurorehabilitation strategies for TBI, focusing on interventions targeting cognitive, motor, and psychological recovery. A total of 32 studies were included and categorized into six approaches: non-invasive brain stimulation, virtual reality (VR), computer-based training, telerehabilitation, robot-assisted therapy (RAT), and mixed approaches. Non-invasive brain stimulation techniques, such as transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS), showed variable effectiveness in improving cognitive outcomes. VR-based therapies enhanced attention and executive functions, while RAT, such as Lokomat and exoskeletons, improved gait symmetry and functional mobility. Computer-assisted programs demonstrated benefits in rehabilitating social cognition and executive functions. Telerehabilitation and telephone-based treatments provided short-term gains but lacked sustained effects. Overall, cognitive improvements were better described and represented, while several motor improvements lacked consistency. Despite the promising results, significant gaps remain, including heterogeneity in methodologies, small sample sizes, and limited long-term outcome data. Full article

Background/Objectives: In recent years, the popularity of non-verbal cognitive training for aphasia has increased. Building on evidence that language abilities engage brain areas involved in executive functions (EFs) processing, this review aims to analyze the utility of EFs training alone or combined with traditional rehabilitation approaches to improve language abilities in aphasia. Methods: Systematic searches were performed in four databases evaluating studies focusing on the effects of EFs training in language rehabilitation, yielding 185 studies. After reading the full text of the selected studies and applying predefined inclusion criteria, nine studies were included based on pertinence and relevance to the topic. This systematic review has been registered in the Prospective Register of Systematic Reviews (PROSPERO 2024) with the number CRD42024519087. Results: The results of the analyzed studies indicate that various EFs training methods, such as computer-assisted executive control training, Cognitive Flexibility in Aphasia Therapy (CFAT), and the Dr. Neuronowski^® program, as well as the combination of transcranial direct current stimulation (tDCS) with EFs training, can lead to improvements in language abilities in people with aphasia. Additionally, EFs training often results in specific effects on treated functions like working memory (near transfer effects) and untreated ones such as spoken sentence comprehension (far transfer effects). Conclusions: Despite the heterogeneity of the treatments and the small simple size of the studies analyzed, preliminary results are promising. Future research should further explore the effectiveness and specific contribution of EFs training to improving language functions in aphasia. Full article

Cardiovascular diseases pose a significant threat to global health, and cardiac rehabilitation (CR) has become a critical component of patient care. Heart Rate Variability Biofeedback (HRVB) is a non-invasive approach that helps modulate the Autonomic Nervous System (ANS) through Resonance Frequency (RF) breathing, supporting CR for cardiovascular patients. However, traditional HRVB techniques rely heavily on manual RF selection and face-to-face guidance, limiting their widespread application, particularly in home-based CR. To address these limitations, we propose a remote human-computer collaborative HRVB system, “FreeResp”, which features autonomous RF adjustment through a simplified cognitive computational model, eliminating the reliance on therapists. Furthermore, the system integrates wearable technology and the Internet of Things (IoT) to support remote monitoring and personalized interventions. By incorporating tactile guidance technology with an airbag, the system assists patients in performing diaphragmatic breathing more effectively. FreeResp demonstrated high consistency with conventional HRVB methods in determining RF values (22/24) from 24 valid training samples. Moreover, a one-month home-based RF breathing training using FreeResp showed significant improvements in Heart Rate Variability (HRV) (p < 0.05). These findings suggest that FreeResp is a promising solution for home-based CR, offering timely and precise interventions and providing a new approach to long-term cardiovascular health management. Full article