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AI-Driven Innovations in Rehabilitation: Integrating Neurological, Musculoskeletal, Sports Medicine and Occupational Therapy Interventions

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 30 July 2026 | Viewed by 4346

Special Issue Editors

Dr. Christos Kokkotis

E-Mail Website
Guest Editor
Department of Physical Education and Sport Science, Democritus University of Thrace, Komotini, 69100, Greece
Interests: soccer; machine learning; biomechanics
Special Issues, Collections and Topics in MDPI journals
Dr. Paraskevi Malliou

E-Mail Website
Guest Editor
Department of Physical Education and Sport Science, School of Physical Education, Sport Science and Occupational Therapy, Democritus University of Thrace, 69100 Komotini, Greece
Interests: anatomy; sport injuries; therapeutic exercises; musculoskeletal injuries and exercise; athletic training

Special Issue Information

Dear Colleagues,

This Special Issue will highlight cutting-edge advancements at the intersection of artificial intelligence, machine learning, and multidisciplinary rehabilitation approaches. We welcome contributions that address the development and application of intelligent systems to support recovery and functional improvement in individuals affected by neurological disorders, musculoskeletal conditions, and sports-related injuries.

In particular, we invite submissions that explore AI-enhanced strategies regarding the following:

  • Neurological rehabilitation: data-driven interventions for stroke, traumatic brain injury, Parkinson’s disease, spinal cord injuries, and other neurological conditions.
  • Musculoskeletal therapy: intelligent tools and biomechanical assessments supporting recovery from joint replacements, tendon injuries, chronic pain, and degenerative conditions.
  • Sports medicine: machine learning applications for predicting injury risk, monitoring performance, and tracking rehabilitation progress.
  • Occupational therapy: AI-supported approaches to enhance functional independence, cognitive and motor skills, adaptive equipment use, and home/work reintegration for patients with diverse diagnoses.
  • Personalized rehabilitation plans: tailoring treatment using real-time data from wearable sensors, motion tracking, neuroimaging, and patient-reported outcomes.

This Special Issue seeks to bring together researchers, clinicians, engineers, occupational therapists, and data scientists to present innovative approaches that reshape how we understand and deliver rehabilitation across settings and disciplines.

Dr. Christos Kokkotis
Dr. Paraskevi Malliou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • rehabilitation
  • artificial intelligence
  • neurological disorders
  • musculoskeletal therapy
  • sports medicine
  • occupational therapy
  • personalized treatment
  • biomechanical assessment

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Published Papers (2 papers)

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Research

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15 pages, 1148 KB  
Article
Early Prediction of Well-Being Outcomes in Older Adults Using Explainable AI and Emotional Intelligence Measures
by Evgenia Kouli, Evangelos Bebetsos, Maria Michalopoulou and Filippos Filippou
Appl. Sci. 2026, 16(7), 3586; https://doi.org/10.3390/app16073586 - 7 Apr 2026
Viewed by 709
Abstract
Background: Well-being in the elderly is shaped by complex emotional and social factors. Early identification of individuals at risk for reduced well-being may support timely preventive or supportive interventions. This study examined whether emotional intelligence indicators collected at baseline can predict well-being status [...] Read more.
Background: Well-being in the elderly is shaped by complex emotional and social factors. Early identification of individuals at risk for reduced well-being may support timely preventive or supportive interventions. This study examined whether emotional intelligence indicators collected at baseline can predict well-being status 5 months later using explainable machine learning models. Methods: A cohort of elderly participants aged 60 to 89 years completed emotional intelligence measures at baseline, and well-being was assessed 5 months later using the POMS questionnaire. Four machine learning algorithms, Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were developed using 5-fold stratified cross-validation. Model performance was evaluated through accuracy, precision, recall, F1-score, ROC AUC, and normalized confusion matrices. SHapley Additive exPlanations (SHAP) were applied to interpret the contribution and directionality of each predictor. Results: XGBoost achieved the highest predictive performance (accuracy = 0.789; F1 = 0.778) and demonstrated balanced classification across well-being categories. SVM also performed robustly (accuracy = 0.760), while LR showed reduced sensitivity for detecting those with poorer well-being. SHAP analysis identified self-control, emotionality, sociability, self-motivation, and well-being components as the most influential predictors. Lower emotionality, higher sociability, and higher self-control scores were linked to a greater probability of favorable well-being outcomes. Conclusions: The findings demonstrate the feasibility of using explainable machine learning models to predict 5-month well-being status within this sample of older adults using emotional intelligence indicators. XGBoost provided the strongest and most balanced performance, while SHAP analysis clarified how specific emotional intelligence dimensions influenced predictions. These findings suggest that interpretable machine learning approaches may support future efforts toward early recognition of older adults who may be at risk for reduced well-being and guide personalized intervention strategies. Full article
(This article belongs to the Special Issue AI-Driven Innovations in Rehabilitation: Integrating Neurological, Musculoskeletal, Sports Medicine and Occupational Therapy Interventions)
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Review

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13 pages, 1775 KB  
Review
Integrating Physical Activity and Artificial Intelligence in Burn Rehabilitation: Muscle Recovery and Body Image Restoration
by Vasiliki J. Malliou, George Pafis, Christos Katsikas and Spyridon Plakias
Appl. Sci. 2025, 15(15), 8323; https://doi.org/10.3390/app15158323 - 26 Jul 2025
Cited by 1 | Viewed by 3006
Abstract
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, [...] Read more.
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
(This article belongs to the Special Issue AI-Driven Innovations in Rehabilitation: Integrating Neurological, Musculoskeletal, Sports Medicine and Occupational Therapy Interventions)
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