The Use of Robotics and Artificial Intelligence in Neurorehabilitation: From Diagnosis to Treatment

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurorehabilitation".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 1378

Special Issue Editors

Prof. Dr. Rocco Salvatore Calabrò
E-Mail Website
Guest Editor
Department of Engineering and Geology, University of G. d'Annunzio Chieti and Pescara, 65127 Pescara, Italy
Interests: infrared thermography; functional infrared spectroscopy (fNIRS); electroencephalography (EEG); photoplethysmography (PPG); wearable sensors; affective computing; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
IRCCS Fondazione Don Carlo Gnocchi, Via di Scandicci 269, 50143 Firenze, Italy
Interests: robot-aided rehabilitation; assistive robotics and prostheses; neurological diseases; musculoskeletal disorders; postural balance; motor learning; rehabilitation; quality of life; stroke; movement analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Neurorehabilitation is a specialized healthcare field aiding individuals with neurological injuries, employing various therapies to restore motor skills and cognitive function, enhancing their quality of life. In recent years, the use of innovative technologies, such as robotics, has proven effective in the treatment of different neurological disorders, including stroke, traumatic brain and spinal cord injury, multiple sclerosis and Parkinson’s disease. Indeed, better outcomes in terms of gait, balance and upper limb function have been described after robotic-aided training, as compared to conventional rehabilitation.

Artificial intelligence (AI) encompasses computer systems that mimic human cognitive functions, performing tasks like learning, reasoning, problem solving and decision making. The integration of AI in neurorehabilitation holds great promise, but it is crucial to approach this technology with a clear understanding of its capabilities and limitations. AI can enhance assessment, diagnosis and personalized treatment plans, but it should complement, rather than replace, human healthcare providers.

The aim of this Special Issue is to provide researchers and clinicians with indications on the current use of robotic devices as both assessment and rehabilitation tools. Moreover, we aim at demonstrating the growing role of AI in the rehabilitation field.

Papers dealing with robotic-assisted motion analysis and rehabilitation in different neurological disorders are welcomed. Combined advanced approaches and use of AI in predicting outcomes (such as deep and machine learning) or to improve assistive robotics are particularly welcomed.   

Dr. Rocco Salvatore Calabrò
Dr. David Perpetuini
Dr. Irene Aprile
Guest Editors

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Keywords

  • robotic-assisted rehabilitation
  • machine learning
  • neurological disorders
  • artificial intelligence

Published Papers (2 papers)

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18 pages, 1062 KiB  
Article
Improving Outcomes in People with Spinal Cord Injury: Encouraging Results from a Multidisciplinary Advanced Rehabilitation Pathway
Brain Sci. 2024, 14(2), 140; https://doi.org/10.3390/brainsci14020140 - 28 Jan 2024
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Abstract
Spinal cord injury (SCI) consists of damage to any segment of the spinal cord extending to potential harm to nerves in the cauda equina. Rehabilitative efforts for SCI can involve conventional physiotherapy, innovative technologies, as well as cognitive treatment and psychological support. The [...] Read more.
Spinal cord injury (SCI) consists of damage to any segment of the spinal cord extending to potential harm to nerves in the cauda equina. Rehabilitative efforts for SCI can involve conventional physiotherapy, innovative technologies, as well as cognitive treatment and psychological support. The aim of this study is to evaluate the feasibility of a dedicated, multidisciplinary, and integrated intervention path for SCI, encompassing both conventional and technological interventions, while observing their impact on cognitive, motor, and behavioral outcomes and the overall quality of life for individuals with SCI. Forty-two patients with SCI were included in the analysis utilizing electronic recovery system data. The treatment regimen included multidisciplinary rehabilitation approaches, such as traditional physiotherapy sessions, speech therapy, psychological support, robotic devices, advanced cognitive rehabilitation, and other interventions. Pre–post comparisons showed a significant improvement in lower limb function (Fugl Meyer Assessment-FMA < 0.001), global cognitive functioning (Montreal Cognitive Assessment-MoCA p < 0.001), and perceived quality of life at both a physical and mental level (Short Form-12-SF-12 p < 0.001). Furthermore, we found a significant reduction in depressive state (Beck Depression Inventory-BDI p < 0.001). In addition, we assessed patient satisfaction using the Short Form of the Patient Satisfaction Questionnaire (PSQ), offering insights into the subjective evaluation of the intervention. In conclusion, this retrospective study provides positive results in terms of improvements in motor function, cognitive functions, and quality of life, highlighting the importance of exploring multidisciplinary approaches. Full article
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Review
Revealing the Complexity of Fatigue: A Review of the Persistent Challenges and Promises of Artificial Intelligence
Brain Sci. 2024, 14(2), 186; https://doi.org/10.3390/brainsci14020186 - 19 Feb 2024
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Abstract
Part I reviews persistent challenges obstructing progress in understanding complex fatigue’s biology. Difficulties quantifying subjective symptoms, mapping multi-factorial mechanisms, accounting for individual variation, enabling invasive sensing, overcoming research/funding insularity, and more are discussed. Part II explores how emerging artificial intelligence and machine and [...] Read more.
Part I reviews persistent challenges obstructing progress in understanding complex fatigue’s biology. Difficulties quantifying subjective symptoms, mapping multi-factorial mechanisms, accounting for individual variation, enabling invasive sensing, overcoming research/funding insularity, and more are discussed. Part II explores how emerging artificial intelligence and machine and deep learning techniques can help address limitations through pattern recognition of complex physiological signatures as more objective biomarkers, predictive modeling to capture individual differences, consolidation of disjointed findings via data mining, and simulation to explore interventions. Conversational agents like Claude and ChatGPT also have potential to accelerate human fatigue research, but they currently lack capacities for robust autonomous contributions. Envisioned is an innovation timeline where synergistic application of enhanced neuroimaging, biosensors, closed-loop systems, and other advances combined with AI analytics could catalyze transformative progress in elucidating fatigue neural circuitry and treating associated conditions over the coming decades. Full article
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