Mapping the Landscape of Biomechanics Research in Stroke Neurorehabilitation: A Bibliometric Perspective

Abstract

Background/Objectives: The incorporation of biomechanics into stroke neurorehabilitation may serve to strengthen the effectiveness of rehabilitation strategies by increasing our understanding of human movement and recovery processes. The present bibliometric analysis of biomechanics research in stroke neurorehabilitation is conducted with the objectives of identifying influential studies, key trends, and emerging research areas that would inform future research and clinical practice. Methods: A comprehensive bibliometric analysis was performed using documents retrieved from the Scopus database on 6 August 2024. The analysis included performance metrics such as publication counts and citation analysis, as well as science mapping techniques, including co-authorship, bibliographic coupling, co-citation, and keyword co-occurrence analyses. Data visualization tools such as VOSviewer and Power BI were utilized to map the bibliometric networks and trends. Results: An overabundance of recent work has yielded substantial advancements in the application of brain–computer interfaces to electroencephalography and functional neuroimaging during stroke neurorehabilitation., which translate neural activity into control signals for external devices and provide critical insights into the biomechanics of motor recovery by enabling precise tracking and feedback of movement during rehabilitation. A sampling of the most impactful contributors and influential publications identified two leading countries of contribution: the United States and China. Three prominent research topic clusters were also noted: biomechanical evaluation and movement analysis, neurorehabilitation and robotics, and motor recovery and functional rehabilitation. Conclusions: The findings underscore the growing integration of advanced technologies such as robotics, neuroimaging, and virtual reality into neurorehabilitation practices. These innovations are poised to enhance the precision and effectiveness of therapeutic interventions. Future research should focus on the long-term impacts of these technologies and the development of accessible, cost-effective tools for clinical use. The integration of multidisciplinary approaches will be crucial in optimizing patient outcomes and improving the quality of life for stroke survivors.

1. Introduction

Biomechanics is the study of biological systems from a mechanical perspective, integrating principles of physics and engineering to understand the forces, motions, and mechanical properties of living organisms. It is a multidisciplinary field that bridges the gap between biology and mechanics, providing insights into the mechanical aspects of biological functions and structures [1]. Through biomechanics, researchers can analyze the movement and structure of the human body, which is essential for applications in healthcare, sports, and rehabilitation [2,3,4,5].

Neurorehabilitation refers to the medical process that aims to aid recovery from nervous system injuries, particularly those resulting from stroke. It involves a multidisciplinary approach that includes physical therapy, occupational therapy, and cognitive therapy, all designed to enhance neuroplasticity—the brain’s ability to reorganize itself by forming new neural connections. This process is critical for recovering functions that are lost or impaired due to stroke, which often results in significant neurological deficits [6,7,8]. In addition to traditional biomechanical approaches, technologies such as neuroimaging and robotics enhance rehabilitation outcomes by providing precise neural and mechanical data. These tools do not replace biomechanical principles but rather amplify the ability to monitor, analyze, and improve motor function during the rehabilitation process.

Biomechanics offers substantial contributions to the field of stroke neurorehabilitation. By understanding the mechanical principles underlying human movement, biomechanics can aid in the design of more effective rehabilitation protocols and assistive devices. This integration can lead to improved outcomes in motor recovery, as it allows for precise measurement and analysis of movement patterns, muscle forces, and joint mechanics, which are essential for devising personalized rehabilitation strategies [9,10,11,12]. Research in biomechanics applied to neurorehabilitation has shown promising results. Studies have demonstrated that biomechanics-based interventions, such as robotic-assisted therapy and brain–computer interfaces, can significantly enhance motor recovery post-stroke by providing repetitive, task-specific training that promotes neuroplasticity. Neuroimaging techniques such as functional MRI and EEG provide critical, real-time feedback on neural mechanisms driving movement, enabling clinicians to tailor rehabilitation protocols more effectively. By mapping brain activity during rehabilitation, these methods offer crucial data that inform biomechanical analyses of motor performance and recovery trajectories. These approaches are grounded in the principles of motor learning and brain plasticity, emphasizing the importance of high-intensity, goal-oriented, and task-specific practice in rehabilitation [13,14,15,16].

Bibliometric analysis is a quantitative approach used to evaluate and analyze the impact, quality, and trends within the academic literature. It involves the application of statistical methods to various aspects of written publications, such as citation counts, publication counts, and the usage patterns of articles [17]. By examining these metrics, researchers can identify influential papers, prolific authors, and emerging research areas within a field. Bibliometrics is particularly useful for understanding the structure and dynamics of scientific disciplines, aiding in the assessment of research performance, and informing policy decisions related to funding and resource allocation [18,19]. Recent bibliometric analyses in the field of biomechanics and stroke neurorehabilitation reveal significant advancements and research trends, particularly in the use of brain–computer interfaces (BCIs), electroencephalography (EEG), and functional neuroimaging. Robotic systems provide the biomechanical precision needed to measure and assess motor recovery, allowing for detailed analysis of joint mechanics and muscle forces during rehabilitation tasks, which are crucial for optimizing intervention strategies. A previous study [20] highlighted the pivotal role of EEG in BCIs, demonstrating its effectiveness in stroke neurorehabilitation by facilitating motor training and communication for patients. Similarly, other studies [21,22] have underscored the growing interest and research outputs in stroke rehabilitation, identifying the United States and China as leading contributors. Carey and Seitz [23] provided a critical perspective on the potential and limitations of neuroimaging techniques, such as functional MRI, in understanding the neurobiological mechanisms underlying stroke recovery. These studies collectively highlight the dynamic and evolving landscape of research in stroke neurorehabilitation, marked by technological advancements and increasing international collaboration.

This bibliometric study aims to contribute to the growing body of knowledge in biomechanics and neurorehabilitation by mapping key trends and identifying emerging technologies, such as robotics and virtual reality, which are shaping the future of stroke rehabilitation. This has been achieved through a bibliometric analysis, supplemented by a narrative review of the key topics that emerged from this analysis. The value of this approach lies in its potential to serve future researchers by offering a detailed mapping of the current research landscape and identifying trends, influential studies, and gaps that may inform and guide subsequent research efforts. By analyzing publication patterns, citation networks, and research hotspots, we aim to gain insights into the development and impact of biomechanics research in stroke neurorehabilitation, ultimately guiding future research directions and enhancing rehabilitation practices for stroke patients.

2. Materials and Methods

2.1. Procedure

The documents were searched in the Scopus database on 6 August 2024, using the BOOLEAN TITLE-ABS-KEY (“biomechanics” AND “neurorehabilitation” AND “stroke”). This initial search yielded 178 documents. Upon reviewing the titles and abstracts, 12 documents were excluded for either lacking an abstract or not being relevant to stroke. All document types were accepted, including research articles, review articles, books, and editorials, regardless of the language (Supplementary Materials).

The CSV file obtained from Scopus was imported into VOSviewer for bibliometric analysis (Figure 1). VOSviewer (version 1.6.20) is a powerful software tool for constructing and visualizing bibliometric networks. These networks can include journals, researchers, or individual publications and can be created based on citation, bibliographic coupling, co-citation, or co-authorship relations [24]. VOSviewer provides advanced text mining functionality that can be used to construct and visualize co-occurrence networks of important terms extracted from a body of scientific literature. Its intuitive graphical interface makes it easier to interpret complex bibliometric data, making it a popular choice among researchers for bibliometric analysis [25]. Additionally, after converting the file to Excel (xls) format, it was imported into Microsoft Power BI to visualize the documents per year. Power BI is a business intelligence tool designed for visualizing statistical data [26].

2.2. Bibliometric Analysis

Both performance analysis and science mapping were conducted. For performance analysis, the number of documents published per year, the authors with the highest number of citations, and the sources with the most documents were determined. The science mapping techniques used included co-authorship analysis, which examines collaborations based on co-authored documents using countries as the unit of analysis; bibliographic coupling, which assesses the extent to which two or more sources cite the same documents using sources as the unit of analysis; co-citation analysis, which evaluates the frequency with which two or more authors are cited together in other documents using cited authors as the unit of analysis; and co-occurrence analysis, which explores how often two or more keywords appear together in the same documents using author keywords as the unit of analysis.

3. Results of Bibliometric Analysis

3.1. Performance Analysis

Table 1. Top 20 authors in citations.

Author	Documents	Citations	Total Link Strength
Colombo, Roberto	8	750	147
Pisano, Fabrizio	7	639	136
Micera, Silvestro	6	611	102
Delconte, Carmen	5	577	117
Mazzone, Alessandra	5	577	117
Awad, Louis N.	4	548	8
Dario, Paolo	6	547	90
Krebs, Hermano Igo	2	458	6
Bae, Jaehyun	2	448	4
Ellis, Terry D.	2	448	4
Walsh, Conor J.	2	448	4
Kwakkel, Gert	2	416	8
Minuco, Giuseppe	2	342	38
Krakauer, John W.	3	293	22
Lang, Catherine E.	3	292	4
Cohen, Leonardo G.	2	288	0
Sterpi, Irma	5	276	96
Bowden, Mark G.	2	272	1
Kautz, Steven A.	2	272	1
Burdet, Etienne	2	231	21

shows the top 20 authors in citations in the 166 documents, which were finally included in our study. In first place is Roberto Colombo with 750 citations in 8 documents, a prominent researcher known for his work in neurorehabilitation, particularly focusing on robot-assisted therapies.

Table 2. Top 20 sources in documents.

Source	Documents	Citations
Neurorehabilitation and Neural Repair	31	2179
Journal of Neuroengineering and Rehabilitation	19	663
Ieee Transactions on Neural Systems and Rehabilitation Engineering	17	1118
Frontiers in Neurorobotics	4	127
Neurorehabilitation	4	33
Brain Sciences	4	19
Clinical Biomechanics	3	101
Ieee International Conference on Rehabilitation Robotics	3	50
Journal of Rehabilitation Research and Development	3	44
Stroke	2	288
Journal of Neuroscience Methods	2	79
Functional Neurology	2	76
Plos One	2	74
Annals of The New York Academy of Sciences	2	66
Biomed Research International	2	51
Journal of Stroke and Cerebrovascular Diseases	2	38
Frontiers in Neurology	2	35
International Journal of Rehabilitation Research	2	35
Proceedings of the Annual International Conference of the Ieee Engineering in Medicine and Biology Society, EMBS	2	13
Bulletin of Russian State Medical University	2	3

presents a comprehensive overview of the top 20 sources based on the number of documents related to biomechanics and stroke neurorehabilitation. Each source is listed alongside the corresponding number of documents and citations, providing insight into the most prolific journals and publications in this research field. Notably, the table highlights “Neurorehabilitation and Neural Repair” as the leading source with 31 documents and 2179 citations, reflecting its significant impact and contribution to the domain. Other prominent sources include the “Journal of Neuroengineering and Rehabilitation” and “IEEE Transactions on Neural Systems and Rehabilitation Engineering”, which also demonstrate substantial research outputs and influence through high citation counts. This table is crucial for researchers aiming to identify key journals for publishing and referencing significant studies within the biomechanical aspects of stroke neurorehabilitation. Finally, Figure 2 illustrates the count of publications from 2000 to 2024 per year. The x-axis represents the years, while the y-axis shows the count, with intervals of 5, ranging from 0 to 20. Each bar signifies the count for a specific year, with labels on top indicating the exact number. The count was very low at the beginning, with zero or one publication from 2000 to 2004. A noticeable increase started in 2005, with counts ranging from 3 to 10 per year until around 2014. From 2015 onwards, the counts generally rose, peaking in 2020 with 16 publications. After 2020, there was a slight decline, with counts ranging from 4 to 7 publications in the years up to 2024.

3.2. Science Mapping

3.2.1. Co-Authorship Analysis

We performed co-authorship analysis using countries as the unit of analysis. Figure 3 depicts a network map generated through this analysis, visualizing the collaborative relationships between different countries based on co-authored publications in the field of biomechanics and stroke neurorehabilitation. Each node in the network represents a country, with node size proportional to the number of publications and links representing co-authorship relationships. The thickness of the links indicates the strength of collaboration, measured by the number of co-authored publications.

Major contributors such as the United States, Italy, and Spain emerge with larger nodes in the network map, indicating their significant research output, surpassing other countries like China and the UK, which also maintain notable outputs. Additionally, regional clusters, particularly among European countries, suggest that geographical proximity facilitates frequent collaborations, with certain regions like the United States serving as central research hubs due to their well-established institutions and funding. Leading countries are not only prolific in their publication volume but also influential through high-impact research and extensive collaborative networks, which help disseminate new methodologies and technologies globally.

3.2.2. Bibliographic Coupling Analysis

Figure 4 presents a bibliographic coupling analysis using sources as the unit of analysis, offering valuable insights into the citation landscape within the field of biomechanics and stroke neurorehabilitation. The analysis highlights key sources, such as “Neurorehabilitation and Neural Repair”, “Journal of Neuroengineering and Rehabilitation”, and “IEEE Transactions on Neural Systems and Rehabilitation Engineering”, which emerge as central to the research field due to their larger node sizes, indicating significant influence and citation frequency. The figure also reveals clusters of related journals that frequently share references, suggesting the existence of specialized subfields within the broader domain. The thickness of the links between nodes reflects the strength of these relationships, with stronger links indicating a higher degree of shared citations. This visualization not only identifies the most influential sources but also uncovers research trends and interconnections, guiding future research efforts by pinpointing key publications and emerging areas of study.

3.2.3. Co-Citation Analysis

Figure 5 depicts a co-citation analysis focused on authors within the field of biomechanics and stroke neurorehabilitation. The limit was set to at least 40 citations per author. Co-citation analysis is a method used to measure the relationship between authors based on how frequently they are cited together in other academic works. In this figure, each node represents an individual author, and the size of the node is proportional to the number of times that author has been co-cited with others. Larger nodes indicate authors who are frequently co-cited, signifying their prominent influence within the research community. The connections or links between the nodes represent co-citation relationships. The thickness of these links correlates with the strength of the co-citation frequency—thicker links denote stronger associations, implying that the linked authors’ works are often referenced together in the same studies. This can suggest a close relationship in their research themes, methodologies, or contributions to the field. Furthermore, the network may reveal clusters or groups of authors, which typically form around specific subfields or schools of thought within the broader domain. These clusters highlight how certain groups of authors are interconnected through shared citations, potentially indicating collaborative networks, intellectual lineage, or common research interests.

3.2.4. Clustering

From the co-occurrence analysis of author keywords in publications of stroke neurorehabilitation biomechanics, three clusters emerged. Only keywords that appeared at least two times were taken into account. The keyword clusters in Figure 6 were generated using VOSviewer’s co-occurrence analysis, which groups terms based on their frequency and relationship across the dataset. The three clusters were defined algorithmically, and the terms assigned to each cluster reflect their contextual use within the literature. Cluster 1 encompasses terms related to the measurement and evaluation of human movement and biomechanical functions. The terms grouped within this cluster focus on the tools, techniques, and technologies used to understand movement patterns, joint mechanics, and muscle activity. Cluster 2 is more centered on the technological aspects of rehabilitation, including robotics and assistive devices, while Cluster 3 emphasizes motor recovery strategies, encompassing broader therapeutic approaches like physiotherapy and task-specific training. Figure 6 presents a classification of key topics into three distinct clusters based on thematic relevance, accompanied by the number of items in each cluster:

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Cluster 1: Biomechanical Assessment and Movement Analysis (34 items). This cluster encompasses a wide range of topics related to the assessment and analysis of human biomechanics and movement. It includes terms such as “3D kinematics”, “biomechanics”, “gait analysis”, and “electromyography”, indicating a focus on the detailed measurement and evaluation of physical movement. Other terms like “motor control”, “plasticity”, “virtual reality”, and “machine learning” suggest the application of advanced techniques and technologies in understanding and improving human movement and rehabilitation.

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Cluster 2: Neurorehabilitation and Robotics (25 items). The second cluster is centered on the intersection of neurorehabilitation and robotic technologies. It includes terms like “exoskeleton”, “rehabilitation robotics”, “robot-assisted therapy”, and “brain injury”, indicating a strong emphasis on using robotics to support recovery from neurological conditions. Additional terms such as “motor recovery”, “movement biomechanics”, and “neurorehabilitation” suggest a focus on the rehabilitation of motor functions, particularly in individuals with neurological impairments.

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Cluster 3: Motor Recovery and Functional Rehabilitation (12 items). The third cluster focuses on topics related to the recovery of motor function and functional rehabilitation. It includes terms like “constraint-induced movement therapy”, “physiotherapy”, “recovery”, and “upper limb”, which point to therapeutic approaches aimed at restoring movement and function in patients. This cluster highlights the application of various rehabilitation techniques to improve motor function, particularly in the context of upper extremity recovery and rehabilitation.

Figure 7 depicts a network of keywords, where each node represents a keyword, and the size of the node reflects the frequency of its occurrence. Links between nodes indicate co-occurrence, meaning the keywords were used together in the same documents. The thickness of these links suggests the strength of their relationship (how often they appear together). The figure helps identify prominent topics and emerging trends in the research on biomechanics and stroke neurorehabilitation, showcasing clusters or groups of related keywords that highlight specific areas of focus within the broader field.

Figure 6. Co-occurrence analysis of author keywords, grouped into three distinct clusters.

Figure 6. Co-occurrence analysis of author keywords, grouped into three distinct clusters.

4. Narrative Review

The sections of the narrative review were structured based on the clusters identified through the co-occurrence analysis of the author keywords. This analysis allowed us to categorize the literature into distinct thematic areas, which then guided the organization of the review. By following the natural groupings of research topics, the review effectively captures the key focus areas within the field, ensuring a comprehensive and systematic exploration of the relevant literature.

4.1. Narrative Review on Biomechanical Assessment and Movement Analysis

Biomechanical assessment and movement analysis are crucial in understanding human motor functions, particularly in clinical settings where these evaluations help in diagnosing and treating conditions related to movement impairments [27]. While robotic systems play a role in neurorehabilitation, this section focuses specifically on the biomechanical assessment of movement. Tools such as 3D motion capture and electromyography are central to evaluating movement patterns and motor function, providing key insights into recovery trajectories. With advancements in technology, the methods of assessing biomechanics and analyzing movement have significantly evolved, providing deeper insights into both normal and pathological motion.

Li et al. [22] highlighted the growing importance of robotic assistance in rehabilitative processes, particularly for stroke patients. Their work emphasizes the quantification of assistance levels in human–robot interactions, a critical component in the biomechanical assessment of stroke rehabilitation. The study demonstrates how technology can enhance the precision of movement assessments, ensuring that rehabilitation efforts are more targeted and effective. This contributes to biomechanics by providing a quantitative approach to assistance in rehabilitation, advancing methods for individualized therapy. The research underpins the article’s focus on technology-driven biomechanical methods in stroke neurorehabilitation. Their approach to tailored robotic assistance aligns with this study’s emphasis on individualized, data-driven therapy. Massie et al. [28] focused on a clinically relevant method for analyzing continuous movement. Their research delved into the nuances of robot-assisted therapy, presenting a methodology that incorporates real-time biomechanical data to assess the efficacy of therapeutic interventions. They developed a continuous movement analysis approach within robotic-assisted therapy, focusing on methods that provide real-time feedback and adaptability during rehabilitation. Their clinical methodology was designed to dynamically track movement changes throughout therapy sessions, offering a responsive assessment of rehabilitation efficacy. The study highlighted the value of continuous, real-time data for adapting therapy in situ, thus enhancing patient outcomes. This continuous assessment is a breakthrough in clinical biomechanics, as it supports adaptive treatment plans that evolve with patient progress. Massie et al.’s work supports the current article’s exploration of real-time biomechanics analysis, providing a basis for adaptive neurorehabilitation. Their approach to integrating immediate feedback into biomechanics exemplifies the data-driven adjustments advocated in this study. The integration of neuroengineering and rehabilitation robotics is exemplified in the work of Kim et al. [29], who investigated motor cortex activation during grasp tasks. Kim et al. focused on linking neuroimaging data with biomechanical analysis to understand motor cortex activation during grasping tasks. Utilizing functional neuroimaging, they examined how specific motor areas activate in coordination with physical movement, providing insights into neural control of motor functions. The findings demonstrate that the targeted activation of motor cortical areas can be monitored in real time and correlated with specific motor tasks. This study contributes to biomechanics by revealing how neural data can refine motor control assessment, enhancing the precision of neurorehabilitation protocols. Kim et al.’s work directly supports this article’s emphasis on incorporating neurofeedback into biomechanical analysis, showing how neuroimaging can enhance our understanding of motor control and contribute to the design of targeted neurorehabilitation strategies. Nie et al. [30] explored portable, open-source solutions for estimating arm movement kinematics, which are crucial in movement analysis. The study revealed that open-source tools could reliably measure arm kinematics, making detailed biomechanical assessments more accessible for clinicians. This portable approach democratizes access to movement analysis technology, potentially transforming biomechanical applications in diverse healthcare settings. Nie et al.’s work aligns with the article’s focus on accessible biomechanics tools for widespread clinical use, underscoring the importance of portable, cost-effective technology in neurorehabilitation. Their work is particularly significant in developing accessible tools for clinicians and researchers, enabling detailed biomechanical assessments in various environments. The portability of these tools allows for broader application in both clinical and research settings, making biomechanical analysis more accessible. Dos Santos et al. [31] focused on a rehabilitative intervention, specifically assessing how elastic tape application influenced the kinematic parameters of stroke patients during movement, highlighting its potential as a therapeutic tool to improve motor function. The study utilized controlled movement tasks to observe the impact of tape on joint mechanics and muscle function. Their findings suggest that elastic tape can positively influence movement patterns by improving stability and control in patients with motor impairments. This study adds to the biomechanical field by offering an accessible, non-invasive intervention to support motor recovery. These findings reinforce the article’s focus on task-specific biomechanical interventions in neurorehabilitation, highlighting how simple tools can contribute to functional recovery and enhance biomechanics-based therapy. This research adds to the body of evidence supporting the use of biomechanical assessments to fine-tune rehabilitation strategies, ensuring that interventions are both effective and tailored to the individual needs of patients. As biomechanical assessment and movement analysis continue to advance, future research should focus on integrating these technologies into routine clinical practice. This will require not only technological innovation but also the development of standardized protocols to ensure consistency and accuracy across different settings.

Biomechanical assessment and movement analysis are indispensable tools in modern clinical practice, particularly in rehabilitation. The integration of advanced technologies such as robotics, neuroimaging, and portable kinematic tools has significantly enhanced the precision and applicability of these assessments. As research continues to evolve, these methods will likely become even more integral to personalized medicine, offering new ways to improve patient outcomes through detailed and dynamic analysis of movement.

4.2. Narrative Review on Neurorehabilitation and Robotics

The field of neurorehabilitation has been revolutionized by the integration of robotics, which offers precise, customizable interventions that significantly enhance the recovery of motor functions. These innovations are poised to significantly enhance the recovery of motor functions and improve the overall quality of life for stroke survivors [16,32] This review explores the contributions of various researchers in advancing the understanding and application of robotics in neurorehabilitation, emphasizing the importance of these technologies in clinical settings.

Ballester et al. [33] focus on the application of robotics in post-stroke rehabilitation, highlighting how robotic devices can facilitate a wide range of motor exercises that are crucial for recovery. Their study demonstrates the versatility of robotic systems in providing tailored rehabilitation programs, which can be adjusted to meet the specific needs of each patient. Their study employed various robotic devices tailored to perform repetitive movements and adapt to the patient’s specific rehabilitation needs. This research showed that robotic devices could efficiently deliver adaptive exercises, which are crucial for personalized rehabilitation. This study highlights the versatility of robotics, suggesting that adaptable robotic support could significantly enhance motor recovery by tailoring exercises to each patient’s progress. Ballester et al.’s work supports this article’s exploration of adaptive, data-driven robotic interventions, reinforcing the idea that robotic systems can personalize recovery protocols for stroke patients, aligning well with the goals of neurorehabilitation. This adaptability is key in addressing the varied and complex nature of stroke recovery, where patient-specific interventions are often required. Pogrzeba et al. [34] discussed the objective assessment capabilities provided by robotics in long-term rehabilitation. Their methodology included implementing robotic systems capable of real-time monitoring and feedback, allowing precise tracking of patient progress. Their findings indicated that robotic systems’ capacity for ongoing, real-time assessment enables clinicians to adjust therapeutic strategies according to the patient’s evolving needs. This feature makes robotics invaluable for data-driven, iterative therapy adjustments. This study underpins the current article’s focus on continuous assessment and feedback in neurorehabilitation, demonstrating how robotics can optimize therapy plans based on real-time data, aligning with personalized rehabilitation objectives. Their research underscores the importance of continuous, data-driven assessments in neurorehabilitation, which are made possible through the integration of robotics. These systems offer real-time feedback and monitoring, allowing for more accurate tracking of patient progress and the adjustment of therapeutic strategies over time.

Kudva et al. [35] explored the relationship between the central nervous system (CNS) and robotic interventions. Their research delved into how robotic systems can be used to stimulate CNS responses, thereby enhancing neuroplasticity and facilitating recovery. Their approach combined robotic-assisted exercises with techniques to stimulate CNS responses, examining how robotics could promote neurological recovery. The study found that robotic interventions could successfully engage the CNS, contributing to neuroplasticity and, thus, functional recovery. This work is pivotal for neurorehabilitation as it bridges physical rehabilitation with neurological stimulation, enhancing the recovery process. Kudva et al.’s findings highlighted the neuroplastic potential of robotic systems, supporting this article’s emphasis on the importance of CNS engagement in neurorehabilitation through technology. Costa et al. [36] focused on the practical aspects of integrating robotic systems into clinical practice. Their work involves the development and testing of an interdisciplinary approach to neurorehabilitation that incorporates robotics as a core component. They developed an interdisciplinary framework that embedded robotics into routine therapy, examining its impact on clinical outcomes and therapy efficacy. This study demonstrated that robotics could seamlessly fit within existing rehabilitation protocols, enhancing therapy efficacy and ensuring efficient, standardized care. The research underscores robotics’ role in formalizing treatment regimens within neurorehabilitation practices. Costa et al.’s work aligns with the article’s focus on accessible and standardized robotic applications, providing a foundation for incorporating robotics into practical, everyday clinical settings. Kantan et al. [37] investigated the use of auditory feedback in robotic therapy, an innovative approach that complements traditional visual and tactile feedback mechanisms. Their research shows that auditory cues can enhance the effectiveness of robotic-assisted rehabilitation by providing additional sensory inputs that help patients better understand and control their movements. Their study explored how sensory cues could improve patient engagement and control during robotic-assisted exercises. Findings revealed that auditory feedback could significantly enhance patients’ understanding and control of movements during therapy, offering a multi-sensory approach to rehabilitation. This novel approach showcases the potential of robotics to engage patients more effectively in their recovery process. Kantan et al.’s research on auditory feedback in robotic therapy enriches this article’s discussion on multi-sensory approaches in neurorehabilitation, illustrating how additional sensory inputs can improve patient outcomes and engagement. This approach represents a novel way to engage patients in their rehabilitation, potentially improving outcomes.

The studies reviewed here suggest that robotics is poised to play an increasingly central role in neurorehabilitation. Future research should focus on further refining these technologies, particularly in developing more intuitive and accessible systems that can be easily integrated into various clinical environments. Additionally, long-term studies are needed to evaluate the sustained benefits of these robotic interventions and to explore their potential in treating a wider range of neurological conditions. The integration of robotics into neurorehabilitation has opened new frontiers in the treatment of neurological disorders. The research reviewed demonstrates the significant advancements made in this field, particularly in the areas of stroke rehabilitation, CNS engagement, and the practical application of robotic systems in clinical settings. As these technologies continue to evolve, they are likely to become indispensable tools in the recovery of motor functions, offering new hope to patients with neurological impairments.

4.3. Narrative Review on Motor Recovery and Functional Rehabilitation

Motor recovery and functional rehabilitation are critical components in the treatment of individuals who have suffered from neurological impairments, such as stroke or spinal cord injuries. These processes aim to restore motor functions and improve the quality of life for patients through various therapeutic approaches, including traditional rehabilitation exercises, robotic assistance, and innovative training methods. This review summarizes recent research contributions to motor recovery and functional rehabilitation, providing insights into the latest advancements and their clinical implications.

Casas et al. [38] focused on robotic training, specifically addressing its role in motor recovery and rehabilitation through the use of robotic-assisted systems. They presented a study on the development of a passive and lightweight wearable device designed to assist with motor recovery in patients undergoing rehabilitation. Their research emphasizes the potential of wearable technologies in enhancing motor function by providing continuous support and feedback during movement exercises. This approach provides continuous support while allowing users to move independently, making rehabilitation more accessible and effective. The study demonstrated that wearable technology could facilitate continuous motor function improvement, highlighting wearable devices’ practicality and impact on daily rehabilitation routines. This is a significant contribution to functional rehabilitation as it extends therapy beyond the clinical environment. Casas et al.’s work supports this article’s emphasis on accessible and functional recovery tools, aligning with the study’s focus on enhancing at-home rehabilitation options for motor recovery. Bonanno and Calabrò [39] explored the role of robot-aided rehabilitation in functional recovery, particularly focusing on its application in neurorehabilitation. Their study highlighted the efficacy of robotic systems in providing repetitive and precise movements that are essential for motor recovery. Their findings indicated that robotic rehabilitation allows for highly targeted, task-specific therapy that enhances motor recovery, making it an effective strategy for refining motor skills in stroke recovery. The study’s insights contribute to functional rehabilitation by emphasizing the need for controlled, repetitive exercise. Bonanno and Calabrò’s research reinforced this article’s focus on precision in motor rehabilitation through robotics, supporting this study’s exploration of robotics as a means of delivering targeted, high-frequency therapy. The controlled environment offered by these robotic systems allows for the fine-tuning of rehabilitation exercises, which is crucial for optimizing functional outcomes in patients. Levin et al. [40] presented a study protocol for a randomized controlled trial that focused on sensorimotor recovery of the upper limb in individuals with spastic hemiparesis. The study investigated the effects of personalized upper limb training combined with anodal transcranial direct current stimulation (tDCS). They emphasized the importance of targeted rehabilitation exercises that promote neuroplasticity, which is the brain’s ability to reorganize itself and form new neural connections. This method aims to maximize neuroplasticity through targeted exercises that promote neural reorganization. The study showed that combining tDCS with upper limb training enhances sensorimotor recovery, demonstrating the synergistic potential of pairing physical exercises with neural stimulation. This work contributes to functional rehabilitation by advancing neuroplasticity-driven therapy approaches. This study’s findings align with the article’s focus on neuroplasticity as a driver for functional recovery, illustrating how combined methodologies can boost motor rehabilitation outcomes through innovative therapies.

Demers and Levin [41] examined the use of virtual reality (VR) in functional rehabilitation, particularly in motor recovery. By analyzing movement trajectories and other kinematic parameters, the researchers assessed whether the 2D virtual environment can be used effectively to support motor recovery after a stroke. The aim was to determine if virtual environments can serve as valid and reliable tools in rehabilitation protocols, offering patients more engaging and accessible methods for improving arm function. Their study demonstrated that VR can be a powerful tool in rehabilitation, offering immersive and engaging environments that motivate patients to perform repetitive exercises. The use of VR in rehabilitation not only enhances patient engagement but also allows for the simulation of real-life scenarios, which is crucial for functional recovery. The study by Demers and Levin supports this article’s interest in VR for functional rehabilitation, providing a basis for integrating immersive environments that enhance motivation and engagement in repetitive motor tasks. Wang et al. [42] explored the benefits of backward walking training (BWT) as a method for improving motor recovery. The study aims to compare outcomes between patients receiving traditional rehabilitation therapy and those undergoing robot-assisted therapy, focusing on how these methods impact both physical and cognitive recovery. This research is important because it explores the potential for technology to enhance the effectiveness of stroke rehabilitation, possibly leading to improved outcomes for patients. This training method offers an example of novel approaches to rehabilitation, focusing on the restoration of motor functions that are critical for everyday activities. Wang et al.’s research complements this article’s exploration of diverse motor rehabilitation techniques, emphasizing the multifaceted benefits of innovative approaches like backward walking.

The research reviewed here highlights the diverse approaches to motor recovery and functional rehabilitation, ranging from wearable technologies and robotic assistance to virtual reality and innovative training methods. These advancements are shaping the future of rehabilitation, offering new hope to patients with motor impairments. As the field continues to evolve, integrating these technologies into clinical practice will be essential for maximizing recovery outcomes and improving the quality of life for patients.

4.4. Summary of the Results

The results of this bibliometric analysis and narrative review highlight several emerging trends and key findings in the field of biomechanics and neurorehabilitation. The primary focus has been on the biomechanical assessment of movement in stroke rehabilitation, with an emphasis on tools like gait analysis, electromyography (EMG), and kinematic assessments. These tools are crucial in evaluating movement patterns, muscle activity, and joint mechanics, providing valuable insights for personalized rehabilitation strategies.

The reviewed articles consistently demonstrate that movement analysis techniques such as 3D motion capture systems and force platforms are pivotal in tracking motor recovery in stroke patients. These systems allow for the precise measurement of joint angles, gait deviations, and muscle activation patterns. Studies show the efficacy of using kinematics to monitor gait patterns post-stroke, emphasizing its role in adjusting rehabilitation protocols to target specific deficits in movement. Additionally, EMG has been widely adopted to assess muscle function, offering clinicians the ability to identify abnormalities in muscle activation during walking or limb movements, contributing to more tailored rehabilitation approaches.

While this section focuses on biomechanical assessment, a recurring theme in the reviewed literature is the integration of robotic assistance into neurorehabilitation. Robotic systems are highlighted for their contribution to delivering repetitive, task-specific training, which is crucial for promoting neuroplasticity and motor learning. Although robotics primarily fits into movement rehabilitation, they also provide key data for biomechanical analysis, as they generate precise, quantifiable metrics of joint motion, force application, and muscle strength during tasks. To clarify, while the primary focus of this section is on the biomechanical assessment of movement, robotic systems are increasingly used in clinical settings to support this assessment. These devices contribute by offering highly controlled environments where patients can engage in repetitive, biomechanically relevant exercises, allowing for detailed measurement and analysis of movement patterns.

Several reviewed studies also integrate neuroimaging techniques, such as functional MRI and EEG, into biomechanical assessments. Neuroimaging offers insights into the neural mechanisms underlying motor function, helping to link brain activity with biomechanical outputs. For instance, EEG-based studies have provided data on how neural activity during movement can be mapped to biomechanical variables like muscle activation and joint movement. These findings enhance our understanding of motor recovery by combining neural feedback with biomechanical data.

Several articles have highlighted the growing importance of virtual reality (VR) and wearable technologies in biomechanics and rehabilitation. These technologies provide immersive environments for task-specific motor training and offer real-time feedback for both clinicians and patients. The future of biomechanics in stroke rehabilitation appears to lie in the integration of advanced monitoring systems, such as wearables and VR, with traditional biomechanical assessments, enabling a more comprehensive and personalized approach to recovery.

5. Discussion

5.1. Bibliometric Analysis

From the calculation of publications per year, it is evident that the number of publications has stabilized in double digits since 2016. This can be attributed to the progress in technology used in biomechanics as well as advancements in research methods [43,44]. However, in the last two years (2022–2023), there has been a decline in publications. This trend should be examined in future bibliometric analyses to determine whether this decline is temporary or if it will stabilize, which would lead to an investigation of potential causes. For example, it could be due to reduced funding or the fact that some key research questions have already been answered, leading to a decrease in the rate of new publications as researchers turn to other emerging areas in neurorehabilitation.

Additionally, Figure 4, which depicts bibliographic coupling with sources as the unit of analysis, combined with Table 2, which presents the top 20 sources in terms of publications and citations, provides valuable insights into the scientific activity and the most influential sources in the field of neurorehabilitation and physical therapy. From the figure, we observe the formation of distinct clusters, which indicate thematic groups of journals that share common references and likely focus on similar research topics. For instance, the blue cluster appears to be more associated with technological applications and engineering in rehabilitation, while other clusters seem to pertain to more clinically oriented journals. This demonstrates the interdisciplinary nature of research in neurorehabilitation, with sources ranging from clinical applications to innovative technological solutions.

Finally, it is worth noting the collaboration that seems to exist between different countries, as indicated by the co-authorship analysis performed using countries as the unit of analysis. International cooperation in research can provide both direct benefits to the research and indirect strategic, economic, or political benefits [45,46]

5.2. Clinical Implications and Future Perspectives

The findings of this bibliometric analysis and the review of current research in biomechanics and stroke neurorehabilitation have significant clinical implications. As the field continues to evolve, the integration of advanced technologies such as robotics, neuroimaging, and virtual reality into clinical practice is expected to transform rehabilitation strategies, making them more personalized and effective. The evidence suggests that these innovations not only enhance motor recovery but also improve the overall quality of life for patients with neurological impairments.

The integration of biomechanics into stroke neurorehabilitation allows for the development of more targeted and effective rehabilitation protocols. By leveraging biomechanical assessments and movement analysis, clinicians can design personalized interventions that address the specific needs of each patient, leading to better recovery outcomes [47,48]. The use of robotic systems in neurorehabilitation offers precise and customizable interventions that can significantly enhance motor recovery. These systems provide continuous, real-time feedback, allowing for the fine-tuning of therapeutic exercises and the monitoring of patient progress. As robotics technology advances, its application in clinical settings is likely to become more widespread, offering new avenues for treating various neurological conditions. Advanced neuroimaging techniques, such as functional MRI, are increasingly being used to understand the neural mechanisms underlying stroke recovery. These insights can guide the development of more effective rehabilitation strategies, particularly in enhancing neuroplasticity and motor control. Incorporating neuroimaging into routine clinical practice could help clinicians better assess and monitor the progress of neurorehabilitation. VR-based rehabilitation programs provide immersive environments that can motivate patients and simulate real-life scenarios, which are crucial for functional recovery. The use of VR in rehabilitation is a promising approach that could improve patient engagement and outcomes, particularly in the recovery of voluntary movements and daily functioning.

Future research should focus on the continued integration of biomechanics, robotics, neuroimaging, and virtual reality into a cohesive neurorehabilitation strategy. This multidisciplinary approach has the potential to offer more comprehensive and effective rehabilitation programs that cater to the diverse needs of stroke patients. As these technologies advance, there is a growing need to make them more accessible and affordable for clinical use. Research and development should prioritize creating cost-effective, user-friendly devices and systems that can be easily implemented in various healthcare settings, including remote and underserved areas. While the short-term benefits of these technologies are evident, long-term studies are needed to evaluate their sustained impact on patient outcomes. Understanding the long-term efficacy of robotics, VR, and other advanced rehabilitation tools will be crucial in establishing their role in standard clinical practice. The future of neurorehabilitation lies in personalized medicine, where treatments are tailored to the individual characteristics of each patient, including their biomechanics, neural activity, and specific rehabilitation needs. Advances in data analytics, machine learning, and artificial intelligence could play a key role in developing personalized rehabilitation protocols that optimize recovery.

5.3. Strengths and Limitations of the Study

The study provides a thorough bibliometric analysis of the field, covering a wide range of publications and identifying key trends, influential researchers, and leading journals. This comprehensive approach offers valuable insights into the evolution of research in biomechanics and stroke neurorehabilitation. By integrating various disciplines such as biomechanics, robotics, neuroimaging, and rehabilitation, the study highlights the importance of a holistic approach to stroke recovery. This multidisciplinary perspective enhances the understanding of the complex interactions involved in neurorehabilitation.

This study successfully identifies emerging trends and research hotspots within the field. This forward-looking analysis helps guide future research directions, ensuring that upcoming studies are aligned with the most promising and impactful areas of investigation. The use of advanced visualization tools like VOSviewer and Power BI to map bibliometric networks and trends provides a clear and accessible representation of the data. These visualizations make complex bibliometric data more understandable and actionable for researchers. The study emphasizes the clinical implications of its findings, making the research directly relevant to practitioners in the field of neurorehabilitation.

However, this study has some limitations. The bibliometric analysis is based solely on documents retrieved from the Scopus database, which may result in the exclusion of relevant publications from other databases such as PubMed or Web of Science, potentially leading to an incomplete picture of the research landscape. While this study includes all document types regardless of language, there may still be a bias toward English-language publications, possibly overlooking important contributions from non-English-speaking regions. Furthermore, although the study provides an extensive overview of research in biomechanics and stroke neurorehabilitation, the findings may not be fully generalizable to other areas of neurorehabilitation or biomechanics.

6. Conclusions

This study contributes to the state of the art in biomechanics and neurorehabilitation by providing a comprehensive bibliometric analysis that identifies influential studies, key trends, and emerging research areas in stroke rehabilitation. It advances the understanding of how biomechanical assessments, such as gait analysis and EMG, are being increasingly integrated with technologies like robotics, neuroimaging, and virtual reality to enhance the precision and effectiveness of rehabilitation strategies. Furthermore, this study highlights future directions, including the need for more accessible, cost-effective tools and the potential for personalized rehabilitation protocols that optimize patient outcomes. By mapping the current research landscape, this work informs both future research initiatives and the development of more targeted clinical interventions.

As the field advances, the integration of multidisciplinary approaches will be crucial in designing personalized rehabilitation protocols that optimize patient outcomes and contribute to the overall improvement of quality of life for individuals recovering from neurological impairments.