Sensor-Based Rehabilitation in Neurological Diseases: A Bibliometric Analysis of Research Trends

Advancing stroke rehabilitation: the role of wearable technology according to research experts

BACKGROUND

Advancements in wearable technology have created new opportunities to monitor stroke survivors' behaviors in daily activities. Research insights are needed to guide its adoption in clinical practice, address current gaps, and shape the future of stroke rehabilitation. This project aims to: (1) Understand stroke rehabilitation researchers' perspectives on the opportunities, challenges, and clinical relevance of wearable technology for stroke rehabilitation, and (2) Identify necessary next steps to integrate wearable technology in research and clinical practice.

METHODS

Using a phenomenological qualitative design, two 90-minute focus groups were conducted with 12 rehabilitation researchers. The focus groups consisted of semi-structured, open-ended questions on functional movement behavior, motor performance and benefits and pitfalls of wearable technology. The transcribed focus groups were analyzed using inductive thematic analysis.

RESULTS

Three main themes were derived from the analysis: (1) Assessing activity performance is critical to inform interventions, (2) The demonstrated benefit is not commensurate with the added hassle, (3) Collaboration is needed between the industry, academia and end-users. Necessary future steps were recognized including the identification of intuitive and actionable metrics, and the integration of sensor-derived data with electronic health records and into clinical workflow to support self-management strategies.

CONCLUSION

Wearable technology shows great potential to complement and support stroke rehabilitation. Many key barriers to clinical adoption remain which underscore the necessity to foster collaborations between industry, academia, and the participants we serve.

Muscle synergies in upper limb stroke rehabilitation: a scoping review

INTRODUCTION

Upper limb impairment is a common consequence of stroke, significantly affecting the quality of life and independence of survivors. This scoping review assesses the emerging field of muscle synergy analysis in enhancing upper limb rehabilitation, focusing on the comparison of various methodologies and their outcomes. It aims to standardize these approaches to improve the effectiveness of rehabilitation interventions and drive future research in the domain.

EVIDENCE ACQUISITION

Studies included in this scoping review focused on the analysis of muscle synergies during longitudinal rehabilitation of stroke survivors' upper limbs. A systematic literature search was conducted using PubMed, Scopus, and Web of Science databases, until September 2023, and was guided by the PRISMA for scoping review framework.

EVIDENCE SYNTHESIS

Fourteen studies involving a total of 247 stroke patients were reviewed, featuring varied patient populations and rehabilitative interventions. Protocols differed among studies, with some utilizing robotic assistance and others relying on traditional therapy methods. Muscle synergy extraction was predominantly conducted using Non-Negative Matrix Factorization from electromyography data, focusing on key upper limb muscles essential for shoulder, elbow, and wrist rehabilitation. A notable observation across the studies was the heterogeneity in findings, particularly in the changes observed in the number, weightings, and temporal coefficients of muscle synergies. The studies indicated varied and complex relationships between muscle synergy variations and clinical outcomes. This diversity underscored the complexity involved in interpreting muscle coordination in the stroke population. The variability in results was also influenced by differing methodologies in muscle synergy analysis, highlighting a need for more standardized approaches to improve future research comparability and consistency.

CONCLUSIONS

The synthesis of evidence presented in this scoping review highlights the promising role of muscle synergy analysis as an indicator of motor control recovery in stroke rehabilitation. By offering a comprehensive overview of the current state of research and advocating for harmonized methodological practices in future longitudinal studies, this scoping review aspires to advance the field of upper limb rehabilitation, ensuring that post-stroke interventions are both scientifically grounded and optimally beneficial for patients.

Approaches of wearable and implantable biosensor towards of developing in precision medicine

In the relentless pursuit of precision medicine, the intersection of cutting-edge technology and healthcare has given rise to a transformative era. At the forefront of this revolution stands the burgeoning field of wearable and implantable biosensors, promising a paradigm shift in how we monitor, analyze, and tailor medical interventions. As these miniature marvels seamlessly integrate with the human body, they weave a tapestry of real-time health data, offering unprecedented insights into individual physiological landscapes. This log embarks on a journey into the realm of wearable and implantable biosensors, where the convergence of biology and technology heralds a new dawn in personalized healthcare. Here, we explore the intricate web of innovations, challenges, and the immense potential these bioelectronics sentinels hold in sculpting the future of precision medicine.

Hotspot Analysis and Frontier Exploration of Stem Cell Research in Intervertebral Disc Regeneration and Repair: A Bibliometric and Visualization Study

BACKGROUND

Stem cells have shown tremendous potential and vast prospects in the research of intervertebral disc (IVD) regeneration and repair, attracting considerable attention in recent years. In this study, a bibliometric analysis and visualization techniques were employed to probe and analyze the hotspots and frontiers of stem cell research in IVD regeneration and repair, aiming to provide valuable references and insights for further investigations.

METHODS

This study utilized the Science Citation Index Expanded from the Web of Science Core Collection database to retrieve and extract relevant literature records as research samples. Visual analysis tools such as VOSviewer 1.6.19, CiteSpace 6.2.R4, and bibliometric online analysis platforms were employed to construct scientific knowledge maps, providing a comprehensive and systematic exposition from various perspectives including collaboration networks, cocitation networks, and co-occurrence networks.

RESULTS

A total of 1075 relevant studies have been published in 303 journals by 4181 authors from 1198 institutions across 54 countries/regions. Over the past 20 years, the field of research has witnessed a significant growth in annual publications and citations. China and the United States have emerged as the primary participants and contributors, with the AO Research Institute Davos, Zhejiang University, and Tokai University being the top 3 leading research institutions. The most productive and highly cited author is Sakai D, who is regarded as a key leader in this research field. The journals with the highest number of publications and citations are Spine and Biomaterials, which are considered to be high-quality and authoritative core journals in this field. The current research focuses primarily on the sources and selection of stem cells, optimization of transplantation strategies, mechanisms of IVD regeneration, and the combined application of stem cells and biomaterials. However, there are still some challenges that need to be addressed, including posttransplantation stability, assessment of regenerative effects, and translation into clinical applications. Future research will concentrate on the diversity of stem cell sources, the application of novel biomaterials, personalized treatments, and the development of gene editing technologies, among other cutting-edge directions.

CONCLUSIONS

This study utilized bibliometric analysis and visualization techniques to unveil the hotspots and frontiers in the research on stem cells for IVD regeneration and repair. These research findings provide essential guidance and references for further experimental design and clinical applications. However, additional experiments and clinical studies are still needed to address the challenges and difficulties faced in the field of IVD regeneration and repair, thus offering novel strategies and approaches for the treatment of IVD diseases.

Two Decades' advancements and Research trends in needle-type Sensor technology: A scientometric analysis

Needle-type sensor, characterized by its slender, elongated shape, is a promising sensing method due to its rapid response, high sensitivity, and portability. Recently, the needle-type sensor technology has garnered increasing attention, leading to its accelerated development and extensive use in medical and healthcare, environmental monitoring, and geosciences. However, there remains a need for a comprehensive review of existing research. Here, we utilize scientometric analysis, which is booming recently, to conduct a comprehensive investigation of the needle-type sensor field. This analysis covers various aspects, including annual trends, journals, institutions, countries, disciplines, authors, references, and keywords of 136,667 publications from the Web of Science Core Collection (WoSCC) database spanning from January 1, 2004, to January 1, 2024. Additionally, we identify current hotspots, frontiers, and predict future trends. Eventually, three research hotspots are refined: multidisciplinary materials science, sensor miniaturization and integration, and biomedical engineering, indicating that further investigations may focus on creating biocompatible materials to enhance sensing properties, optimizing sensor structure through miniaturization and integration methods, and improving clinical applications in biomedical engineering. This work may facilitate the development of needle-type sensors.

The Role and Importance of Using Sensor-Based Devices in Medical Rehabilitation: A Literature Review on the New Therapeutic Approaches

Due to the growth of sensor technology, more affordable integrated circuits, and connectivity technologies, the usage of wearable equipment and sensing devices for monitoring physical activities, whether for wellness, sports monitoring, or medical rehabilitation, has exploded. The current literature review was performed between October 2022 and February 2023 using PubMed, Web of Science, and Scopus in accordance with P.R.I.S.M.A. criteria. The screening phase resulted in the exclusion of 69 articles that did not fit the themes developed in all subchapters of the study, 41 articles that dealt exclusively with rehabilitation and orthopaedics, 28 articles whose abstracts were not visible, and 10 articles that dealt exclusively with other sensor-based devices and not medical ones; the inclusion phase resulted in the inclusion of 111 articles. Patients who utilise sensor-based devices have several advantages due to rehabilitating a missing component, which marks the accomplishment of a fundamental goal within the rehabilitation program. As technology moves faster and faster forward, the field of medical rehabilitation has to adapt to the time we live in by using technology and intelligent devices. This means changing every part of rehabilitation and finding the most valuable and helpful gadgets that can be used to regain lost functions, keep people healthy, or prevent diseases.

The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review

The integration of wearable sensor technology and machine learning algorithms has significantly transformed the field of intelligent medical rehabilitation. These innovative technologies enable the collection of valuable movement, muscle, or nerve data during the rehabilitation process, empowering medical professionals to evaluate patient recovery and predict disease development more efficiently. This systematic review aims to study the application of wearable sensor technology and machine learning algorithms in different disease rehabilitation training programs, obtain the best sensors and algorithms that meet different disease rehabilitation conditions, and provide ideas for future research and development. A total of 1490 studies were retrieved from two databases, the Web of Science and IEEE Xplore, and finally 32 articles were selected. In this review, the selected papers employ different wearable sensors and machine learning algorithms to address different disease rehabilitation problems. Our analysis focuses on the types of wearable sensors employed, the application of machine learning algorithms, and the approach to rehabilitation training for different medical conditions. It summarizes the usage of different sensors and compares different machine learning algorithms. It can be observed that the combination of these two technologies can optimize the disease rehabilitation process and provide more possibilities for future home rehabilitation scenarios. Finally, the present limitations and suggestions for future developments are presented in the study.