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Bioengineering
Special Issues
Intelligent Health Management, Nursing and Rehabilitation Technology
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Intelligent Health Management, Nursing and Rehabilitation Technology

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 802

Special Issue Editor

Dr. Ning Deng

E-Mail Website
Guest Editor
College of Biomedical Engineering and Instrumental Science, Zhejiang University, Zhejiang, China
Interests: chronic disease management; artificial intelligent in medicine

Special Issue Information

Dear Colleagues,

The emergence of intelligent health management, nursing and rehabilitation technology signifies a notable fusion of artificial intelligence, machine learning, and modern healthcare practices. This emerging domain aims to enhance traditional rehabilitation processes, refine precise health monitoring, and promote proactive management of chronic conditions. The integration of smart technologies enables real-time monitoring, personalized rehabilitation plans, and reliable clinical decision-making, thus advancing a patient-centric healthcare paradigm.

The scope of this Special Issue encompasses several pivotal aspects vital to the evolution and application of intelligent health management and rehabilitation technology:

  • Proactive Health Systems: Exploring the development and optimization of proactive health systems for real-time health monitoring and management at both individual and community levels.
  • Predictive Models: Investigating the formulation and application of predictive models to identify potential health risks early, optimize rehabilitation plans, and improve the management of chronic conditions.
  • Medical Artificial Intelligence: Delving into the application of AI and ML in supporting clinical decisions, disease diagnosis, and treatment optimization.
  • Traditional Chinese Medicine Health Management: Examining the integration of modern technology with principles of traditional chinese medicine for personalized health and rehabilitation solutions.
  • Smart Healthcare: Encouraging research on designing and implementing smart healthcare solutions leveraging data analytics to enhance patient rehabilitation outcomes and quality of life.
  • Personalized Rehabilitation Strategies: Research focused on utilizing data analytics and machine learning algorithms to design and optimize rehabilitation plans tailored to individual needs for optimal recovery outcomes.
  • Remote Monitoring and Intervention: Investigations into technologies and systems enabling remote health monitoring and real-time interventions, enhancing convenience and effectiveness of rehabilitation processes.
  • Smart Health Devices and Sensors: Exploration of innovative smart devices and sensors for collecting and analyzing health data to support clinical decisions and personalized treatment approaches.

Specifically, the synergy of AI and ML with rehabilitation technology promises to enhance the the development of adaptive rehabilitation protocols. These intelligent systems can evaluate an individual’s progress in real-time, adjusting the rehabilitation plan accordingly to ensure optimal recovery. Furthermore, precise health monitoring, facilitated by smart sensors and wearable devices, delivers invaluable data, guiding informed clinical decisions and timely interventions. Additionally, the proactive management of chronic conditions, bolstered by data analytics, lays the foundation for predictive healthcare, significantly enhancing the quality of life for individuals with chronic ailments.

This Special Issue welcomes original research and insightful reviews exploring the innovative frameworks, methodologies, and applications at the core of intelligent health management and rehabilitation technology. We are particularly interested in contributions that explore the integration of machine learning algorithms with rehabilitation technologies, the development of intelligent health monitoring systems, and the design of personalized rehabilitation strategies that leverage data analytics.

Dr. Ning Deng
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • intelligent rehabilitation technology
  • health monitoring systems
  • machine learning in healthcare
  • intelligent nursing technology
  • data-driven health management

Published Papers (2 papers)

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Research

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18 pages, 1009 KiB  
Article
Unveiling the Unpredictable in Parkinson’s Disease: Sensor-Based Monitoring of Dyskinesias and Freezing of Gait in Daily Life
by Alessandro Zampogna, Luigi Borzì, Domiziana Rinaldi, Carlo Alberto Artusi, Gabriele Imbalzano, Martina Patera, Leonardo Lopiano, Francesco Pontieri, Gabriella Olmo and Antonio Suppa
Bioengineering 2024, 11(5), 440; https://doi.org/10.3390/bioengineering11050440 - 29 Apr 2024
Viewed by 139
Abstract
Background: Dyskinesias and freezing of gait are episodic disorders in Parkinson’s disease, characterized by a fluctuating and unpredictable nature. This cross-sectional study aims to objectively monitor Parkinsonian patients experiencing dyskinesias and/or freezing of gait during activities of daily living and assess possible changes [...] Read more.
Background: Dyskinesias and freezing of gait are episodic disorders in Parkinson’s disease, characterized by a fluctuating and unpredictable nature. This cross-sectional study aims to objectively monitor Parkinsonian patients experiencing dyskinesias and/or freezing of gait during activities of daily living and assess possible changes in spatiotemporal gait parameters. Methods: Seventy-one patients with Parkinson’s disease (40 with dyskinesias and 33 with freezing of gait) were continuously monitored at home for a minimum of 5 days using a single wearable sensor. Dedicated machine-learning algorithms were used to categorize patients based on the occurrence of dyskinesias and freezing of gait. Additionally, specific spatiotemporal gait parameters were compared among patients with and without dyskinesias and/or freezing of gait. Results: The wearable sensor algorithms accurately classified patients with and without dyskinesias as well as those with and without freezing of gait based on the recorded dyskinesias and freezing of gait episodes. Standard spatiotemporal gait parameters did not differ significantly between patients with and without dyskinesias or freezing of gait. Both the time spent with dyskinesias and the number of freezing of gait episodes positively correlated with the disease severity and medication dosage. Conclusions: A single inertial wearable sensor shows promise in monitoring complex, episodic movement patterns, such as dyskinesias and freezing of gait, during daily activities. This approach may help implement targeted therapeutic and preventive strategies for Parkinson’s disease. Full article
(This article belongs to the Special Issue Intelligent Health Management, Nursing and Rehabilitation Technology)

Review

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30 pages, 3358 KiB  
Review
Advancements and Challenges in Non-Invasive Sensor Technologies for Swallowing Assessment: A Review
by Yuwen Wu, Kai Guo, Yuyi Chu, Zhisen Wang, Hongbo Yang and Juzhong Zhang
Bioengineering 2024, 11(5), 430; https://doi.org/10.3390/bioengineering11050430 - 27 Apr 2024
Viewed by 174
Abstract
Dysphagia is a pervasive health issue that impacts diverse demographic groups worldwide, particularly the elderly, stroke survivors, and those suffering from neurological disorders. This condition poses substantial health risks, including malnutrition, respiratory complications, and increased mortality. Additionally, it exacerbates economic burdens by extending [...] Read more.
Dysphagia is a pervasive health issue that impacts diverse demographic groups worldwide, particularly the elderly, stroke survivors, and those suffering from neurological disorders. This condition poses substantial health risks, including malnutrition, respiratory complications, and increased mortality. Additionally, it exacerbates economic burdens by extending hospital stays and escalating healthcare costs. Given that this disorder is frequently underestimated in vulnerable populations, there is an urgent need for enhanced diagnostic and therapeutic strategies. Traditional diagnostic tools such as the videofluoroscopic swallowing study (VFSS) and flexible endoscopic evaluation of swallowing (FEES) require interpretation by clinical experts and may lead to complications. In contrast, non-invasive sensors offer a more comfortable and convenient approach for assessing swallowing function. This review systematically examines recent advancements in non-invasive swallowing function detection devices, focusing on the validation of the device designs and their implementation in clinical practice. Moreover, this review discusses the swallowing process and the associated biomechanics, providing a theoretical foundation for the technologies discussed. It is hoped that this comprehensive overview will facilitate a paradigm shift in swallowing assessments, steering the development of technologies towards more accessible and accurate diagnostic tools, thereby improving patient care and treatment outcomes. Full article
(This article belongs to the Special Issue Intelligent Health Management, Nursing and Rehabilitation Technology)
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