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Wearable Sensors and Internet of Things for Biomedical Monitoring
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Wearable Sensors and Internet of Things for Biomedical Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 6298

Special Issue Editors

Dr. Massimo Martinelli

E-Mail Website
Guest Editor
Signals and Images Laboratory, Institute of Information Science and Technologies "A. Faedo" (ISTI), National Research Council of Italy (CNR), Via Moruzzi, 1, 56124 Pisa, Italy
Interests: computational intelligence and intelligent systems; deep learning; artificial intelligence; decision support systems; advanced web technologies; multimedia information processing, signal processing, wearable sensors, biomedical sensors, physiological signal processing; assistive technologies; interactive systems and augmented reality
Special Issues, Collections and Topics in MDPI journals
Dr. Davide Moroni

E-Mail Website
Guest Editor
Institute of Information Science and Technologies, National Research Council of Italy, Signals and Images Laboratory, Via Moruzzi, 1, 56124 Pisa, Italy
Interests: computational intelligence and intelligent systems; artificial intelligence; computer vision; multimedia information processing; signal processing; assistive technologies; interactive systems and augmented reality
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Aleš Procházka

E-Mail Website
Guest Editor
1. Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague, Czech Republic
2. Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 166 36 Prague, Czech Republic
Interests: digital signal processing; machine learning; computational intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The study and implementation of increasingly miniaturized sensors have led the development of wearable devices connected to Internet, which can be used to monitor ubiquitously physiological parameters and activities, including medical procedures, in several kinds of situations and environments.

The technological advances in wearable sensors, network communication, and data sciences led to the conception of the Internet of Biomedical Things (IoBT) and to the exploration of several possible applications of physical and chemical sensors, ranging from telemedicine intelligence to telerobotics for surgical assistance, from ambient assisted living to cognitive coaching.

Multidisciplinary theoretical and practical skills are often required to collect, process, and analyze the data (signals, images, etc.) obtained by systems of biomedical thing, in order to evaluate biophysical responses and correlate them with context parameters and external factors. To this end, intelligent computational models that deal with real-time big data multimedia information are needed for performance evaluation, adaptive planning, rehabilitation, prevention, or simulation to highlight and discriminate among different pathologies and allow for targeted decisions.

This Special Issue, titled "Wearable Sensors and Internet of Things for Biomedical Monitoring", intends to explore the scientific-technological frontier that underlies the optimal solution of the above-mentioned problems, while involving the development and use of innovative sensors and smart methods for the interpretation of data and scenarios.

Dr. Massimo Martinelli
Dr. Davide Moroni
Prof. Dr. Aleš Procházka
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 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. Sensors 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 2600 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

  • biological signals and sensors
  • telemedicine, telemonitoring and telecare
  • artificial intelligence
  • digital signal and image processing
  • biological activities
  • medical activities
  • motion analysis
  • multimedia data analysis
  • internal or external proximity, depth and motion sensors
  • acoustic, magnetic, electric, inductive, mechanical and thermal sensors
  • textile sensors and advanced materials
  • sensors’ data aggregation and fusion
  • pervasive computing on the Internet of Biomedical Things
  • privacy and data protection in the Internet of Biomedical Things
  • machine-learning approaches in the Internet of Biomedical Things
  • wearable sensors and cloud computing on the Internet of Biomedical Things

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Published Papers (3 papers)

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Research

17 pages, 4153 KiB  
Article
A Wearable Personalised Sonification and Biofeedback Device to Enhance Movement Awareness
by Toh Yen Pang, Thomas Connelly, Frank Feltham, Chi-Tsun Cheng, Azizur Rahman, Jeffrey Chan, Luke McCarney and Katrina Neville
Sensors 2024, 24(15), 4814; https://doi.org/10.3390/s24154814 - 24 Jul 2024
Viewed by 883
Abstract
Movement sonification has emerged as a promising approach for rehabilitation and motion control. Despite significant advancements in sensor technologies, challenges remain in developing cost-effective, user-friendly, and reliable systems for gait detection and sonification. This study introduces a novel wearable personalised sonification and biofeedback [...] Read more.
Movement sonification has emerged as a promising approach for rehabilitation and motion control. Despite significant advancements in sensor technologies, challenges remain in developing cost-effective, user-friendly, and reliable systems for gait detection and sonification. This study introduces a novel wearable personalised sonification and biofeedback device to enhance movement awareness for individuals with irregular gait and posture. Through the integration of inertial measurement units (IMUs), MATLAB, and sophisticated audio feedback mechanisms, the device offers real-time, intuitive cues to facilitate gait correction and improve functional mobility. Utilising a single wearable sensor attached to the L4 vertebrae, the system captures kinematic parameters to generate auditory feedback through discrete and continuous tones corresponding to heel strike events and sagittal plane rotations. A preliminary test that involved 20 participants under various audio feedback conditions was conducted to assess the system’s accuracy, reliability, and user synchronisation. The results indicate a promising improvement in movement awareness facilitated by auditory cues. This suggests a potential for enhancing gait and balance, particularly beneficial for individuals with compromised gait or those undergoing a rehabilitation process. This paper details the development process, experimental setup, and initial findings, discussing the integration challenges and future research directions. It also presents a novel approach to providing real-time feedback to participants about their balance, potentially enabling them to make immediate adjustments to their posture and movement. Future research should evaluate this method in varied real-world settings and populations, including the elderly and individuals with Parkinson’s disease. Full article
(This article belongs to the Special Issue Wearable Sensors and Internet of Things for Biomedical Monitoring)
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34 pages, 9369 KiB  
Article
Healthcare Application of In-Shoe Motion Sensor for Older Adults: Frailty Assessment Using Foot Motion during Gait
by Chenhui Huang, Fumiyuki Nihey, Kazuki Ihara, Kenichiro Fukushi, Hiroshi Kajitani, Yoshitaka Nozaki and Kentaro Nakahara
Sensors 2023, 23(12), 5446; https://doi.org/10.3390/s23125446 - 8 Jun 2023
Cited by 5 | Viewed by 1794
Abstract
Frailty poses a threat to the daily lives of healthy older adults, highlighting the urgent need for technologies that can monitor and prevent its progression. Our objective is to demonstrate a method for providing long-term daily frailty monitoring using an in-shoe motion sensor [...] Read more.
Frailty poses a threat to the daily lives of healthy older adults, highlighting the urgent need for technologies that can monitor and prevent its progression. Our objective is to demonstrate a method for providing long-term daily frailty monitoring using an in-shoe motion sensor (IMS). We undertook two steps to achieve this goal. Firstly, we used our previously established SPM-LOSO-LASSO (SPM: statistical parametric mapping; LOSO: leave-one-subject-out; LASSO: least absolute shrinkage and selection operator) algorithm to construct a lightweight and interpretable hand grip strength (HGS) estimation model for an IMS. This algorithm automatically identified novel and significant gait predictors from foot motion data and selected optimal features to construct the model. We also tested the robustness and effectiveness of the model by recruiting other groups of subjects. Secondly, we designed an analog frailty risk score that combined the performance of the HGS and gait speed with the aid of the distribution of HGS and gait speed of the older Asian population. We then compared the effectiveness of our designed score with the clinical expert-rated score. We discovered new gait predictors for HGS estimation via IMSs and successfully constructed a model with an “excellent” intraclass correlation coefficient and high precision. Moreover, we tested the model on separately recruited subjects, which confirmed the robustness of our model for other older individuals. The designed frailty risk score also had a large effect size correlation with clinical expert-rated scores. In conclusion, IMS technology shows promise for long-term daily frailty monitoring, which can help prevent or manage frailty for older adults. Full article
(This article belongs to the Special Issue Wearable Sensors and Internet of Things for Biomedical Monitoring)
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15 pages, 1715 KiB  
Article
Coherence Function and Adaptive Noise Cancellation Performance of an Acoustic Sensor System for Use in Detecting Coronary Artery Disease
by Matthew Fynn, Sven Nordholm and Yue Rong
Sensors 2022, 22(17), 6591; https://doi.org/10.3390/s22176591 - 31 Aug 2022
Cited by 5 | Viewed by 2231
Abstract
Adaptive noise cancellation is a useful linear technique to attenuate unwanted background noise that cannot be removed using traditional frequency-selective filters. Usually, this is due to the signal and noise co-existing in the same frequency band. This paper tests a weighted least mean [...] Read more.
Adaptive noise cancellation is a useful linear technique to attenuate unwanted background noise that cannot be removed using traditional frequency-selective filters. Usually, this is due to the signal and noise co-existing in the same frequency band. This paper tests a weighted least mean squares (WLMS) algorithm on a stethoscope system for use in detecting coronary artery disease in the presence of background noise. Each stethoscope is equipped with two microphones: one used to detect heart signals and one used to detect background noise. The WLMS method was used for four different sources of background noise whilst measuring a heartbeat, including a single tone, multiple tones, hospital/clinic noise, and breathing noise. The magnitude-squared coherence between both microphones was unity for the tone scenarios, resulting in complete attenuation. For the other background noise sources, a less-than-unity magnitude-squared coherence resulted in minor and no attenuation. Thus, the coherence function is a tool that can be used to predict the amount of attenuation achievable by linear adaptive noise-cancellation techniques, such as WLMS, as presented in this article. Full article
(This article belongs to the Special Issue Wearable Sensors and Internet of Things for Biomedical Monitoring)
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