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Wearable and Unobtrusive Technologies for Healthcare Monitoring—2nd Edition
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Wearable and Unobtrusive Technologies for Healthcare Monitoring—2nd Edition

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 4566

Special Issue Editors

Dr. Carlo Massaroni

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Guest Editor
Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy
Interests: wearable sensors; sensors; physiological monitoring; algorithms for data processing including machine learning; applications of sensors in clinical, occupational, and sports fields
Special Issues, Collections and Topics in MDPI journals
Dr. Emiliano Schena

E-Mail Website
Guest Editor
Laboratory of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
Interests: physiological monitoring; wearable systems; wearable sensors; physiological measurements; active living; cardiorespiratory monitoring; soft sensors
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Domenico Formica

E-Mail Website
Guest Editor
Neurophysiology and Neuroengineering of Human-Technology Interaction Research Unit, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21-00128 Rome, Italy
Interests: robotics; mechatronic; human motor control; neuroengineering; human-machine interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable and unobtrusive technologies are revolutionizing personal care services, as well as the screening, prevention, and management of chronic diseases. A range of patients and users may benefit from wearable and unobtrusive technologies for monitoring the progression of pathologies, facilitating early detection and diagnosis of life-threatening diseases and stress levels, assessing the efficacy of administered therapies, providing low-cost and non-invasive diagnoses, and monitoring relevant or vital signals, even remotely.

This Special Issue is focused on wearable sensors and devices, unobtrusive technologies, and applications in the healthcare/wellness fields to improve the safety, effectiveness, and efficiency of healthcare services in acute and chronic conditions, but also for prevention with the aim of a healthy life and active aging. We strongly encourage the submission of papers focusing on the keywords below, but works on related topics may also be considered.

Dr. Carlo Massaroni
Dr. Emiliano Schena
Prof. Dr. Domenico Formica
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

  • wearable sensors and technologies for medical applications
  • wearable sensors and technologies for physiological parameter monitoring
  • wearable and technologies sensors for applications in neuroscience
  • implantable sensors and devices
  • environmental sensors and devices for healthcare applications
  • body area sensor networks for medical applications
  • sensors for continuous patient monitoring
  • sensors for remote healthcare applications
  • metrological assessment of wearable and unobtrusive sensors

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Related Special Issue

Published Papers (6 papers)

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Research

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25 pages, 9089 KiB  
Article
Remotely Powered Two-Wire Cooperative Sensors for Bioimpedance Imaging Wearables
by Olivier Chételat, Michaël Rapin, Benjamin Bonnal, André Fivaz, Benjamin Sporrer, James Rosenthal and Josias Wacker
Sensors 2024, 24(18), 5896; https://doi.org/10.3390/s24185896 - 11 Sep 2024
Viewed by 307
Abstract
Bioimpedance imaging aims to generate a 3D map of the resistivity and permittivity of biological tissue from multiple impedance channels measured with electrodes applied to the skin. When the electrodes are distributed around the body (for example, by delineating a cross section of [...] Read more.
Bioimpedance imaging aims to generate a 3D map of the resistivity and permittivity of biological tissue from multiple impedance channels measured with electrodes applied to the skin. When the electrodes are distributed around the body (for example, by delineating a cross section of the chest or a limb), bioimpedance imaging is called electrical impedance tomography (EIT) and results in functional 2D images. Conventional EIT systems rely on individually cabling each electrode to master electronics in a star configuration. This approach works well for rack-mounted equipment; however, the bulkiness of the cabling is unsuitable for a wearable system. Previously presented cooperative sensors solve this cabling problem using active (dry) electrodes connected via a two-wire parallel bus. The bus can be implemented with two unshielded wires or even two conductive textile layers, thus replacing the cumbersome wiring of the conventional star arrangement. Prior research demonstrated cooperative sensors for measuring bioimpedances, successfully realizing a measurement reference signal, sensor synchronization, and data transfer though still relying on individual batteries to power the sensors. Subsequent research using cooperative sensors for biopotential measurements proposed a method to remove batteries from the sensors and have the central unit supply power over the two-wire bus. Building from our previous research, this paper presents the application of this method to the measurement of bioimpedances. Two different approaches are discussed, one using discrete, commercially available components, and the other with an application-specific integrated circuit (ASIC). The initial experimental results reveal that both approaches are feasible, but the ASIC approach offers advantages for medical safety, as well as lower power consumption and a smaller size. Full article
(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring—2nd Edition)
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19 pages, 6739 KiB  
Article
Towards the Instrumentation of Facemasks Used as Personal Protective Equipment for Unobtrusive Breathing Monitoring of Workers
by Mariangela Pinnelli, Daniela Lo Presti, Sergio Silvestri, Roberto Setola, Emiliano Schena and Carlo Massaroni
Sensors 2024, 24(17), 5815; https://doi.org/10.3390/s24175815 - 7 Sep 2024
Viewed by 303
Abstract
This study focuses on the integration and validation of a filtering face piece 3 (FFP3) facemask module for monitoring breathing activity in industrial environments. The key objective is to ensure accurate, real-time respiratory rate (RR) monitoring while maintaining workers’ comfort. RR monitoring is [...] Read more.
This study focuses on the integration and validation of a filtering face piece 3 (FFP3) facemask module for monitoring breathing activity in industrial environments. The key objective is to ensure accurate, real-time respiratory rate (RR) monitoring while maintaining workers’ comfort. RR monitoring is conducted through temperature variations detected using temperature sensors tested in two configurations: sensor t1, integrated inside the exhalation valve and necessitating structural mask modifications, and sensor t2, mounted externally in a 3D-printed structure, thus preserving its certification as a piece of personal protective equipment (PPE). Ten healthy volunteers participated in static and dynamic tests, simulating typical daily life and industrial occupational activities while wearing the breathing activity monitoring module and a chest strap as a reference instrument. These tests were carried out in both indoor and outdoor settings. The results demonstrate comparable mean absolute error (MAE) for t1 and t2 in both indoor (i.e., 0.31 bpm and 0.34 bpm) and outdoor conditions (i.e., 0.43 bpm and 0.83 bpm). During simulated working activities, both sensors showed consistency with MAE values in static tests and were not influenced by motion artifacts, with more than 97% of RR estimated errors within ±2 bpm. These findings demonstrate the effectiveness of integrating a smart module into protective masks, enhancing occupational health monitoring by providing continuous and precise RR data without requiring additional wearable devices. Full article
(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring—2nd Edition)
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14 pages, 1462 KiB  
Article
Exploring the Potential of a Smart Ring to Predict Postoperative Pain Outcomes in Orthopedic Surgery Patients
by Michael Morimoto, Ashraf Nawari, Rada Savic and Meir Marmor
Sensors 2024, 24(15), 5024; https://doi.org/10.3390/s24155024 - 3 Aug 2024
Viewed by 698
Abstract
Poor pain alleviation remains a problem following orthopedic surgery, leading to prolonged recovery time, increased morbidity, and prolonged opioid use after hospitalization. Wearable device data, collected during postsurgical recovery, may help ameliorate poor pain alleviation because a patient’s physiological state during the recovery [...] Read more.
Poor pain alleviation remains a problem following orthopedic surgery, leading to prolonged recovery time, increased morbidity, and prolonged opioid use after hospitalization. Wearable device data, collected during postsurgical recovery, may help ameliorate poor pain alleviation because a patient’s physiological state during the recovery process may be inferred from sensor data. In this study, we collected smart ring data from 37 inpatients following orthopedic surgery and developed machine learning models to predict if a patient had postsurgical poor pain alleviation. Machine learning models based on the smart ring data were able to predict if a patient had poor pain alleviation during their hospital stay with an accuracy of 70.0%, an F1-score of 0.769, and an area under the receiver operating characteristics curve of 0.762 on an independent test dataset. These values were similar to performance metrics from existing models that rely on static, preoperative patient factors. Our results provide preliminary evidence that wearable device data may help control pain after orthopedic surgery by incorporating real-time, objective estimates of a patient’s pain during recovery. Full article
(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring—2nd Edition)
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18 pages, 6058 KiB  
Article
Scalp Electroencephalogram-Derived Involvement Indexes during a Working Memory Task Performed by Patients with Epilepsy
by Erica Iammarino, Ilaria Marcantoni, Agnese Sbrollini, MHD Jafar Mortada, Micaela Morettini and Laura Burattini
Sensors 2024, 24(14), 4679; https://doi.org/10.3390/s24144679 - 18 Jul 2024
Viewed by 723
Abstract
Electroencephalography (EEG) wearable devices are particularly suitable for monitoring a subject’s engagement while performing daily cognitive tasks. EEG information provided by wearable devices varies with the location of the electrodes, the suitable location of which can be obtained using standard multi-channel EEG recorders. [...] Read more.
Electroencephalography (EEG) wearable devices are particularly suitable for monitoring a subject’s engagement while performing daily cognitive tasks. EEG information provided by wearable devices varies with the location of the electrodes, the suitable location of which can be obtained using standard multi-channel EEG recorders. Cognitive engagement can be assessed during working memory (WM) tasks, testing the mental ability to process information over a short period of time. WM could be impaired in patients with epilepsy. This study aims to evaluate the cognitive engagement of nine patients with epilepsy, coming from a public dataset by Boran et al., during a verbal WM task and to identify the most suitable location of the electrodes for this purpose. Cognitive engagement was evaluated by computing 37 engagement indexes based on the ratio of two or more EEG rhythms assessed by their spectral power. Results show that involvement index trends follow changes in cognitive engagement elicited by the WM task, and, overall, most changes appear most pronounced in the frontal regions, as observed in healthy subjects. Therefore, involvement indexes can reflect cognitive status changes, and frontal regions seem to be the ones to focus on when designing a wearable mental involvement monitoring EEG system, both in physiological and epileptic conditions. Full article
(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring—2nd Edition)
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17 pages, 2527 KiB  
Article
Sensor-Based Quantification of MDS-UPDRS III Subitems in Parkinson’s Disease Using Machine Learning
by Rene Peter Bremm, Lukas Pavelka, Maria Moscardo Garcia, Laurent Mombaerts, Rejko Krüger and Frank Hertel
Sensors 2024, 24(7), 2195; https://doi.org/10.3390/s24072195 - 29 Mar 2024
Cited by 1 | Viewed by 1132
Abstract
Wearable sensors could be beneficial for the continuous quantification of upper limb motor symptoms in people with Parkinson’s disease (PD). This work evaluates the use of two inertial measurement units combined with supervised machine learning models to classify and predict a subset of [...] Read more.
Wearable sensors could be beneficial for the continuous quantification of upper limb motor symptoms in people with Parkinson’s disease (PD). This work evaluates the use of two inertial measurement units combined with supervised machine learning models to classify and predict a subset of MDS-UPDRS III subitems in PD. We attached the two compact wearable sensors on the dorsal part of each hand of 33 people with PD and 12 controls. Each participant performed six clinical movement tasks in parallel with an assessment of the MDS-UPDRS III. Random forest (RF) models were trained on the sensor data and motor scores. An overall accuracy of 94% was achieved in classifying the movement tasks. When employed for classifying the motor scores, the averaged area under the receiver operating characteristic values ranged from 68% to 92%. Motor scores were additionally predicted using an RF regression model. In a comparative analysis, trained support vector machine models outperformed the RF models for specific tasks. Furthermore, our results surpass the literature in certain cases. The methods developed in this work serve as a base for future studies, where home-based assessments of pharmacological effects on motor function could complement regular clinical assessments. Full article
(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring—2nd Edition)
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11 pages, 240 KiB  
Perspective
Breaking down the Digital Fortress: The Unseen Challenges in Healthcare Technology—Lessons Learned from 10 Years of Research
by Alison Keogh, Rob Argent, Cailbhe Doherty, Ciara Duignan, Orna Fennelly, Ciaran Purcell, William Johnston and Brian Caulfield
Sensors 2024, 24(12), 3780; https://doi.org/10.3390/s24123780 - 11 Jun 2024
Cited by 2 | Viewed by 914
Abstract
Healthcare is undergoing a fundamental shift in which digital health tools are becoming ubiquitous, with the promise of improved outcomes, reduced costs, and greater efficiency. Healthcare professionals, patients, and the wider public are faced with a paradox of choice regarding technologies across multiple [...] Read more.
Healthcare is undergoing a fundamental shift in which digital health tools are becoming ubiquitous, with the promise of improved outcomes, reduced costs, and greater efficiency. Healthcare professionals, patients, and the wider public are faced with a paradox of choice regarding technologies across multiple domains. Research is continuing to look for methods and tools to further revolutionise all aspects of health from prediction, diagnosis, treatment, and monitoring. However, despite its promise, the reality of implementing digital health tools in practice, and the scalability of innovations, remains stunted. Digital health is approaching a crossroads where we need to shift our focus away from simply looking at developing new innovations to seriously considering how we overcome the barriers that currently limit its impact. This paper summarises over 10 years of digital health experiences from a group of researchers with backgrounds in physical therapy—in order to highlight and discuss some of these key lessons—in the areas of validity, patient and public involvement, privacy, reimbursement, and interoperability. Practical learnings from this collective experience across patient cohorts are leveraged to propose a list of recommendations to enable researchers to bridge the gap between the development and implementation of digital health tools. Full article
(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring—2nd Edition)
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