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Wearable and Unobtrusive Technologies for Healthcare Monitoring—2nd Edition

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

Deadline for manuscript submissions: 10 November 2025 | Viewed by 15094

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Dr. Carlo Massaroni

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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
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Prof. Dr. Emiliano Schena

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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
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Prof. Dr. Domenico Formica

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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

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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.

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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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Wearable and Unobtrusive Technologies for Healthcare Monitoring in Sensors (56 articles)

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Research

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17 pages, 3625 KiB

Open AccessArticle

Automated Assessment of Upper Extremity Function with the Modified Mallet Score Using Single-Plane Smartphone Videos

by Cancan Su, Lianne Brandt, Guangwen Sun, Kaitlynn Sampel, Edward D. Lemaire, Kevin Cheung, Albert Tu and Natalie Baddour

Sensors 2025, 25(5), 1619; https://doi.org/10.3390/s25051619 - 6 Mar 2025

Viewed by 721

Abstract

The Modified Mallet Score (MMS) is widely used to assess upper limb function but requires evaluation by experienced clinicians. This study automated MMS assessments using smartphone videos, artificial intelligence (AI), and new algorithms. A total of 125 videos covering all MMS grades were recorded from four neurotypical participants. For all recordings, an expert physician provided manual scores as the ground truth. The OpenPose BODY25 model extracted body keypoint data, which were used to calculate joint angles for an automated scoring algorithm. The algorithm’s scores were compared to the ground truth and expert manual scoring. High accuracy was achieved for the global abduction, hand-to-neck, hand-on-spine, and hand-to-mouth movements, with Pearson correlation coefficients (PCCs) > 0.9 and a low root mean square error (RMSE). Although slightly less accurate for global external rotation, the algorithm still showed strong agreement. This study demonstrates the potential of using AI and smartphone videos for reliable, remote upper limb assessments. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring—2nd Edition)

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17 pages, 3529 KiB

Open AccessArticle

Meta-Transfer-Learning-Based Multimodal Human Pose Estimation for Lower Limbs

by Guoming Du, Haiqi Zhu, Zhen Ding, Hong Huang, Xiaofeng Bie and Feng Jiang

Sensors 2025, 25(5), 1613; https://doi.org/10.3390/s25051613 - 6 Mar 2025

Viewed by 854

Abstract

Accurate and reliable human pose estimation (HPE) is essential in interactive systems, particularly for applications requiring personalized adaptation, such as controlling cooperative robots and wearable exoskeletons, especially for healthcare monitoring equipment. However, continuously maintaining diverse datasets and frequently updating models for individual adaptation are both resource intensive and time-consuming. To address these challenges, we propose a meta-transfer learning framework that integrates multimodal inputs, including high-frequency surface electromyography (sEMG), visual-inertial odometry (VIO), and high-precision image data. This framework improves both accuracy and stability through a knowledge fusion strategy, resolving the data alignment issue, ensuring seamless integration of different modalities. To further enhance adaptability, we introduce a training and adaptation framework with few-shot learning, facilitating efficient updating of encoders and decoders for dynamic feature adjustment in real-time applications. Experimental results demonstrate that our framework provides accurate, high-frequency pose estimations, particularly for intra-subject adaptation. Our approach enables efficient adaptation to new individuals with only a few new samples, providing an effective solution for personalized motion analysis with minimal data. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring—2nd Edition)

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18 pages, 5553 KiB

Open AccessArticle

Accuracy of the Instantaneous Breathing and Heart Rates Estimated by Smartphone Inertial Units

by Eliana Cinotti, Jessica Centracchio, Salvatore Parlato, Daniele Esposito, Antonio Fratini, Paolo Bifulco and Emilio Andreozzi

Sensors 2025, 25(4), 1094; https://doi.org/10.3390/s25041094 - 12 Feb 2025

Viewed by 810

Abstract

Seismocardiography (SCG) and Gyrocardiography (GCG) use lightweight, miniaturized accelerometers and gyroscopes to record, respectively, cardiac-induced linear accelerations and angular velocities of the chest wall. These inertial sensors are also sensitive to thoracic movements with respiration, which cause baseline wanderings in SCG and GCG signals. Nowadays, accelerometers and gyroscopes are widely integrated into smartphones, thus increasing the potential of SCG and GCG as cardiorespiratory monitoring tools. This study investigates the accuracy of smartphone inertial sensors in simultaneously measuring instantaneous heart rates and breathing rates. Smartphone-derived SCG and GCG signals were acquired from 10 healthy subjects at rest. The performances of heartbeats and respiratory acts detection, as well as of inter-beat intervals (IBIs) and inter-breath intervals (IBrIs) estimation, were evaluated for both SCG and GCG via the comparison with simultaneous electrocardiography and respiration belt signals. Heartbeats were detected with a sensitivity and positive predictive value (PPV) of 89.3% and 93.3% in SCG signals and of 97.3% and 97.9% in GCG signals. Moreover, IBIs measurements reported strong linear relationships (R² > 0.999), non-significant biases, and Bland–Altman limits of agreement (LoA) of ±7.33 ms for SCG and ±5.22 ms for GCG. On the other hand, respiratory acts detection scored a sensitivity and PPV of 95.6% and 94.7% for SCG and of 95.7% and 92.0% for GCG. Furthermore, high R² values (0.976 and 0.968, respectively), non-significant biases, and an LoA of ±0.558 s for SCG and ±0.749 s for GCG were achieved for IBrIs estimates. The results of this study confirm that smartphone inertial sensors can provide accurate measurements of both instantaneous heart rate and breathing rate without the need for additional devices. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring—2nd Edition)

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25 pages, 9089 KiB

Open AccessArticle

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 1407

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1031

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 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

Open AccessArticle

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

Cited by 2 | Viewed by 2367

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 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

Open AccessArticle

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 1454

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. 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

Open AccessArticle

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 2 | Viewed by 2193

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 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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Review

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30 pages, 2043 KiB

Open AccessReview

Wearable Devices for Arrhythmia Detection: Advancements and Clinical Implications

by Ahmed Abdelrazik, Mahmoud Eldesouky, Ibrahim Antoun, Edward Y. M. Lau, Abdulmalik Koya, Zakariyya Vali, Safiyyah A. Suleman, James Donaldson and G. André Ng

Sensors 2025, 25(9), 2848; https://doi.org/10.3390/s25092848 - 30 Apr 2025

Viewed by 897

Abstract

Cardiac arrhythmias are a growing global health concern, and the need for accessible, continuous monitoring has driven rapid advancements in wearable technologies. This review explores the evolution, capabilities, and clinical impact of modern wearables for arrhythmia detection, including smartwatches, smart rings, ECG patches, and smart textiles. In light of the recent surge in commercially available wearables across all categories, this review offers a detailed comparative analysis of leading devices, evaluating cost, regulatory approval, model specifications, and system compatibility. Smartwatches and patches, in particular, show a strong performance in atrial fibrillation detection, with patches outperforming Holter monitors in long-term monitoring and diagnostic yield. This review highlights a paradigm shift toward patient-initiated diagnostics but also discusses challenges such as false positives, regulatory gaps, and healthcare integration. Overall, wearable devices hold significant promise for reshaping arrhythmia management through early detection and remote monitoring. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring—2nd Edition)

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Other

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11 pages, 240 KiB

Open AccessPerspective

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 8 | Viewed by 2373

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 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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