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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 27207

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Dr. Oscar Casas

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Instrumentation, Sensors and Interfaces Group, Universitat Politècnica de Catalunya-BarcelonaTech, 08860 Castelldefels, Barcelona, Spain
Interests: research, development, and transfer of knowledge on new sensors and measurement methods; electronic interfaces for signal conditioning and processing. Emphasis on sensors based on variations in electrical impedance using low-cost technologies, bioelectric and biomechanical signals, autonomous sensors, wireless sensor networks, analog signal processing, bioelectrical impedance spectroscopy and tomography and reduction of noise and instrumentation interference (electromagnetic compatibility); Biomedical applications in clinical and non-clinical environments (telemedicine, eHealth) and for disabled people; sensor networks for agriculture, environment, buildings and intelligent cities; sensors for the automotive industry; noninvasive measures in civil engineering and archaeology
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Special Issue Information

Dear Colleagues,

We are currently suffering the extremely negative consequences of the COVID-19 pandemic. In COVID-19 patients, pneumonia is the most frequent serious manifestation of infection, characterized by fever, cough, dyspnoea, and bilateral infiltrates on chest imaging. No other specific clinical features can yet reliably distinguish COVID-19 from other viral respiratory infections. In addition, patients have often developed other complications such as arrhythmias, acute cardiac injury, and shock. Therefore, for example, a low-cost wearable device able to monitor the evolution of temperature, breathing, blood oxygen, and the cardiovascular condition of the patient can surely help in both the prevention and patient monitoring phases. Furthermore, adding geo-localization to an eHealth system will enable real-time monitoring and optimize decision-making processes to contain this pandemic or to prepare for those of the future. In general, eHealth is often thought of as a way to improve the management of chronic diseases and assist in emergency medicine. However, the use of eHealth devices as potential tools for everyone is still not well considered. For this reason, it is necessary to continue advancing in the optimization of systems for measuring physiological parameters.

The aim of this Special Issue is to bring together innovative developments in the use of electronic sensors and their conditioning circuits for physiological parameter measurements. Authors are encouraged to submit novel material that advances the state of the art of sensor developments and analog and digital interfaces providing high-accuracy, low-cost, low-power solutions in high value-added applications.

Both review articles and original research papers are solicited. There is particular interest in papers envisioning innovative sensor applications in real-life problems, including both hospital and home application systems.

Dr. Oscar Casas
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.

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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

Biomedical measurement
Biomedical equipment
Wearable Sensors
Biomedical circuits and systems
Body area networks
Body sensor networks
Biomedical imaging
Non-invasive medical devices
Non-contact physiological monitoring
Biotelemetry
eHealth
Patient monitoring
Telemedicine
Biochemical sensors
Electrical and optical sensors
Textile biosensors and electronic textiles

Published Papers (9 papers)

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10 pages, 919 KiB

Open AccessArticle

Sample Entropy Improves Assessment of Postural Control in Early-Stage Multiple Sclerosis

by L. Eduardo Cofré Lizama, Xiangyu He, Tomas Kalincik, Mary P. Galea and Maya G. Panisset

Sensors 2024, 24(3), 872; https://doi.org/10.3390/s24030872 - 29 Jan 2024

Cited by 1 | Viewed by 837

Abstract

Postural impairment in people with multiple sclerosis (pwMS) is an early indicator of disease progression. Common measures of disease assessment are not sensitive to early-stage MS. Sample entropy (SE) may better identify early impairments. We compared the sensitivity and specificity of SE with linear measurements, differentiating pwMS (EDSS 0–4) from healthy controls (HC). 58 pwMS (EDSS ≤ 4) and 23 HC performed quiet standing tasks, combining a hard or foam surface with eyes open or eyes closed as a condition. Sway was recorded at the sternum and lumbar spine. Linear measures, mediolateral acceleration range with eyes open, mediolateral jerk with eyes closed, and SE in the anteroposterior and mediolateral directions were calculated. A multivariate ANOVA and AUC-ROC were used to determine between-groups differences and discriminative ability, respectively. Mild MS (EDSS ≤ 2.0) discriminability was secondarily assessed. Significantly lower SE was observed under most conditions in pwMS compared to HC, except for lumbar and sternum SE when on a hard surface with eyes closed and in the anteroposterior direction, which also offered the strongest discriminability (AUC = 0.747), even for mild MS. Overall, between-groups differences were task-dependent, and SE (anteroposterior, hard surface, eyes closed) was the best pwMS classifier. SE may prove a useful tool to detect subtle MS progression and intervention effectiveness. Full article

(This article belongs to the Special Issue Sensors for Physiological Parameters Measurement)

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20 pages, 3748 KiB

Open AccessArticle

Heartbeat Detection in Gyrocardiography Signals without Concurrent ECG Tracings

by Salvatore Parlato, Jessica Centracchio, Daniele Esposito, Paolo Bifulco and Emilio Andreozzi

Sensors 2023, 23(13), 6200; https://doi.org/10.3390/s23136200 - 6 Jul 2023

Cited by 5 | Viewed by 1901

Abstract

A heartbeat generates tiny mechanical vibrations, mainly due to the opening and closing of heart valves. These vibrations can be recorded by accelerometers and gyroscopes applied on a subject’s chest. In particular, the local 3D linear accelerations and 3D angular velocities of the chest wall are referred to as seismocardiograms (SCG) and gyrocardiograms (GCG), respectively. These signals usually exhibit a low signal-to-noise ratio, as well as non-negligible amplitude and morphological changes due to changes in posture and the sensors’ location, respiratory activity, as well as other sources of intra-subject and inter-subject variability. These factors make heartbeat detection a complex task; therefore, a reference electrocardiogram (ECG) lead is usually acquired in SCG and GCG studies to ensure correct localization of heartbeats. Recently, a template matching technique based on cross correlation has proven to be particularly effective in recognizing individual heartbeats in SCG signals. This study aims to verify the performance of this technique when applied on GCG signals. Tests were conducted on a public database consisting of SCG, GCG, and ECG signals recorded synchronously on 100 patients with valvular heart diseases. The results show that the template matching technique identified heartbeats in GCG signals with a sensitivity and positive predictive value (PPV) of 87% and 92%, respectively. Regression, correlation, and Bland–Altman analyses carried out on inter-beat intervals obtained from GCG and ECG (assumed as reference) reported a slope of 0.995, an intercept of 4.06 ms (R² > 0.99), a Pearson’s correlation coefficient of 0.9993, and limits of agreement of about ±13 ms with a negligible bias. A comparison with the results of a previous study obtained on SCG signals from the same database revealed that GCG enabled effective cardiac monitoring in significantly more patients than SCG (95 vs. 77). This result suggests that GCG could ensure more robust and reliable cardiac monitoring in patients with heart diseases with respect to SCG. Full article

(This article belongs to the Special Issue Sensors for Physiological Parameters Measurement)

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14 pages, 1182 KiB

Open AccessArticle

Estimation of Spatiotemporal Gait Parameters in Walking on a Photoelectric System: Validation on Healthy Children by Standard Gait Analysis

by Silvia Campagnini, Guido Pasquini, Florian Schlechtriem, Giulia Fransvea, Laura Simoni, Filippo Gerli, Federica Magaldi, Giovanna Cristella, Robert Riener, Maria Chiara Carrozza and Andrea Mannini

Sensors 2023, 23(13), 6059; https://doi.org/10.3390/s23136059 - 30 Jun 2023

Cited by 2 | Viewed by 1077

Abstract

The use of stereophotogrammetry systems is challenging when targeting children’s gait analysis due to the time required and the need to keep physical markers in place. For this reason, marker-less photoelectric systems appear to be a solution for accurate and fast gait analysis in youth. The aim of this study is to validate a photoelectric system and its configurations (LED filter setting) on healthy children, comparing the kinematic gait parameters with those obtained from a three-dimensional stereophotogrammetry system. Twenty-seven healthy children were enrolled. Three LED filter settings for the OptoGait were compared to the BTS P6000. The analysis included the non-parametric 80% limits of agreement and the intraclass correlation coefficient (ICC). Additionally, normalised limits of agreement and bias (NLoAs and Nbias) were compared to the clinical experience of physical therapists (i.e., assuming an error lower than 5% is acceptable). ICCs showed excellent consistency for most of the parameters and filter settings; NLoAs varied between 1.39% and 12.62%. An inverse association between the number of LEDs for filter setting and the bias values was also observed. Observations confirm the validity of the OptoGait system for the evaluation of spatiotemporal gait parameters in children. Full article

(This article belongs to the Special Issue Sensors for Physiological Parameters Measurement)

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16 pages, 4939 KiB

Open AccessArticle

Quality Indexes of the ECG Signal Transmitted Using Optical Wireless Link

by Amel Chehbani, Stephanie Sahuguede, Anne Julien-Vergonjanne and Olivier Bernard

Sensors 2023, 23(9), 4522; https://doi.org/10.3390/s23094522 - 6 May 2023

Viewed by 1743

Abstract

This work relates to the quality of the electrocardiogram (ECG) signal of an elderly person, transmitted using optical wireless links. The studied system uses infrared signals between an optical transmitter located on the person’s wrist and optical receivers placed on the ceiling. As the elderly person moves inside a room, the optical channel is time-varying, affecting the received ECG signal. To assess the ECG quality, we use specific signal quality indexes (SQIs), allowing the evaluation of the spectral and statistical characteristics of the signal. Our main contribution is studying how the SQIs behave according to the optical transmission performance and the studied context in order to determine the conditions required to obtain excellent quality indexes. The approach is based on the simulation of the whole chain, from the raw ECG to the extraction process after transmission until the evaluation of SQIs. This technique was developed considering optical channel modeling, including the mobility of the elderly. The obtained results show the potential of optical wireless communication technologies for reliable ECG monitoring in such a context. It has been observed that excellent ECG quality can be obtained with a minimum SNR of 11 dB for on–off keying modulation. Full article

(This article belongs to the Special Issue Sensors for Physiological Parameters Measurement)

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16 pages, 3789 KiB

Open AccessArticle

High-Sensitivity Flexible Piezoresistive Pressure Sensor Using PDMS/MWNTS Nanocomposite Membrane Reinforced with Isopropanol for Pulse Detection

by Zhiming Long, Xinggu Liu, Junjie Xu, Yubo Huang and Zhuqing Wang

Sensors 2022, 22(13), 4765; https://doi.org/10.3390/s22134765 - 24 Jun 2022

Cited by 9 | Viewed by 2931

Abstract

Flexible pressure sensors with high sensitivity and good linearity are in high demand to meet the long-term and accurate detection requirements for pulse detection. In this study, we propose a composite membrane pressure sensor using polydimethylsiloxane (PDMS) and multiwalled carbon nanotubes (MWNTS) reinforced with isopropanol prepared by solution blending and a self-made 3D-printed mold. The device doped with isopropanol had a higher sensitivity and linearity owning to the construction of additional conductive paths. The optimal conditions for realizing a high-performance pressure sensor are a multiwalled carbon nanotube mass ratio of 7% and a composite membrane thickness of 490 μm. The membrane achieves a high linear sensitivity of −57.07 kΩ∙kPa⁻¹ and a linear fitting correlation coefficient of 98.78% in the 0.13~5.2 kPa pressure range corresponding to pulse detection. Clearly, this device has great potential for application in pulse detection. Full article

(This article belongs to the Special Issue Sensors for Physiological Parameters Measurement)

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17 pages, 4483 KiB

Open AccessArticle

Non-Contact Video-Based Assessment of the Respiratory Function Using a RGB-D Camera

by Andrea Valenzuela, Nicolás Sibuet, Gemma Hornero and Oscar Casas

Sensors 2021, 21(16), 5605; https://doi.org/10.3390/s21165605 - 20 Aug 2021

Cited by 7 | Viewed by 3387

Abstract

A fully automatic, non-contact method for the assessment of the respiratory function is proposed using an RGB-D camera-based technology. The proposed algorithm relies on the depth channel of the camera to estimate the movements of the body’s trunk during breathing. It solves in [...] Read more.

O (1)

, as the acquisition relies on the mean depth value of the target regions only using the color channels to automatically locate them. This simplicity allows the extraction of real-time values of the respiration, as well as the synchronous assessment on multiple body parts. Two different experiments have been performed: a first one conducted on 10 users in a single region and with a fixed breathing frequency, and a second one conducted on 20 users considering a simultaneous acquisition in two regions. The breath rate has then been computed and compared with a reference measurement. The results show a non-statistically significant bias of

0.11

breaths/min and

96 %

limits of agreement of

- 2.21

2.34

breaths/min regarding the breath-by-breath assessment. The overall real-time assessment shows a RMSE of

0.21

breaths/min. We have shown that this method is suitable for applications where respiration needs to be monitored in non-ambulatory and static environments. Full article

(This article belongs to the Special Issue Sensors for Physiological Parameters Measurement)

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35 pages, 5724 KiB

Open AccessArticle

Implementation of a Deep Learning Algorithm Based on Vertical Ground Reaction Force Time–Frequency Features for the Detection and Severity Classification of Parkinson’s Disease

by Febryan Setiawan and Che-Wei Lin

Sensors 2021, 21(15), 5207; https://doi.org/10.3390/s21155207 - 31 Jul 2021

Cited by 12 | Viewed by 3629

Abstract

Conventional approaches to diagnosing Parkinson’s disease (PD) and rating its severity level are based on medical specialists’ clinical assessment of symptoms, which are subjective and can be inaccurate. These techniques are not very reliable, particularly in the early stages of the disease. A novel detection and severity classification algorithm using deep learning approaches was developed in this research to classify the PD severity level based on vertical ground reaction force (vGRF) signals. Different variations in force patterns generated by the irregularity in vGRF signals due to the gait abnormalities of PD patients can indicate their severity. The main purpose of this research is to aid physicians in detecting early stages of PD, planning efficient treatment, and monitoring disease progression. The detection algorithm comprises preprocessing, feature transformation, and classification processes. In preprocessing, the vGRF signal is divided into 10, 15, and 30 s successive time windows. In the feature transformation process, the time domain vGRF signal in windows with varying time lengths is modified into a time–frequency spectrogram using a continuous wavelet transform (CWT). Then, principal component analysis (PCA) is used for feature enhancement. Finally, different types of convolutional neural networks (CNNs) are employed as deep learning classifiers for classification. The algorithm performance was evaluated using k-fold cross-validation (kfoldCV). The best average accuracy of the proposed detection algorithm in classifying the PD severity stage classification was 96.52% using ResNet-50 with vGRF data from the PhysioNet database. The proposed detection algorithm can effectively differentiate gait patterns based on time–frequency spectrograms of vGRF signals associated with different PD severity levels. Full article

(This article belongs to the Special Issue Sensors for Physiological Parameters Measurement)

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25 pages, 9133 KiB

Open AccessArticle

AIoT-Enabled Rehabilitation Recognition System—Exemplified by Hybrid Lower-Limb Exercises

by Yi-Chun Lai, Yao-Chiang Kan, Yu-Chiang Lin and Hsueh-Chun Lin

Sensors 2021, 21(14), 4761; https://doi.org/10.3390/s21144761 - 12 Jul 2021

Cited by 9 | Viewed by 2669

Abstract

Ubiquitous health management (UHM) is vital in the aging society. The UHM services with artificial intelligence of things (AIoT) can assist home-isolated healthcare in tracking rehabilitation exercises for clinical diagnosis. This study combined a personalized rehabilitation recognition (PRR) system with the AIoT for the UHM of lower-limb rehabilitation exercises. The three-tier infrastructure integrated the recognition pattern bank with the sensor, network, and application layers. The wearable sensor collected and uploaded the rehab data to the network layer for AI-based modeling, including the data preprocessing, featuring, machine learning (ML), and evaluation, to build the recognition pattern. We employed the SVM and ANFIS methods in the ML process to evaluate 63 features in the time and frequency domains for multiclass recognition. The Hilbert-Huang transform (HHT) process was applied to derive the frequency-domain features. As a result, the patterns combining the time- and frequency-domain features, such as relative motion angles in y- and z-axis, and the HHT-based frequency and energy, could achieve successful recognition. Finally, the suggestive patterns stored in the AIoT-PRR system enabled the ML models for intelligent computation. The PRR system can incorporate the proposed modeling with the UHM service to track the rehabilitation program in the future. Full article

(This article belongs to the Special Issue Sensors for Physiological Parameters Measurement)

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34 pages, 1678 KiB

Open AccessSystematic Review

Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review

by Ziyu Liu, Azadeh Alavi, Minyi Li and Xiang Zhang

Sensors 2023, 23(9), 4221; https://doi.org/10.3390/s23094221 - 23 Apr 2023

Cited by 6 | Viewed by 7718

Abstract

Medical time series are sequential data collected over time that measures health-related signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive care unit (ICU) readings. Analyzing medical time series and identifying the latent patterns and trends that lead to uncovering highly valuable insights for enhancing diagnosis, treatment, risk assessment, and disease progression. However, data mining in medical time series is heavily limited by the sample annotation which is time-consuming and labor-intensive, and expert-depending. To mitigate this challenge, the emerging self-supervised contrastive learning, which has shown great success since 2020, is a promising solution. Contrastive learning aims to learn representative embeddings by contrasting positive and negative samples without the requirement for explicit labels. Here, we conducted a systematic review of how contrastive learning alleviates the label scarcity in medical time series based on PRISMA standards. We searched the studies in five scientific databases (IEEE, ACM, Scopus, Google Scholar, and PubMed) and retrieved 1908 papers based on the inclusion criteria. After applying excluding criteria, and screening at title, abstract, and full text levels, we carefully reviewed 43 papers in this area. Specifically, this paper outlines the pipeline of contrastive learning, including pre-training, fine-tuning, and testing. We provide a comprehensive summary of the various augmentations applied to medical time series data, the architectures of pre-training encoders, the types of fine-tuning classifiers and clusters, and the popular contrastive loss functions. Moreover, we present an overview of the different data types used in medical time series, highlight the medical applications of interest, and provide a comprehensive table of 51 public datasets that have been utilized in this field. In addition, this paper will provide a discussion on the promising future scopes such as providing guidance for effective augmentation design, developing a unified framework for analyzing hierarchical time series, and investigating methods for processing multimodal data. Despite being in its early stages, self-supervised contrastive learning has shown great potential in overcoming the need for expert-created annotations in the research of medical time series. Full article

(This article belongs to the Special Issue Sensors for Physiological Parameters Measurement)

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