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Special Issue "Wearable Sensors in Healthcare: Methods, Algorithms, Applications"

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

Deadline for manuscript submissions: 31 October 2019.

Special Issue Editors

Prof. Dr. Andrea Facchinetti
E-Mail Website
Guest Editor
Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, 35131 Padova PD, Italy
Interests: software to improve both performance and usability of continuous glucose monitoring (CGM) sensors; new algorithms to predict the onset of type 2 diabetes
Special Issues and Collections in MDPI journals
Dr. Martina Vettoretti
E-Mail Website
Guest Editor
Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, 35131, Padova PD, Italy
Interests: signal processing and modeling techniques for the analysis of glucose sensors data; strategies for type 1 diabetes insulin therapy optimization; machine-learning techniques applied to prediction of type 2 diabetes and asthma onset
Dr. Veronica Iacovacci
E-Mail Website
Guest Editor
BioRobotics Institute, Sant'Anna School of Advanced Studies, Pisa, Italy
Interests: mechatronics for implantable artificial organs (mainly artificial pancreas and artificial bladder systems) with a particular focus on magnetic actuation and sensing for implantable devices; microfabrication; smart magnetic materials; smart microsystems for targeted therapy
Prof. Dr. Danilo Pani
E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, Universita degli Studi di Cagliari, Cagliari, Italy
Interests: real-time biomedical signal processing, including non-invasive fetal electrocardiography, neural signal decoding for motor neuroprostheses, and electrophysiology of taste; wearable electronics and textile electrodes; tele-health systems
Prof. Dr. Giovanni Sparacino
E-Mail Website
Guest Editor
Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, 35131, Padova PD, Italy
Interests: sensors and algorithms for continuous glucose monitoring; deconvolution and parameter estimation techniques for the study of physiological systems; linear and nonlinear biological time-series analysis; measurement and processing of biomedical signals (EEG, event-related potentials, local field potentials, fNIRS, etc.) for clinical research and applications
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable sensors enabling the continuous-time monitoring of health-related parameters outside the clinical setting have the potential to revolutionize healthcare. Indeed, wearable sensors, coupled with tailored signal processing algorithms for the extraction of digital biomarkers, often outperform traditional clinical methods in detecting health events. For instance, in people with diabetes, glucose monitoring sensors can detect hypoglycemic events that standard blood tests cannot reveal. Similarly, ambulatory dynamic and long-term electrocardiograms can detect arrhythmias that happen unpredictably throughout the day and thus can be difficult to capture by conventional 10 seconds rest electrocardiography. Wearable sensors have also acquired increasing importance in the field of rehabilitation thanks to the possibility of detecting not only physiological but also movement data.

Data collected by wearable sensors offer a more detailed picture of the patient health status that allows planning personalized interventions with the aim of minimizing potential health risks. When monitoring chronic diseases, this can be done, for example, by telemedicine applications or personalized decision-support systems. Such preventive and personalized medicine applications will potentially change the healthcare system from a reactive to a proactive system.

Given their potentialities, wearable sensors are currently the object of intense research activity, both in academic groups and industries, including multinational IT companies. A broad variety of wearable sensing technologies have been proposed, such as mechanical sensors for tracking movements and pressure, chemical sensors measuring analytes in biological fluids (e.g., interstitial fluids, breath, sweat, saliva, and tears), and other sensors based on electrical, optical, thermal, and acoustic techniques to sense physiological signals.

Although great steps forward have been made in recent years, wearable sensors still need to be improved from both hardware and software points of view, in order to increase their usability, accuracy, and medical utility, and finally integrate them in the healthcare system.

In this Special Issue, we seek original research papers or review papers about algorithms for wearable sensors and their application in the medical field. In particular, we look for contributions on:

  • algorithms to enhance the performance of wearable sensors in terms of accuracy and precision (e.g. calibration and filtering algorithms)
  • algorithms using wearable sensors data to extract medical knowledge (e.g. event detection or prediction)
  • methods to provide personalized interventions (e.g. therapy adjustment, behavioral coaching and biofeedback) based on wearable sensors data.

Prof. Dr. Andrea Facchinetti
Dr. Martina Vettoretti
Dr. Veronica Iacovacci
Prof. Dr. Danilo Pani
Prof. Dr. Giovanni Sparacino
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 papers will be 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 1800 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.

Published Papers (11 papers)

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Research

Open AccessArticle
Prediction of Relative Physical Activity Intensity Using Multimodal Sensing of Physiological Data
Sensors 2019, 19(20), 4509; https://doi.org/10.3390/s19204509 - 17 Oct 2019
Abstract
This study examined the feasibility of a non-laboratory approach that uses machine learning on multimodal sensor data to predict relative physical activity (PA) intensity. A total of 22 participants completed up to 7 PA sessions, where each session comprised 5 trials (sitting and [...] Read more.
This study examined the feasibility of a non-laboratory approach that uses machine learning on multimodal sensor data to predict relative physical activity (PA) intensity. A total of 22 participants completed up to 7 PA sessions, where each session comprised 5 trials (sitting and standing, comfortable walk, brisk walk, jogging, running). Participants wore a wrist-strapped sensor that recorded heart-rate (HR), electrodermal activity (Eda) and skin temperature (Temp). After each trial, participants provided ratings of perceived exertion (RPE). Three classifiers, including random forest (RF), neural network (NN) and support vector machine (SVM), were applied independently on each feature set to predict relative PA intensity as low (RPE ≤ 11), moderate (RPE 12–14), or high (RPE ≥ 15). Then, both feature fusion and decision fusion of all combinations of sensor modalities were carried out to investigate the best combination. Among the single modality feature sets, HR provided the best performance. The combination of modalities using feature fusion provided a small improvement in performance. Decision fusion did not improve performance over HR features alone. A machine learning approach using features from HR provided acceptable predictions of relative PA intensity. Adding features from other sensing modalities did not significantly improve performance. Full article
(This article belongs to the Special Issue Wearable Sensors in Healthcare: Methods, Algorithms, Applications)
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Open AccessArticle
Quality of Daily-Life Gait: Novel Outcome for Trials that Focus on Balance, Mobility, and Falls
Sensors 2019, 19(20), 4388; https://doi.org/10.3390/s19204388 - 11 Oct 2019
Abstract
Technological advances in inertial sensors allow for monitoring of daily-life gait characteristics as a proxy for fall risk. The quality of daily-life gait could serve as a valuable outcome for intervention trials, but the uptake of these measures relies on their power to [...] Read more.
Technological advances in inertial sensors allow for monitoring of daily-life gait characteristics as a proxy for fall risk. The quality of daily-life gait could serve as a valuable outcome for intervention trials, but the uptake of these measures relies on their power to detect relevant changes in fall risk. We collected daily-life gait characteristics in 163 older people (aged 77.5 ± 7.5, 107♀) over two measurement weeks that were two weeks apart. We present variance estimates of daily-life gait characteristics that are sensitive to fall risk and estimate the number of participants required to obtain sufficient statistical power for repeated comparisons. The provided data allows for power analyses for studies using daily-life gait quality as outcome. Our results show that the number of participants required (i.e., 8 to 343 depending on the anticipated effect size and between-measurements correlation) is similar to that generally used in fall prevention trials. We propose that the quality of daily-life gait is a promising outcome for intervention studies that focus on improving balance and mobility and reducing falls. Full article
(This article belongs to the Special Issue Wearable Sensors in Healthcare: Methods, Algorithms, Applications)
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Open AccessArticle
Ambulatory Assessment of the Dynamic Margin of Stability Using an Inertial Sensor Network
Sensors 2019, 19(19), 4117; https://doi.org/10.3390/s19194117 - 23 Sep 2019
Abstract
Loss of stability is a precursor to falling and therefore represents a leading cause of injury, especially in fragile people. Thus, dynamic stability during activities of daily living (ADLs) needs to be considered to assess balance control and fall risk. The dynamic margin [...] Read more.
Loss of stability is a precursor to falling and therefore represents a leading cause of injury, especially in fragile people. Thus, dynamic stability during activities of daily living (ADLs) needs to be considered to assess balance control and fall risk. The dynamic margin of stability (MOS) is often used as an indicator of how the body center of mass is located and moves relative to the base of support. In this work, we propose a magneto-inertial measurement unit (MIMU)-based method to assess the MOS of a gait. Six young healthy subjects were asked to walk on a treadmill at different velocities while wearing MIMUs on their lower limbs and pelvis. We then assessed the MOS by computing the lower body displacement with respect to the leading inverse kinematics approach. The results were compared with those obtained using a camera-based system in terms of root mean square deviation (RMSD) and correlation coefficient (ρ). We obtained a RMSD of ≤1.80 cm and ρ ≥ 0.85 for each walking velocity. The findings revealed that our method is comparable to camera-based systems in terms of accuracy, suggesting that it may represent a strategy to assess stability during ADLs in unstructured environments. Full article
(This article belongs to the Special Issue Wearable Sensors in Healthcare: Methods, Algorithms, Applications)
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Open AccessArticle
Low Resource Complexity R-peak Detection Based on Triangle Template Matching and Moving Average Filter
Sensors 2019, 19(18), 3997; https://doi.org/10.3390/s19183997 - 16 Sep 2019
Abstract
A novel R-peak detection algorithm suitable for wearable electrocardiogram (ECG) devices is proposed with four objectives: robustness to noise, low latency processing, low resource complexity, and automatic tuning of parameters. The approach is a two-pronged algorithm comprising (1) triangle template matching to accentuate [...] Read more.
A novel R-peak detection algorithm suitable for wearable electrocardiogram (ECG) devices is proposed with four objectives: robustness to noise, low latency processing, low resource complexity, and automatic tuning of parameters. The approach is a two-pronged algorithm comprising (1) triangle template matching to accentuate the slope information of the R-peaks and (2) a single moving average filter to define a dynamic threshold for peak detection. The proposed algorithm was validated on eight ECG public databases. The obtained results not only presented good accuracy, but also low resource complexity, all of which show great potential for detection R-peaks in ECG signals collected from wearable devices. Full article
(This article belongs to the Special Issue Wearable Sensors in Healthcare: Methods, Algorithms, Applications)
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Open AccessArticle
Enhanced Accuracy of Continuous Glucose Monitoring during Exercise through Physical Activity Tracking Integration
Sensors 2019, 19(17), 3757; https://doi.org/10.3390/s19173757 - 30 Aug 2019
Abstract
Current Continuous Glucose Monitors (CGM) exhibit increased estimation error during periods of aerobic physical activity. The use of readily-available exercise monitoring devices opens new possibilities for accuracy enhancement during these periods. The viability of an array of physical activity signals provided by three [...] Read more.
Current Continuous Glucose Monitors (CGM) exhibit increased estimation error during periods of aerobic physical activity. The use of readily-available exercise monitoring devices opens new possibilities for accuracy enhancement during these periods. The viability of an array of physical activity signals provided by three different wearable devices was considered. Linear regression models were used in this work to evaluate the correction capabilities of each of the wearable signals and propose a model for CGM correction during exercise. A simple two-input model can reduce CGM error during physical activity (17.46% vs. 13.8%, p < 0.005) to the magnitude of the baseline error level (13.61%). The CGM error is not worsened in periods without physical activity. The signals identified as optimal inputs for the model are “Mets” (Metabolic Equivalent of Tasks) from the Fitbit Charge HR device, which is a normalized measurement of energy expenditure, and the skin temperature reading provided by the Microsoft Band 2 device. A simpler one-input model using only “Mets” is also viable for a more immediate implementation of this correction into market devices. Full article
(This article belongs to the Special Issue Wearable Sensors in Healthcare: Methods, Algorithms, Applications)
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Open AccessArticle
Classification of Postprandial Glycemic Status with Application to Insulin Dosing in Type 1 Diabetes—An In Silico Proof-of-Concept
Sensors 2019, 19(14), 3168; https://doi.org/10.3390/s19143168 - 18 Jul 2019
Cited by 1
Abstract
In the daily management of type 1 diabetes (T1D), determining the correct insulin dose to be injected at meal-time is fundamental to achieve optimal glycemic control. Wearable sensors, such as continuous glucose monitoring (CGM) devices, are instrumental to achieve this purpose. In this [...] Read more.
In the daily management of type 1 diabetes (T1D), determining the correct insulin dose to be injected at meal-time is fundamental to achieve optimal glycemic control. Wearable sensors, such as continuous glucose monitoring (CGM) devices, are instrumental to achieve this purpose. In this paper, we show how CGM data, together with commonly recorded inputs (carbohydrate intake and bolus insulin), can be used to develop an algorithm that allows classifying, at meal-time, the post-prandial glycemic status (i.e., blood glucose concentration being too low, too high, or within target range). Such an outcome can then be used to improve the efficacy of insulin therapy by reducing or increasing the corresponding meal bolus dose. A state-of-the-art T1D simulation environment, including intraday variability and a behavioral model, was used to generate a rich in silico dataset corresponding to 100 subjects over a two-month scenario. Then, an extreme gradient-boosted tree (XGB) algorithm was employed to classify the post-prandial glycemic status. Finally, we demonstrate how the XGB algorithm outcome can be exploited to improve glycemic control in T1D through real-time adjustment of the meal insulin bolus. The proposed XGB algorithm obtained good accuracy at classifying post-prandial glycemic status (AUROC = 0.84 [0.78, 0.87]). Consequently, when used to adjust, in real-time, meal insulin boluses obtained with a bolus calculator, the proposed approach improves glycemic control when compared to the baseline bolus calculator. In particular, percentage time in target [70, 180] mg/dL was improved from 61.98 (±13.89) to 67.00 (±11.54; p < 0.01) without increasing hypoglycemia. Full article
(This article belongs to the Special Issue Wearable Sensors in Healthcare: Methods, Algorithms, Applications)
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Open AccessArticle
Two-Stage Latent Dynamics Modeling and Filtering for Characterizing Individual Walking and Running Patterns with Smartphone Sensors
Sensors 2019, 19(12), 2712; https://doi.org/10.3390/s19122712 - 17 Jun 2019
Abstract
Recently, data from built-in sensors in smartphones have been readily available, and analyzing data for various types of health information from smartphone users has become a popular health care application area. Among relevant issues in the area, one of the most prominent topics [...] Read more.
Recently, data from built-in sensors in smartphones have been readily available, and analyzing data for various types of health information from smartphone users has become a popular health care application area. Among relevant issues in the area, one of the most prominent topics is analyzing the characteristics of human movements. In this paper, we focus on characterizing the human movements of walking and running based on a novel machine learning approach. Since walking and running are human fundamental activities, analyzing their characteristics promptly and automatically during daily smartphone use is particularly valuable. In this paper, we propose a machine learning approach, referred to as ’two-stage latent dynamics modeling and filtering’ (TS-LDMF) method, where we combine a latent space modeling stage with a nonlinear filtering stage, for characterizing individual dynamic walking and running patterns by analyzing smartphone sensor data. For the task of characterizing movements, the proposed method makes use of encoding the high-dimensional sequential data from movements into random variables in a low-dimensional latent space. The use of random variables in the latent space, often called latent variables, is particularly useful, because it is capable of conveying compressed information concerning movements and efficiently handling the uncertainty originating from high-dimensional sequential observation. Our experimental results show that the proposed use of two-stage latent dynamics modeling and filtering yields promising results for characterizing individual dynamic walking and running patterns. Full article
(This article belongs to the Special Issue Wearable Sensors in Healthcare: Methods, Algorithms, Applications)
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Open AccessArticle
Using Wearable and Non-Invasive Sensors to Measure Swallowing Function: Detection, Verification, and Clinical Application
Sensors 2019, 19(11), 2624; https://doi.org/10.3390/s19112624 - 09 Jun 2019
Abstract
Background: A widely used method for assessing swallowing dysfunction is the videofluoroscopic swallow study (VFSS) examination. However, this method has a risk of radiation exposure. Therefore, using wearable, non-invasive and radiation-free sensors to assess swallowing function has become a research trend. This study [...] Read more.
Background: A widely used method for assessing swallowing dysfunction is the videofluoroscopic swallow study (VFSS) examination. However, this method has a risk of radiation exposure. Therefore, using wearable, non-invasive and radiation-free sensors to assess swallowing function has become a research trend. This study addresses the use of a surface electromyography sensor, a nasal airflow sensor, and a force sensing resistor sensor to monitor the coordination of respiration and larynx movement which are considered the major indicators of the swallowing function. The demand for an autodetection program that identifies the swallowing patterns from multiple sensors is raised. The main goal of this study is to show that the sensor-based measurement using the proposed detection program is able to detect early-stage swallowing disorders, which specifically, are useful for the assessment of the coordination between swallowing and respiration. Methods: Three sensors were used to collect the signals from submental muscle, nasal cavity, and thyroid cartilage, respectively, during swallowing. An analytic swallowing model was proposed based on these sensors. A set of temporal parameters related to the swallowing events in this model were defined and measured by an autodetection algorithm. The verification of this algorithm was accomplished by comparing the results from the sensors with the results from the VFSS. A clinical application of the long-term smoking effect on the swallowing function was detected by the proposed sensors and the program. Results: The verification results showed that the swallowing patterns obtained from the sensors strongly correlated with the laryngeal movement monitored from the VFSS. The temporal parameters measured from these two methods had insignificant delays which were all smaller than 0.03 s. In the smoking effect application, this study showed that the differences between the swallowing function of smoking and nonsmoking participants, as well as their disorders, is revealed by the sensor-based method without the VFSS examination. Conclusions: This study showed that the sensor-based non-invasive measurement with the proposed detection algorithm is a viable method for temporal parameter measurement of the swallowing function. Full article
(This article belongs to the Special Issue Wearable Sensors in Healthcare: Methods, Algorithms, Applications)
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Open AccessArticle
Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study
Sensors 2019, 19(8), 1849; https://doi.org/10.3390/s19081849 - 18 Apr 2019
Cited by 1
Abstract
The negative effects of mental stress on human health has been known for decades. High-level stress must be detected at early stages to prevent these negative effects. After the emergence of wearable devices that could be part of our lives, researchers have started [...] Read more.
The negative effects of mental stress on human health has been known for decades. High-level stress must be detected at early stages to prevent these negative effects. After the emergence of wearable devices that could be part of our lives, researchers have started detecting extreme stress of individuals with them during daily routines. Initial experiments were performed in laboratory environments and recently a number of works took a step outside the laboratory environment to the real-life. We developed an automatic stress detection system using physiological signals obtained from unobtrusive smart wearable devices which can be carried during the daily life routines of individuals. This system has modality-specific artifact removal and feature extraction methods for real-life conditions. We further tested our system in a real-life setting with collected physiological data from 21 participants of an algorithmic programming contest for nine days. This event had lectures, contests as well as free time. By using heart activity, skin conductance and accelerometer signals, we successfully discriminated contest stress, relatively higher cognitive load (lecture) and relaxed time activities by using different machine learning methods. Full article
(This article belongs to the Special Issue Wearable Sensors in Healthcare: Methods, Algorithms, Applications)
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Open AccessArticle
Estimation of the Knee Adduction Moment and Joint Contact Force during Daily Living Activities Using Inertial Motion Capture
Sensors 2019, 19(7), 1681; https://doi.org/10.3390/s19071681 - 09 Apr 2019
Abstract
Knee osteoarthritis is a major cause of pain and disability in the elderly population with many daily living activities being difficult to perform as a result of this disease. The present study aimed to estimate the knee adduction moment and tibiofemoral joint contact [...] Read more.
Knee osteoarthritis is a major cause of pain and disability in the elderly population with many daily living activities being difficult to perform as a result of this disease. The present study aimed to estimate the knee adduction moment and tibiofemoral joint contact force during daily living activities using a musculoskeletal model with inertial motion capture derived kinematics in an elderly population. Eight elderly participants were instrumented with 17 inertial measurement units, as well as 53 opto-reflective markers affixed to anatomical landmarks. Participants performed stair ascent, stair descent, and sit-to-stand movements while both motion capture methods were synchronously recorded. A musculoskeletal model containing 39 degrees-of-freedom was used to estimate the knee adduction moment and tibiofemoral joint contact force. Strong to excellent Pearson correlation coefficients were found for the IMC-derived kinematics across the daily living tasks with root mean square errors (RMSE) between 3° and 7°. Furthermore, moderate to strong Pearson correlation coefficients were found in the knee adduction moment and tibiofemoral joint contact forces with RMSE between 0.006–0.014 body weight × body height and 0.4 to 1 body weights, respectively. These findings demonstrate that inertial motion capture may be used to estimate knee adduction moments and tibiofemoral contact forces with comparable accuracy to optical motion capture. Full article
(This article belongs to the Special Issue Wearable Sensors in Healthcare: Methods, Algorithms, Applications)
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Open AccessArticle
Fetal Heart Rate Monitoring Implemented by Dynamic Adaptation of Transmission Power of a Flexible Ultrasound Transducer Array
Sensors 2019, 19(5), 1195; https://doi.org/10.3390/s19051195 - 08 Mar 2019
Abstract
Fetal heart rate (fHR) monitoring using Doppler Ultrasound (US) is a standard method to assess fetal health before and during labor. Typically, an US transducer is positioned on the maternal abdomen and directed towards the fetal heart. Due to fetal movement or displacement [...] Read more.
Fetal heart rate (fHR) monitoring using Doppler Ultrasound (US) is a standard method to assess fetal health before and during labor. Typically, an US transducer is positioned on the maternal abdomen and directed towards the fetal heart. Due to fetal movement or displacement of the transducer, the relative fetal heart location (fHL) with respect to the US transducer can change, leading to frequent periods of signal loss. Consequently, frequent repositioning of the US transducer is required, which is a cumbersome task affecting clinical workflow. In this research, a new flexible US transducer array is proposed which allows for measuring the fHR independently of the fHL. In addition, a method for dynamic adaptation of the transmission power of this array is introduced with the aim of reducing the total acoustic dose transmitted to the fetus and the associated power consumption, which is an important requirement for application in an ambulatory setting. The method is evaluated using an in-vitro setup of a beating chicken heart. We demonstrate that the signal quality of the Doppler signal acquired with the proposed method is comparable to that of a standard, clinical US transducer. At the same time, our transducer array is able to measure the fHR for varying fHL while only using 50% of the total transmission power of standard, clinical US transducers. Full article
(This article belongs to the Special Issue Wearable Sensors in Healthcare: Methods, Algorithms, Applications)
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