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Advanced Wearable Sensors Technologies for Healthcare Monitoring

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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 (25 February 2024) | Viewed by 25163

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Prof. Dr. Toshiyo Tamura

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Guest Editor

Future Robotics Organization, Waseda University, Bldg.# 120-5, Room 202, 513 Waseda-Tsurumaki, Shinjuku-ku, Tokyo 162-0041, Japan
Interests: bio-instrumentation; bio-signal interpretation; assistive device; rehabilitation engineering; wearable and unobstructive sensor; regulatory science; standards
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Special Issue Information

Dear Colleagues,

Wearable sensor technologies are rapidly evolving and extending their reach to critical applications of wellness and healthcare. The progress is driven by advances in sensor technology, computing, wireless communications, signal processing, and pattern recognition. Wearable technologies allow extension of the monitoring into the community and have been used in many research and clinical applications, including monitoring of healthy, elderly and frail individuals, individuals with neurological disorders (stroke, Parkinson’s disease, etc.), measuring levels of physical activity in disease-association studies and developing behavioral interventions.

The goal of this Special Issue is to highlight state of the art applications of wearable sensors with focus on wellness and healthcare applications of the technology. We sincerely invite you to submit original unpublished work on the listed or related topics.

Papers are solicited in, but are not limited to, the following and related topics:

New sensor materials and technologies for medical applications;
Wearable and implantable sensors for biomedical applications;
Sensing systems for healthcare;
Sensors and Systems for Physical Rehabilitation;
Wearable sensors;
Connected Sensors for the Internet of Things;
Physical activity;
Activity monitoring;
Emotion prediction;
Stress detection;
Fatigue detection;
Fall detection;
Sport-related activity monitoring;
Health monitoring;
Pervasive healthcare;
Physiological sensors.

Prof. Dr. Toshiyo Tamura
Guest Editor

Manuscript Submission Information

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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

new sensor materials and technologies for medical applications
wearable and implantable sensors for biomedical applications
sensing systems for healthcare
sensors and systems for physical rehabilitation
wearable sensors
connected sensors for the Internet of Things
physical activity
activity monitoring
emotion prediction
stress detection
fatigue detection
fall detection
sport-related activity monitoring
health monitoring
pervasive healthcare
physiological sensors

Published Papers (10 papers)

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Research

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16 pages, 3738 KiB

Open AccessArticle

Concurrent Supra-Postural Auditory–Hand Coordination Task Affects Postural Control: Using Sonification to Explore Environmental Unpredictability in Factors Affecting Fall Risk

by Dobromir Dotov, Ariel Motsenyat and Laurel J. Trainor

Sensors 2024, 24(6), 1994; https://doi.org/10.3390/s24061994 - 21 Mar 2024

Viewed by 576

Abstract

Clinical screening tests for balance and mobility often fall short of predicting fall risk. Cognitive distractors and unpredictable external stimuli, common in busy natural environments, contribute to this risk, especially in older adults. Less is known about the effects of upper sensory–motor coordination, such as coordinating one’s hand with an external stimulus. We combined movement sonification and affordable inertial motion sensors to develop a task for the precise measurement and manipulation of full-body interaction with stimuli in the environment. In a double-task design, we studied how a supra-postural activity affected quiet stance. The supra-postural task consisted of rhythmic synchronization with a repetitive auditory stimulus. The stimulus was attentionally demanding because it was being modulated continuously. The participant’s hand movement was sonified in real time, and their goal was to synchronize their hand movement with the stimulus. In the unpredictable condition, the tempo changed at random points in the trial. A separate sensor recorded postural fluctuations. Young healthy adults were compared to older adult (OA) participants without known risk of falling. The results supported the hypothesis that supra-postural coordination would entrain postural control. The effect was stronger in OAs, supporting the idea that diminished reserve capacities reduce the ability to isolate postural control from sensory–motor and cognitive activity. Full article

(This article belongs to the Special Issue Advanced Wearable Sensors Technologies for Healthcare Monitoring)

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12 pages, 1422 KiB

Open AccessArticle

Assessing the Validity of the Ergotex IMU in Joint Angle Measurement: A Comparative Study with Optical Tracking Systems

by Jose M. Jimenez-Olmedo, Juan Tortosa-Martínez, Juan M. Cortell-Tormo and Basilio Pueo

Sensors 2024, 24(6), 1903; https://doi.org/10.3390/s24061903 - 16 Mar 2024

Cited by 1 | Viewed by 529

Abstract

An observational, repeated measures design was used in this study to assess the validity of the Ergotex Inertial Measurement Unit (IMU) against a 3D motion capture system for measuring trunk, hip, and shoulder angles in ten healthy adult males (38.8 ± 7.3 y, bodyweight 79.2 ± 115.9 kg, body height 179.1 ± 8.1 cm). There were minimal systematic differences between the devices, with the most significant discrepancy being 1.4 degrees for the 80-degree target angle, denoting Ergotex’s precision in joint angle measurements. These results were statistically significant (p < 0.001), with predominantly trivial to small effect sizes, indicating high accuracy for clinical and biomechanical applications. Bland–Altman analysis showed Limits of Agreement (LoA) approximately ±2.5 degrees across all angles and positions, with overall LoA ranging from 3.6 to −2.4 degrees, reflecting Ergotex’s consistent performance. Regression analysis indicated uniform variance across measurements, with minor heteroscedastic errors producing a negligible underestimation trend of around 0.5 degrees in some instances. In conclusion, the Ergotex IMU is a reliable tool for accurate joint angle measurements. It offers a practical and cost-effective alternative to more complex systems, particularly in settings where precise measurement is essential. Full article

(This article belongs to the Special Issue Advanced Wearable Sensors Technologies for Healthcare Monitoring)

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18 pages, 2935 KiB

Open AccessArticle

Approach for Non-Intrusive Detection of the Fit of Orthopaedic Devices Based on Vibrational Data

by Constanze Neupetsch, Eric Hensel, Andreas Heinke, Tom Stapf, Nico Stecher, Hagen Malberg, Christoph-Eckhard Heyde and Welf-Guntram Drossel

Sensors 2023, 23(14), 6500; https://doi.org/10.3390/s23146500 - 18 Jul 2023

Viewed by 1003

Abstract

The soft tissues of residual limb amputees are subject to large volume fluctuations over the course of a day. Volume fluctuations in residual limbs can lead to local pressure marks, causing discomfort, pain and rejection of prostheses. Existing methods for measuring interface stress encounter several limitations. A major problem is that the measurement instrumentation is applied in the sensitive interface between the prosthesis and residual limb. This paper presents the principle investigation of a non-intrusive technique to evaluate the fit of orthopaedic prosthesis sockets in transfemoral amputees based on experimentally obtained vibrational data. The proposed approach is based on changes in the dynamical behaviour detectable at the outer surface of prostheses; thus, the described interface is not affected. Based on the experimental investigations shown and the derived results, it can be concluded that structural dynamic measurements are a promising non-intrusive technique to evaluate the fit of orthopaedic prosthesis sockets in transfemoral amputee patients. The obtained resonance frequency changes of 2% are a good indicator of successful applicabilityas these changes can be detected without the need for complex measurement devices. Full article

(This article belongs to the Special Issue Advanced Wearable Sensors Technologies for Healthcare Monitoring)

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36 pages, 17376 KiB

Open AccessArticle

Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar—A Feasibility Study

by Nastassia Vysotskaya, Christoph Will, Lorenzo Servadei, Noah Maul, Christian Mandl, Merlin Nau, Jens Harnisch and Andreas Maier

Sensors 2023, 23(8), 4111; https://doi.org/10.3390/s23084111 - 19 Apr 2023

Cited by 3 | Viewed by 3697

Abstract

Blood pressure monitoring is of paramount importance in the assessment of a human’s cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitations—it only provides a static blood pressure value pair, is incapable of capturing blood pressure variations over time, is inaccurate, and causes discomfort upon use. This work presents a radar-based approach that utilizes the movement of the skin due to artery pulsation to extract pressure waves. From those waves, a set of 21 features was collected and used—together with the calibration parameters of age, gender, height, and weight—as input for a neural network-based regression model. After collecting data from 55 subjects from radar and a blood pressure reference device, we trained 126 networks to analyze the developed approach’s predictive power. As a result, a very shallow network with just two hidden layers produced a systolic error of

9.2 \pm 8.3

mmHg (mean error ± standard deviation) and a diastolic error of

7.7 \pm 5.7

mmHg. While the trained model did not reach the requirements of the AAMI and BHS blood pressure measuring standards, optimizing network performance was not the goal of the proposed work. Still, the approach has displayed great potential in capturing blood pressure variation with the proposed features. The presented approach therefore shows great potential to be incorporated into wearable devices for continuous blood pressure monitoring for home use or screening applications, after improving this approach even further. Full article

(This article belongs to the Special Issue Advanced Wearable Sensors Technologies for Healthcare Monitoring)

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24 pages, 7454 KiB

Open AccessArticle

Characterisation and Quantification of Upper Body Surface Motions for Tidal Volume Determination in Lung-Healthy Individuals

by Bernhard Laufer, Fabian Hoeflinger, Paul D. Docherty, Nour Aldeen Jalal, Sabine Krueger-Ziolek, Stefan J. Rupitsch, Leonhard Reindl and Knut Moeller

Sensors 2023, 23(3), 1278; https://doi.org/10.3390/s23031278 - 22 Jan 2023

Cited by 5 | Viewed by 1685

Abstract

Measurement of accurate tidal volumes based on respiration-induced surface movements of the upper body would be valuable in clinical and sports monitoring applications, but most current methods lack the precision, ease of use, or cost effectiveness required for wide-scale uptake. In this paper, the theoretical ability of different sensors, such as inertial measurement units, strain gauges, or circumference measurement devices to determine tidal volumes were investigated, scrutinised and evaluated. Sixteen subjects performed different breathing patterns of different tidal volumes, while using a motion capture system to record surface motions and a spirometer as a reference to obtain tidal volumes. Subsequently, the motion-capture data were used to determine upper-body circumferences, tilt angles, distance changes, movements and accelerations—such data could potentially be measured using optical encoders, inertial measurement units, or strain gauges. From these parameters, the measurement range and correlation with the volume signal of the spirometer were determined. The highest correlations were found between the spirometer volume and upper body circumferences; surface deflection was also well correlated, while accelerations carried minor respiratory information. The ranges of thorax motion parameters measurable with common sensors and the values and correlations to respiratory volume are presented. This article thus provides a novel tool for sensor selection for a smart shirt analysis of respiration. Full article

(This article belongs to the Special Issue Advanced Wearable Sensors Technologies for Healthcare Monitoring)

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12 pages, 3887 KiB

Open AccessArticle

An Advanced Internet of Things System for Heatstroke Prevention with a Noninvasive Dual-Heat-Flux Thermometer

by Toshiyo Tamura, Ming Huang, Takumi Yoshimura, Shinjiro Umezu and Toru Ogata

Sensors 2022, 22(24), 9985; https://doi.org/10.3390/s22249985 - 18 Dec 2022

Cited by 2 | Viewed by 2057

Abstract

Heatstroke is a concern during sudden heat waves. We designed and prototyped an Internet of Things system for heatstroke prevention, which integrates physiological information, including deep body temperature (DBT), based on the dual-heat-flux method. A dual-heat-flux thermometer developed to monitor DBT in real-time was also evaluated. Real-time readings from the thermometer are stored on a cloud platform and processed by a decision rule, which can alert the user to heatstroke. Although the validation of the system is ongoing, its feasibility is demonstrated in a preliminary experiment. Full article

(This article belongs to the Special Issue Advanced Wearable Sensors Technologies for Healthcare Monitoring)

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18 pages, 11849 KiB

Open AccessArticle

Ground-Reaction-Force-Based Gait Analysis and Its Application to Gait Disorder Assessment: New Indices for Quantifying Walking Behavior

by Ji Su Park and Choong Hyun Kim

Sensors 2022, 22(19), 7558; https://doi.org/10.3390/s22197558 - 06 Oct 2022

Cited by 1 | Viewed by 2301

Abstract

Gait assessment is an important tool for determining whether a person has a gait disorder. Existing gait analysis studies have a high error rate due to the heel-contact-event-based technique. Our goals were to overcome the shortcomings of existing gait analysis techniques and to develop more objective indices for assessing gait disorders. This paper proposes a method for assessing gait disorders via the observation of changes in the center of pressure (COP) in the medial–lateral direction, i.e., COPx, during the gait cycle. The data for the COPx were used to design a gait cycle estimation method applicable to patients with gait disorders. A polar gaitogram was drawn using the gait cycle and COPx data. The difference between the areas inside the two closed curves in the polar gaitogram, area ratio index (ARI), and the slope of the tangential line common to the two closed curves were proposed as gait analysis indices. An experimental study was conducted to verify that these two indices can be used to differentiate between stroke patients and healthy adults. The findings indicated the potential of using the proposed polar gaitogram and indices to develop and apply wearable devices to assess gait disorders. Full article

(This article belongs to the Special Issue Advanced Wearable Sensors Technologies for Healthcare Monitoring)

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12 pages, 4863 KiB

Open AccessArticle

Muscle Engagement Monitoring Using Self-Adhesive Elastic Nanocomposite Fabrics

by Yun-An Lin, Yash Mhaskar, Amy Silder, Pinata H. Sessoms, John J. Fraser and Kenneth J. Loh

Sensors 2022, 22(18), 6768; https://doi.org/10.3390/s22186768 - 07 Sep 2022

Cited by 2 | Viewed by 2425

Abstract

Insight into, and measurements of, muscle contraction during movement may help improve the assessment of muscle function, quantification of athletic performance, and understanding of muscle behavior, prior to and during rehabilitation following neuromusculoskeletal injury. A self-adhesive, elastic fabric, nanocomposite, skin-strain sensor was developed and validated for human movement monitoring. We hypothesized that skin-strain measurements from these wearables would reveal different degrees of muscle engagement during functional movements. To test this hypothesis, the strain sensing properties of the elastic fabric sensors, especially their linearity, stability, repeatability, and sensitivity, were first verified using load frame tests. Human subject tests conducted in parallel with optical motion capture confirmed that they can reliably measure tensile and compressive skin-strains across the calf and tibialis anterior. Then, a pilot study was conducted to assess the correlation of skin-strain measurements with surface electromyography (sEMG) signals. Subjects did biceps curls with different weights, and the responses of the elastic fabric sensors worn over the biceps brachii and flexor carpi radialis (i.e., forearm) were well-correlated with sEMG muscle engagement measures. These nanocomposite fabric sensors were validated for monitoring muscle engagement during functional activities and did not suffer from the motion artifacts typically observed when using sEMGs in free-living community settings. Full article

(This article belongs to the Special Issue Advanced Wearable Sensors Technologies for Healthcare Monitoring)

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14 pages, 5066 KiB

Open AccessArticle

Adaptive Motion Artifact Reduction in Wearable ECG Measurements Using Impedance Pneumography Signal

by Xiang An, Yanzhong Liu, Yixin Zhao, Sichao Lu, George K. Stylios and Qiang Liu

Sensors 2022, 22(15), 5493; https://doi.org/10.3390/s22155493 - 23 Jul 2022

Cited by 9 | Viewed by 2503

Abstract

Noise is a common problem in wearable electrocardiogram (ECG) monitoring systems because the presence of noise can corrupt the ECG waveform causing inaccurate signal interpretation. By comparison with electromagnetic interference and its minimization, the reduction of motion artifact is more difficult and challenging because its time-frequency characteristics are unpredictable. Based on the characteristics of motion artifacts, this work uses adaptive filtering, a specially designed ECG device, and an Impedance Pneumography (IP) data acquisition system to combat motion artifacts. The newly designed ECG-IP acquisition system maximizes signal correlation by measuring both ECG and IP signals simultaneously using the same pair of electrodes. Signal comparison investigations between ECG and IP signals under five different body motions were carried out, and the Pearson Correlation Coefficient |r| was higher than 0.6 in all cases, indicating a good correlation. To optimize the performance of adaptive motion artifact reduction, the IP signal was filtered to a 5 Hz low-pass filter and then fed into a Recursive Least Squares (RLS) adaptive filter as a reference input signal. The performance of the proposed motion artifact reduction method was evaluated subjectively and objectively, and the results proved that the method could suppress the motion artifacts and achieve minimal distortion to the denoised ECG signal. Full article

(This article belongs to the Special Issue Advanced Wearable Sensors Technologies for Healthcare Monitoring)

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Review

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45 pages, 6468 KiB

Open AccessReview

A Comprehensive Review of the Recent Developments in Wearable Sweat-Sensing Devices

by Nur Fatin Adini Ibrahim, Norhayati Sabani, Shazlina Johari, Asrulnizam Abd Manaf, Asnida Abdul Wahab, Zulkarnay Zakaria and Anas Mohd Noor

Sensors 2022, 22(19), 7670; https://doi.org/10.3390/s22197670 - 10 Oct 2022

Cited by 11 | Viewed by 7163

Abstract

Sweat analysis offers non-invasive real-time on-body measurement for wearable sensors. However, there are still gaps in current developed sweat-sensing devices (SSDs) regarding the concerns of mixing fresh and old sweat and real-time measurement, which are the requirements to ensure accurate the measurement of wearable devices. This review paper discusses these limitations by aiding model designs, features, performance, and the device operation for exploring the SSDs used in different sweat collection tools, focusing on continuous and non-continuous flow sweat analysis. In addition, the paper also comprehensively presents various sweat biomarkers that have been explored by earlier works in order to broaden the use of non-invasive sweat samples in healthcare and related applications. This work also discusses the target analyte’s response mechanism for different sweat compositions, categories of sweat collection devices, and recent advances in SSDs regarding optimal design, functionality, and performance. Full article

(This article belongs to the Special Issue Advanced Wearable Sensors Technologies for Healthcare Monitoring)

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