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Wearable Sensors and Technology for Human Health 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 (15 February 2024) | Viewed by 17704

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Dr. Dhruv R. Seshadri

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Guest Editor

Sports Medicine Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
Interests: wearable tech; digital health; bioelectronics
Special Issues, Collections and Topics in MDPI journals

Dr. Ethan Robert Harlow

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Guest Editor

Department of Orthopaedic Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
Interests: wearable tech; digital health; orthopedics; sports medicine; big data; signal processing
Special Issues, Collections and Topics in MDPI journals

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Dear Colleagues,

The interest in measuring human performance through wearable technology has dramatically increased as the technology continues to advance. The driving force of this growing field is the ability to carry out individualized measurements across multidisciplinary performance areas and diverse populations of people that provide meaningful real-time feedback to optimize performance and monitor wellbeing. There is tremendous potential for this technology and many modes in which it can be applied to better inform clinical decision making and modulate training to prevent poor outcomes. One additional area of interest around wearable technology is in the early detection of injury, which is secondary to both musculoskeletal and systemic physiologic insults. The groups at highest risk for injury and those that may clinically benefit are athletes and military personnel. At present, current strategies to identify precursors and at-risk performance behavior for injury are limited to subjective assessments and crude examination tools. Wearable technology has promising utility in advancing this field as it can harness continuous data from multiple physiologic somatic systems and can incorporate additional cognitive and emotional inputs, all of which likely play a role in the complex and dynamic system that leads to injury.

Dr. Dhruv R. Seshadri
Dr. Ethan Robert Harlow
Guest Editors

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Keywords

wearable technology
sensors
bioelectronics
flexible electronics
human performance
injury prevention
physiological monitoring
remote monitoring
digital biomarkers
digital therapeutics

Published Papers (6 papers)

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15 pages, 2598 KiB

Open AccessArticle

WATCH-BPM—Comparison of a WATCH-Type Blood Pressure Monitor with a Conventional Ambulatory Blood Pressure Monitor and Auscultatory Sphygmomanometry

by Mathini Vaseekaran, Sven Kaese, Dennis Görlich, Marcus Wiemer and Alexander Samol

Sensors 2023, 23(21), 8877; https://doi.org/10.3390/s23218877 - 31 Oct 2023

Viewed by 1664

Abstract

Background: Smart devices that are able to measure blood pressure (BP) are valuable for hypertension or heart failure management using digital technology. Data regarding their diagnostic accuracy in comparison to standard noninvasive measurement in accordance to Riva-Rocci are sparse. This study compared a wearable watch-type oscillometric BP monitor (Omron HeartGuide), a wearable watch-type infrared BP monitor (Smart Wear), a conventional ambulatory BP monitor, and auscultatory sphygmomanometry. Methods: Therefore, 159 consecutive patients (84 male, 75 female, mean age 64.33 ± 16.14 years) performed observed single measurements with the smart device compared to auscultatory sphygmomanometry (n = 109) or multiple measurements during 24 h compared to a conventional ambulatory BP monitor on the upper arm (n = 50). The two BP monitoring devices were simultaneously worn on the same arm throughout the monitoring period. In a subgroup of 50 patients, single measurements were also performed with an additional infrared smart device. Results: The intraclass correlation coefficient (ICC) between the difference and the mean of the oscillometric Omron HeartGuide and the conventional method for the single measurement was calculated for both systole (0.765) and diastole (0.732). This is exactly how the ICC was calculated for the individual mean values calculated over the 24 h long-term measurement of the individual patients for both systole (0.880) and diastole (0.829). The ICC between the infrared device and the conventional method was “bad” for SBP (0.329) and DBP (0.025). Therefore, no further long-term measurements were performed with the infrared device. Conclusion: The Omron HeartGuide device provided comparable BP values to the standard devices for single and long-term measurements. The infrared smart device failed to acquire valid measurement data. Full article

(This article belongs to the Special Issue Wearable Sensors and Technology for Human Health Monitoring)

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13 pages, 3995 KiB

Open AccessArticle

A Novel Force-Sensing Smart Textile: Inserting Silicone-Embedded FBG Sensors into a Knitted Undergarment

by Ka-Po Lee, Joanne Yip, Kit-Lun Yick, Chao Lu, Linyue Lu and Qi-Wen Emma Lei

Sensors 2023, 23(11), 5145; https://doi.org/10.3390/s23115145 - 28 May 2023

Cited by 3 | Viewed by 2347

Abstract

A number of textile-based fiber optic sensors have recently been proposed for the continuous monitoring of vital signs. However, some of these sensors are likely unsuitable for conducting direct measurements on the torso as they lack elasticity and are inconvenient. This project provides a novel method for creating a force-sensing smart textile by inlaying four silicone-embedded fiber Bragg grating sensors into a knitted undergarment. The applied force was determined within 3 N after transferring the Bragg wavelength. The results show that the sensors embedded in the silicone membranes achieved enhanced sensitivity to force, as well as flexibility and softness. Additionally, by assessing the degree of FBG response to a range of standardized forces, the linearity (R²) between the shift in the Bragg wavelength and force was found to be above 0.95, with an ICC of 0.97, when tested on a soft surface. Furthermore, the real-time data acquisition could facilitate the adjustment and monitoring of force during the fitting processes, such as in bracing treatment for adolescent idiopathic scoliosis patients. Nevertheless, the optimal bracing pressure has not yet been standardized. This proposed method could help orthotists to adjust the tightness of brace straps and the location of padding in a more scientific and straightforward way. The output of this project could be further extended to determine ideal bracing pressure levels. Full article

(This article belongs to the Special Issue Wearable Sensors and Technology for Human Health Monitoring)

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18 pages, 4721 KiB

Open AccessArticle

Estimating Scalp Moisture in a Hat Using Wearable Sensors

by Haomin Mao, Shuhei Tsuchida, Tsutomu Terada and Masahiko Tsukamoto

Sensors 2023, 23(10), 4965; https://doi.org/10.3390/s23104965 - 22 May 2023

Viewed by 1565

Abstract

Hair quality is easily affected by the scalp moisture content, and hair loss and dandruff will occur when the scalp surface becomes dry. Therefore, it is essential to monitor scalp moisture content constantly. In this study, we developed a hat-shaped device equipped with wearable sensors that can continuously collect scalp data in daily life for estimating scalp moisture with machine learning. We established four machine learning models, two based on learning with non-time-series data and two based on learning with time-series data collected by the hat-shaped device. Learning data were obtained in a specially designed space with a controlled environmental temperature and humidity. The inter-subject evaluation showed a Mean Absolute Error (MAE) of 8.50 using Support Vector Machine (SVM) with 5-fold cross-validation with 15 subjects. Moreover, the intra-subject evaluation showed an average MAE of 3.29 in all subjects using Random Forest (RF). The achievement of this study is using a hat-shaped device with cheap wearable sensors attached to estimate scalp moisture content, which avoids the purchase of a high-priced moisture meter or a professional scalp analyzer for individuals. Full article

(This article belongs to the Special Issue Wearable Sensors and Technology for Human Health Monitoring)

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18 pages, 11175 KiB

Open AccessArticle

Wearable Fabric Loop Sensor Based on Magnetic-Field-Induced Conductivity for Simultaneous Detection of Cardiac Activity and Respiration Signals

by Hyun-Seung Cho, Jin-Hee Yang, Sang-Yeob Lee, Jeong-Whan Lee and Joo-Hyeon Lee

Sensors 2022, 22(24), 9884; https://doi.org/10.3390/s22249884 - 15 Dec 2022

Cited by 3 | Viewed by 1645

Abstract

In this study, a noncontact fabric loop sensor based on magnetic-field-induced conductivity, which can simultaneously detect cardiac activity and respiration signals, was developed and the effects of the sensor’s shape and measurement position on the sensing performance were analyzed. Fifteen male subjects in their twenties wore sleeveless shirts equipped with various types of fabric loop sensors (spiky, extrusion, and spiral), and the cardiac activity and respiratory signals were measured twice at positions P2, P4, and P6. The measurements were verified by comparing them against the reference electrocardiogram (ECG) and respiratory signals measured using BIOPAC^® (MP150, ECG100B, RSP100C). The waveforms of the raw signal measured by the fabric loop sensor were filtered with a bandpass filter (1–20 Hz) and qualitatively compared with the ECG signal obtained from the Ag/AgCI electrode. Notwithstanding a slight difference in performance, the three fabric sensors could simultaneously detect cardiac activity and respiration signals at all measurement positions. In addition, it was verified through statistical analysis that the highest-quality signal was obtained at the measurement position of P4 or P6 using the spiral loop sensor. Full article

(This article belongs to the Special Issue Wearable Sensors and Technology for Human Health Monitoring)

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22 pages, 1956 KiB

Open AccessSystematic Review

The Use of Sensors to Prevent, Predict Transition to Chronic and Personalize Treatment of Low Back Pain: A Systematic Review

by Pablo Herrero, Izarbe Ríos-Asín, Diego Lapuente-Hernández, Luis Pérez, Sandra Calvo and Marina Gil-Calvo

Sensors 2023, 23(18), 7695; https://doi.org/10.3390/s23187695 - 6 Sep 2023

Viewed by 1095

Abstract

Non-specific low back pain (NSLBP) is a highly prevalent condition that implies substantial expenses and affects quality of life in terms of occupational and recreational activities, physical and psychological health, and general well-being. The diagnosis and treatment are challenging processes due to the unknown underlying causes of the condition. Recently, sensors have been included in clinical practice to implement its management. In this review, we furthered knowledge about the potential benefits of sensors such as force platforms, video systems, electromyography, or inertial measure systems in the assessment process of NSLBP. We concluded that sensors could identify specific characteristics of this population like impaired range of movement, decreased stability, or disturbed back muscular activation. Sensors could provide sufferers with earlier diagnosis, prevention strategies to avoid chronic transition, and more efficient treatment approaches. Nevertheless, the review has limitations that need to be considered in the interpretation of results. Full article

(This article belongs to the Special Issue Wearable Sensors and Technology for Human Health Monitoring)

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16 pages, 3875 KiB

Open AccessSystematic Review

Wearable Sensor Technology to Predict Core Body Temperature: A Systematic Review

by Conor M. Dolson, Ethan R. Harlow, Dermot M. Phelan, Tim J. Gabbett, Benjamin Gaal, Christopher McMellen, Benjamin J. Geletka, Jacob G. Calcei, James E. Voos and Dhruv R. Seshadri

Sensors 2022, 22(19), 7639; https://doi.org/10.3390/s22197639 - 9 Oct 2022

Cited by 17 | Viewed by 8263

Abstract

Heat-related illnesses, which range from heat exhaustion to heatstroke, affect thousands of individuals worldwide every year and are characterized by extreme hyperthermia with the core body temperature (CBT) usually > 40 °C, decline in physical and athletic performance, CNS dysfunction, and, eventually, multiorgan failure. The measurement of CBT has been shown to predict heat-related illness and its severity, but the current measurement methods are not practical for use in high acuity and high motion settings due to their invasive and obstructive nature or excessive costs. Noninvasive predictions of CBT using wearable technology and predictive algorithms offer the potential for continuous CBT monitoring and early intervention to prevent HRI in athletic, military, and intense work environments. Thus far, there has been a lack of peer-reviewed literature assessing the efficacy of wearable devices and predictive analytics to predict CBT to mitigate heat-related illness. This systematic review identified 20 studies representing a total of 25 distinct algorithms to predict the core body temperature using wearable technology. While a high accuracy in prediction was noted, with 17 out of 18 algorithms meeting the clinical validity standards. few algorithms incorporated individual and environmental data into their core body temperature prediction algorithms, despite the known impact of individual health and situational and environmental factors on CBT. Robust machine learning methods offer the ability to develop more accurate, reliable, and personalized CBT prediction algorithms using wearable devices by including additional data on user characteristics, workout intensity, and the surrounding environment. The integration and interoperability of CBT prediction algorithms with existing heat-related illness prevention and treatment tools, including heat indices such as the WBGT, athlete management systems, and electronic medical records, will further prevent HRI and increase the availability and speed of data access during critical heat events, improving the clinical decision-making process for athletic trainers and physicians, sports scientists, employers, and military officers. Full article

(This article belongs to the Special Issue Wearable Sensors and Technology for Human Health Monitoring)

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