Wearable Sensors and Portable Devices in Healthcare Application

Topic Information

Dear Colleagues,

With the current popularity of home healthcare, people are gradually changing their mindset from therapy to prevention. There is a prospect that detection and intervention at an early stage of the disease can significantly reduce the treatment burden. This relies on long-term monitoring of multiple physiological indicators of the human body. Potential trends through physiological signals are recognized in time before the visible evidence of condition worsening. Compared with the traditional medical model, wearable medical devices have advantages in portability, digitization, and real-time use, promising a wide range of applications.

Wearable sensors and devices such as handheld devices, head-mounted devices, smart bracelets, smart clothing, smart jewelry, and portable patch sensors ought to be comfortable, small, stable, safe, efficient, and friendly to interact with. They provide long-term self-health management, for example, through the monitoring of blood pressure, blood glucose, blood lipid, heart rate, electrocardio, electroencephalogram, electromyography, pulse, respiration, sleep rhythm, and other biomedical signals, combined with the corresponding algorithm to assess health status and medical diagnosis.

This Topic aims to provide communication on new sensor materials, measurement methods, device structures, and physiological indicator derivation algorithms for health applications. Potential topics include but are not limited to the following:

• Smart wearable devices
• Sensing mechanisms
• Algorithms for physiological indicators
• Signal processing
• Integrated applications

Dr. Weiting Liu
Prof. Dr. Dajing Chen
Prof. Dr. Lei Ren
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Healthcare healthcare	2.4	3.5	2013	20.3 Days	CHF 2700
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600
Journal of Functional Biomaterials jfb	5.0	4.6	2010	16.6 Days	CHF 2700

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Published Papers (5 papers)

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17 pages, 828 KiB  

Open AccessSystematic Review

Update on the Use of Infrared Thermography in the Early Detection of Diabetic Foot Complications: A Bibliographic Review

by Marina Faus Camarena, Marta Izquierdo-Renau, Iván Julian-Rochina, Manel Arrébola and Manuel Miralles

Sensors 2024, 24(1), 252; https://doi.org/10.3390/s24010252 - 31 Dec 2023

Cited by 6 | Viewed by 3447

Abstract

Foot lesions are among the most frequent causes of morbidity and disability in the diabetic population. Thus, the exploration of preventive control measures is vital for detecting early signs and symptoms of this disease. Infrared thermography is one of the complementary diagnostic tools [...] Read more.

Foot lesions are among the most frequent causes of morbidity and disability in the diabetic population. Thus, the exploration of preventive control measures is vital for detecting early signs and symptoms of this disease. Infrared thermography is one of the complementary diagnostic tools available that has proven to be effective in the control of diabetic foot. The last review on this topic was published in 2015 and so, we conducted a bibliographic review of the main databases (PubMed, the Web of Science, Cochrane library, and Scopus) during the third quarter of 2023. We aimed to identify the effectiveness of infrared thermography as a diagnostic element in pre-ulcerous states in diabetic patients and to detect diabetic foot ulcer complications. We obtained a total of 1199 articles, 26 of which were finally included in the present review and published after 2013. After analyzing the use of infrared thermography in diabetic patients both with and without ulcers, as well as in healthy individuals, we concluded that is an effective tool for detecting early-stage ulcers in diabetic foot patients. Full article

(This article belongs to the Topic Wearable Sensors and Portable Devices in Healthcare Application)

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13 pages, 2383 KiB  

Open AccessArticle

Decreased Visual Search Behavior in Elderly Drivers during the Early Phase of Reverse Parking, But an Increase during the Late Phase

by Siyeong Kim, Ken Kondo, Naoto Noguchi, Ryoto Akiyama, Yoko Ibe, Yeongae Yang and Bumsuk Lee

Sensors 2023, 23(23), 9555; https://doi.org/10.3390/s23239555 - 1 Dec 2023

Viewed by 1878

Abstract

The aim of this study was to assess the characteristics of visual search behavior in elderly drivers in reverse parking. Fourteen healthy elderly and fourteen expert drivers performed a perpendicular parking task. The parking process was divided into three consecutive phases (Forward, Reverse, [...] Read more.

The aim of this study was to assess the characteristics of visual search behavior in elderly drivers in reverse parking. Fourteen healthy elderly and fourteen expert drivers performed a perpendicular parking task. The parking process was divided into three consecutive phases (Forward, Reverse, and Straighten the wheel) and the visual search behavior was monitored using an eye tracker (Tobii Pro Glasses 2). In addition, driving-related tests and quality of life were evaluated in elderly drivers. As a result, elderly drivers had a shorter time of gaze at the vertex of the parking space both in direct vision and reflected in the driver-side mirror during the Forward and the Reverse phases. In contrast, they had increased gaze time in the passenger-side mirror in the Straighten the wheel phase. Multiple regression analysis revealed that quality of life could be predicted by the total gaze time in the Straighten the wheel phase (β = −0.45), driving attitude (β = 0.62), and driving performance (β = 0.58); the adjusted R² value was 0.87. These observations could improve our understanding of the characteristics of visual search behavior in parking performance and how this behavior is related to quality of life in elderly drivers. Full article

(This article belongs to the Topic Wearable Sensors and Portable Devices in Healthcare Application)

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21 pages, 5357 KiB  

Open AccessArticle

An ECG Signal Acquisition and Analysis System Based on Machine Learning with Model Fusion

by Shi Su, Zhihong Zhu, Shu Wan, Fangqing Sheng, Tianyi Xiong, Shanshan Shen, Yu Hou, Cuihong Liu, Yijin Li, Xiaolin Sun and Jie Huang

Sensors 2023, 23(17), 7643; https://doi.org/10.3390/s23177643 - 3 Sep 2023

Cited by 7 | Viewed by 5452

Abstract

Recently, cardiovascular disease has become the leading cause of death worldwide. Abnormal heart rate signals are an important indicator of cardiovascular disease. At present, the ECG signal acquisition instruments on the market are not portable and manual analysis is applied in data processing, [...] Read more.

Recently, cardiovascular disease has become the leading cause of death worldwide. Abnormal heart rate signals are an important indicator of cardiovascular disease. At present, the ECG signal acquisition instruments on the market are not portable and manual analysis is applied in data processing, which cannot address the above problems. To solve these problems, this study proposes an ECG acquisition and analysis system based on machine learning. The ECG analysis system responsible for ECG signal classification includes two parts: data preprocessing and machine learning models. Multiple types of models were built for overall classification, and model fusion was conducted. Firstly, traditional models such as logistic regression, support vector machines, and XGBoost were employed, along with feature engineering that primarily included morphological features and wavelet coefficient features. Subsequently, deep learning models, including convolutional neural networks and long short-term memory networks, were introduced and utilized for model fusion classification. The system’s classification accuracy for ECG signals reached 99.13%. Future work will focus on optimizing the model and developing a more portable instrument that can be utilized in the field. Full article

(This article belongs to the Topic Wearable Sensors and Portable Devices in Healthcare Application)

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20 pages, 4682 KiB  

Open AccessArticle

Wearable and Noninvasive Device for Integral Congestive Heart Failure Management in the IoMT Paradigm

by José L. Ausín, Javier Ramos, Antonio Lorido, Pedro Molina and J. Francisco Duque-Carrillo

Sensors 2023, 23(16), 7055; https://doi.org/10.3390/s23167055 - 9 Aug 2023

Cited by 7 | Viewed by 3308

Abstract

Noninvasive remote monitoring of hemodynamic variables is essential in optimizing treatment opportunities and predicting rehospitalization in patients with congestive heart failure. The objective of this study is to develop a wearable bioimpedance-based device, which can provide continuous measurement of cardiac output and stroke [...] Read more.

Noninvasive remote monitoring of hemodynamic variables is essential in optimizing treatment opportunities and predicting rehospitalization in patients with congestive heart failure. The objective of this study is to develop a wearable bioimpedance-based device, which can provide continuous measurement of cardiac output and stroke volume, as well as other physiological parameters for a greater prognosis and prevention of congestive heart failure. The bioimpedance system, which is based on a robust and cost-effective measuring principle, was implemented in a CMOS application specific integrated circuit, and operates as the analog front-end of the device, which has been provided with a radio-frequency section for wireless communication. The operating parameters of the proposed wearable device are remotely configured through a graphical user interface to measure the magnitude and the phase of complex impedances over a bandwidth of 1 kHz to 1 MHz. As a result of this study, a cardiac activity monitor was implemented, and its accuracy was evaluated in 33 patients with different heart diseases, ages, and genders. The proposed device was compared with a well-established technique such as Doppler echocardiography, and the results showed that the two instruments are clinically equivalent. Full article

(This article belongs to the Topic Wearable Sensors and Portable Devices in Healthcare Application)

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19 pages, 8020 KiB  

Open AccessArticle

A Real-Time Evaluation Algorithm for Noncontact Heart Rate Variability Monitoring

by Xiangyu Han, Qian Zhai, Ning Zhang, Xiufeng Zhang, Long He, Min Pan, Bin Zhang and Tao Liu

Sensors 2023, 23(15), 6681; https://doi.org/10.3390/s23156681 - 26 Jul 2023

Cited by 11 | Viewed by 3200

Abstract

Noncontact vital sign monitoring based on radar has attracted great interest in many fields. Heart Rate Variability (HRV), which measures the fluctuation of heartbeat intervals, has been considered as an important indicator for general health evaluation. This paper proposes a new algorithm for [...] Read more.

Noncontact vital sign monitoring based on radar has attracted great interest in many fields. Heart Rate Variability (HRV), which measures the fluctuation of heartbeat intervals, has been considered as an important indicator for general health evaluation. This paper proposes a new algorithm for HRV monitoring in which frequency-modulated continuous-wave (FMCW) radar is used to separate echo signals from different distances, and the beamforming technique is adopted to improve signal quality. After the phase reflecting the chest wall motion is demodulated, the acceleration is calculated to enhance the heartbeat and suppress the impact of respiration. The time interval of each heartbeat is estimated based on the smoothed acceleration waveform. Finally, a joint optimization algorithm was developed and is used to precisely segment the acceleration signal for analyzing HRV. Experimental results from 10 participants show the potential of the proposed algorithm for obtaining a noncontact HRV estimation with high accuracy. The proposed algorithm can measure the interbeat interval (IBI) with a root mean square error (RMSE) of 14.9 ms and accurately estimate HRV parameters with an RMSE of 3.24 ms for MEAN (the average value of the IBI), 4.91 ms for the standard deviation of normal to normal (SDNN), and 9.10 ms for the root mean square of successive differences (RMSSD). These results demonstrate the effectiveness and feasibility of the proposed method in emotion recognition, sleep monitoring, and heart disease diagnosis. Full article

(This article belongs to the Topic Wearable Sensors and Portable Devices in Healthcare Application)

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