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Monitoring of Human Physiological Signals

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 9672

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Special Issue Editor

Prof. Dr. Miran Lee

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Guest Editor

Human-Computer Interaction Lab, Daegu University, Gyeongsan 38453, South Korea
Interests: bio-signal processing (EEC, ECG, EMG, and EOG); pattern recognition; human–robot interaction; robotic mood transition; pain expression; sports medicine

Special Issue Information

Dear Colleagues,

Health systems and their applications utilizing human physiological signals have a significant scope of application. Physiological signals are used to improve the performance of health monitoring and diagnosis systems by integrating complex health data such as electromyogram, electrocardiogram, electroencephalogram, and pulse. In addition, methods for processing multiple physiological signals are evolving into various complex and intelligent methods, such as deep learning and machine learning methods, which enable a smart health management system based on AI.

Prof. Dr. Miran Lee
Guest Editor

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Keywords

healthcare
health management
Internet of Things healthcare
sensor fusion in biomedical imaging
remote sensing in healthcare
diagnostics
health monitoring
biosignal processing

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Published Papers (6 papers)

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Research

27 pages, 10747 KiB

Open AccessArticle

MC-EVM: A Movement-Compensated EVM Algorithm with Face Detection for Remote Pulse Monitoring

by Abdallah Benhamida and Miklos Kozlovszky

Appl. Sci. 2025, 15(3), 1652; https://doi.org/10.3390/app15031652 - 6 Feb 2025

Viewed by 840

Abstract

Automated tasks, mainly in the biomedical field, help to develop new technics to provide faster solutions for monitoring patients’ health status. For instance, they help to measure different types of human bio-signal, perform fast data analysis, and enable overall patient status monitoring. Eulerian Video Magnification (EVM) can reveal small-scale and hidden changes in real life such as color and motion changes that are used to detect actual pulse. However, due to patient movement during the measurement, the EVM process will result in the wrong estimation of the pulse. In this research, we provide a working prototype for effective artefact elimination using a face movement compensated EVM (MC-EVM) which aims to track the human face as the main Region Of Interest (ROI) and then use EVM to estimate the pulse. Our primary contribution lays on the development and training of two face detection models using TensorFlow Lite: the Single-Shot MultiBox Detector (SSD) and the EfficientDet-Lite0 models that are used based on the computational capabilities of the device in use. By employing one of these models, we can crop the face accurately from the video, which is then processed using EVM to estimate the pulse. MC-EVM showed very promising results and ensured robust pulse measurement by effectively mitigating the impact of patient movement. The results were compared and validated against ground-truth data that were made available online and against pre-existing solutions from the state-of-the-art. Full article

(This article belongs to the Special Issue Monitoring of Human Physiological Signals)

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20 pages, 6368 KiB

Open AccessArticle

Effect of a Virtual Biophilic Residential Environment on the Perception and Responses of Seniors

by Eun-Ji Lee, Sung-Jun Park and Joon-Ho Choi

Appl. Sci. 2024, 14(23), 11431; https://doi.org/10.3390/app142311431 - 9 Dec 2024

Viewed by 1018

Abstract

This study investigates the effects of a virtual biophilic residential environment on seniors’ physiological and subjective responses to evaluate its potential to promote healing and recovery. Thirty seniors were exposed to three different scales (units, buildings, complexes) of virtual biophilic residential environments that combined both physical and digital biophilic elements. Physiological responses, including heart rate, heart rate variability, and galvanic skin response, were measured alongside self-reported levels of satisfaction and immersion. The primary objective was to assess the effectiveness of physical and digital design interventions at each residential scale. The findings revealed that the virtual biophilic residential environment reduced physiological stress in seniors, with the most significant impact observed at the unit scale. Digital design interventions further enhance stress relief benefits, indicating that integrating physical and digital elements in biophilic residential environments can positively influence seniors’ stress levels. Additionally, significant correlations were identified between physiological responses and subjective perceptions of immersion and satisfaction. This study is valuable as an initial comparative analysis of the effectiveness of physical and digital approaches in biophilic design. This paper is a preliminary study and is significant in that it systematizes virtual environment research from an age-friendly perspective and expands approaches to biophilic design. Full article

(This article belongs to the Special Issue Monitoring of Human Physiological Signals)

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11 pages, 1355 KiB

Open AccessArticle

Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank

by Jukyung Lee, Hyosung Joo and Jihwan Woo

Appl. Sci. 2024, 14(23), 11107; https://doi.org/10.3390/app142311107 - 28 Nov 2024

Viewed by 1459

Abstract

Remote photoplethysmography (rPPG) is a non-contact technology that monitors heart activity by detecting subtle color changes within the facial blood vessels. It provides an unconstrained and unconscious approach that can be widely applied to health monitoring systems. In recent years, research has been actively conducted to improve rPPG signals and to extract significant information from facial videos. However, rPPG can be vulnerable to degradation due to changes in the illumination and motion of a subject, and overcoming these challenges remains difficult. In this study, we propose a machine learning-based filter bank (MLFB) noise reduction algorithm to improve the quality of rPPG signals. The MLFB algorithm determines the optimal spectral band for extracting information on cardiovascular activity and reconstructing an rPPG signal using a support vector machine. The proposed approach was validated with an open dataset, achieving a 35.5% (i.e., resulting in a mean absolute error of 2.5 beats per minute) higher accuracy than those of conventional methods. The proposed algorithm can be integrated into various rPPG algorithms for the pre-processing of RGB signals. Moreover, its computational efficiency is expected to enable straightforward implementation in system development, making it broadly applicable across the healthcare field. Full article

(This article belongs to the Special Issue Monitoring of Human Physiological Signals)

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19 pages, 4466 KiB

Open AccessArticle

New Web-Based Ventilator Monitoring System Consisting of Central and Remote Mobile Applications in Intensive Care Units

by Kyuseok Kim, Yeonkyeong Kim, Young Sam Kim, Kyu Bom Kim and Su Hwan Lee

Appl. Sci. 2024, 14(15), 6842; https://doi.org/10.3390/app14156842 - 5 Aug 2024

Cited by 1 | Viewed by 2771

Abstract

A ventilator central monitoring system (VCMS) that can efficiently respond to and treat patients’ respiratory issues in intensive care units (ICUs) is critical. Using Internet of Things (IoT) technology without loss or delay in patient monitoring data, clinical staff can overcome spatial constraints in patient respiratory management by integrated monitoring of multiple ventilators and providing real-time information through remote mobile applications. This study aimed to establish a VCMS and assess its effectiveness in an ICU setting. A VCMS comprises central monitoring and mobile applications, with significant real-time information from multiple patient monitors and ventilator devices stored and managed through the VCMS server, establishing an integrated monitoring environment on a web-based platform. The developed VCMS was analyzed in terms of real-time display and data transmission. Twenty-one respiratory physicians and staff members participated in usability and satisfaction surveys on the developed VCMS. The data transfer capacity derived an error of approximately

10^{- 7}

, and the difference in data transmission capacity was approximately

{1.99 \times 10}^{- 7} \pm {9.97 \times 10}^{- 6}

with a 95% confidence interval of

{- 1.16 \times 10}^{- 7}

{5.13 \times 10}^{- 7}

among 18 ventilators and patient monitors. The proposed VCMS could transmit data from various devices without loss of information within the ICU. The medical software validation, consisting of 37 tasks and 9 scenarios, showed a task completion rate of approximately 92%, with a 95% confidence interval of 88.81–90.43. The satisfaction survey consisted of 23 items and showed results of approximately 4.66 points out of 5. These results demonstrated that the VCMS can be readily used by clinical ICU staff, confirming its clinical utility and applicability. The proposed VCMS can help clinical staff quickly respond to the alarm of abnormal events and diagnose and treat based on longitudinal patient data. The mobile applications overcame space constraints, such as isolation to prevent respiratory infection transmission of clinical staff for continuous monitoring of respiratory patients and enabled rapid consultation, ensuring consistent care. Full article

(This article belongs to the Special Issue Monitoring of Human Physiological Signals)

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13 pages, 3523 KiB

Open AccessArticle

Measuring the Effect of Mental Health on Type 2 Diabetes

by Mijin Noh and Yangsok Kim

Appl. Sci. 2024, 14(12), 5184; https://doi.org/10.3390/app14125184 - 14 Jun 2024

Cited by 1 | Viewed by 1288

Abstract

There are many putative risk factors for type 2 diabetes (T2D), and the causal relationship between these factors and diabetes has been established. Socio-environmental and biological approaches are increasingly used to infer causality, and research is needed to understand the causal role of these factors in diabetes risk. Therefore, this study investigated the extent to which the treatment factor of stress induces the risk of diabetes through socio-environmental and biological factors. We present machine learning-based causal inference results generated using DoWhy, a Python library that provides a four-step causal inference method consisting of modeling, identification, estimation, and refutation steps. This study used 253,680 examples collected by the Behavioral Risk Factor Surveillance System (BRFSS), created a causal model based on a socio-environmental model, and tested the statistical significance of the obtained estimates. We identified several causal relationships and attempted various refutations. The results show that mental health problems increase the incidence of diabetes by about 15% (mean value: 0.146). The causal effect was evaluated based on the causal model, and the reliability of causal inference was proved through three refutation tests. Full article

(This article belongs to the Special Issue Monitoring of Human Physiological Signals)

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9 pages, 1613 KiB

Open AccessArticle

Development of Sensory Virtual Reality Interface Using EMG Signal-Based Grip Strength Reflection System

by Younghoon Shin and Miran Lee

Appl. Sci. 2024, 14(11), 4415; https://doi.org/10.3390/app14114415 - 23 May 2024

Viewed by 1666

Abstract

In virtual reality (VR), a factor that can maximize user immersion is the development of an intuitive and sensory interaction method. Physical devices such as controllers or data gloves of existing VR devices are used to control the movement intentions of the user, but their shortfall is that grip strength and detailed muscle strength cannot be reflected. Therefore, this study intended to establish a more sensory VR environment compared to existing methods by reflecting the grip strength of the flexor digitorum profundus of the user of the VR content. In this experiment, the muscle activity of the flexor digitorum profundus was obtained from six subjects based on surface electromyography, and four objects with differing intensity were created within a VR program in which the objects were made to be destroyed depending on muscle activity. As a result, satisfaction was improved because the users could sensitively interact with the objects inside the VR environment, and the intended motion control of the user was reflected in the VR content. Full article

(This article belongs to the Special Issue Monitoring of Human Physiological Signals)

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