Sensors

Research

11 pages, 3630 KiB

Open AccessCommunication

by Jeong-Woo Seo, Jungmi Choi, Kunho Lee and Jaeuk U. Kim

Sensors 2021, 21(23), 7782; https://doi.org/10.3390/s21237782 - 23 Nov 2021

Cited by 6 | Viewed by 2365

Non-invasive measurement of physiological parameters and indicators, specifically among the elderly, is of utmost importance for personal health monitoring. In this study, we focused on photoplethysmography (PPG), and developed a regression model that calculates variables from the second (SDPPG) and third (TDPPG) derivatives [...] Read more.

Non-invasive measurement of physiological parameters and indicators, specifically among the elderly, is of utmost importance for personal health monitoring. In this study, we focused on photoplethysmography (PPG), and developed a regression model that calculates variables from the second (SDPPG) and third (TDPPG) derivatives of the PPG pulse that can observe the inflection point of the pulse wave measured by a wearable PPG device. The PPG pulse at the earlobe was measured for 3 min in 84 elderly Korean women (age: 71.19 ± 6.97 years old). Based on the PPG-based cardiovascular function, we derived additional variables from TDPPG, in addition to the aging variable to predict the age. The Aging Index (AI) from SDPPG and Sum of TDPPG variables were calculated in the second and third differential forms of PPG. The variables that significantly correlated with age were c/a, Tac, AI of SDPPG, sum of TDPPG, and correlation coefficient ‘r’ of the model. In multiple linear regression analysis, the r value of the model was 0.308, and that using deep learning on the model was 0.839. Moreover, the possibility of improving the accuracy of the model using supervised deep learning techniques, rather than the addition of datasets, was confirmed. Full article

(This article belongs to the Special Issue Wearable and Remote Sensing and Monitoring for Personal and Professional Healthcare)

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18 pages, 7834 KiB

Open AccessArticle

Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System

by Wenyu Cai, Dongyang Zhao, Meiyan Zhang, Yinan Xu and Zhu Li

Sensors 2021, 21(18), 6246; https://doi.org/10.3390/s21186246 - 17 Sep 2021

Cited by 8 | Viewed by 2585

Abstract

As the intensity of work increases, many of us sit for long hours while working in the office. It is not easy to sit properly at work all the time and sitting for a long time with wrong postures may cause a series [...] Read more.

As the intensity of work increases, many of us sit for long hours while working in the office. It is not easy to sit properly at work all the time and sitting for a long time with wrong postures may cause a series of health problems as time goes by. In addition, monitoring the sitting posture of patients with spinal disease would be beneficial for their recovery. Accordingly, this paper designs and implements a sitting posture recognition system from a flexible array pressure sensor, which is used to acquire pressure distribution map of sitting hips in a real-time manner. Moreover, an improved self-organizing map-based classification algorithm for six kinds of sitting posture recognition is proposed to identify whether the current sitting posture is appropriate. The extensive experimental results verify that the performance of ISOM-based sitting posture recognition algorithm (ISOM-SPR) in short outperforms that of four kinds of traditional algorithms including decision tree-based (DT), K-means-based (KM), back propagation neural network-based (BP), self-organizing map-based (SOM) sitting posture recognition algorithms. Finally, it is proven that the proposed system based on ISOM-SPR algorithm has good robustness and high accuracy. Full article

(This article belongs to the Special Issue Wearable and Remote Sensing and Monitoring for Personal and Professional Healthcare)

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19 pages, 5853 KiB

Open AccessArticle

Smart Hospital Sensor Network Deployment for Mobile and Remote Healthcare System

by Yoonkyung Jang, Intae Ryoo and Seokhoon Kim

Sensors 2021, 21(16), 5514; https://doi.org/10.3390/s21165514 - 17 Aug 2021

Cited by 5 | Viewed by 2734

Abstract

In this paper, we propose a hospital sensor network deployment method for smart healthcare systems. Since sensor nodes in hospitals are always in an environment where power can be supplied, it is essential to have stable network connectivity by achieving optimal gateway deployment, [...] Read more.

In this paper, we propose a hospital sensor network deployment method for smart healthcare systems. Since sensor nodes in hospitals are always in an environment where power can be supplied, it is essential to have stable network connectivity by achieving optimal gateway deployment, rather than focusing on energy efficiency. The proposed technique leads to an access point (AP) layout that minimizes the overall network operation cost. The operation cost is calculated per unit time, and it includes installation cost and maintenance cost. In addition, group numbers are assigned to sensor nodes for guaranteeing network connectivity, no matter where the mobile sensor devices move. The performance of the proposed methodology has been verified through numerical experiments. Full article

(This article belongs to the Special Issue Wearable and Remote Sensing and Monitoring for Personal and Professional Healthcare)

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24 pages, 7650 KiB

Open AccessArticle

Developing the Next Generation of Augmented Reality Games for Pediatric Healthcare: An Open-Source Collaborative Framework Based on ARCore for Implementing Teaching, Training and Monitoring Applications

by Aida Vidal-Balea, Óscar Blanco-Novoa, Paula Fraga-Lamas and Tiago M. Fernández-Caramés

Sensors 2021, 21(5), 1865; https://doi.org/10.3390/s21051865 - 7 Mar 2021

Cited by 17 | Viewed by 4783

Abstract

Augmented Reality (AR) provides an alternative to the traditional forms of interaction between humans and machines, and facilitates the access to certain technologies to groups of people with special needs like children. For instance, in pediatric healthcare, it is important to help children [...] Read more.

Augmented Reality (AR) provides an alternative to the traditional forms of interaction between humans and machines, and facilitates the access to certain technologies to groups of people with special needs like children. For instance, in pediatric healthcare, it is important to help children to feel comfortable during medical procedures and tests that may be performed on them. To tackle such an issue with the help of AR-based solutions, this article presents the design, implementation and evaluation of a novel open-source collaborative framework that enables to develop teaching, training, and monitoring pediatric healthcare applications. Specifically, such a framework allows for building collaborative applications and shared experiences for AR devices, providing functionalities for connecting with other AR devices and enabling real-time visualization and simultaneous interaction with virtual objects. Since all the communications involved in AR interactions are handled by AR devices, the proposed collaborative framework is able to operate autonomously through a Local Area Network (LAN), thus requiring no cloud or external servers. In order to demonstrate the potential of the proposed framework, a practical use case application is presented. Such an application has been designed to motivate pediatric patients and to encourage them to increase their physical activity through AR games. The presented games do not require any previous configuration, as they use ARCore automatic surface detection technology. Moreover, the AR mobile gaming framework allows multiple players to engage in the same AR experience, so children can interact and collaborate among them sharing the same AR content. In addition, the proposed AR system provides a remote web application that is able to collect and to visualize data on patient use, aiming to provide healthcare professionals with qualified data about the mobility and mood of their patients through an intuitive and user-friendly web tool. Finally, to determine the performance of the proposed AR system, this article presents its evaluation in terms of latency and processing time. The results show that both times are low enough to provide a good user experience. Full article

(This article belongs to the Special Issue Wearable and Remote Sensing and Monitoring for Personal and Professional Healthcare)

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17 pages, 4874 KiB

Open AccessArticle

Development of a Virtual Reality Simulator for an Intelligent Robotic System Used in Ankle Rehabilitation

by Florin Covaciu, Adrian Pisla and Anca-Elena Iordan

Sensors 2021, 21(4), 1537; https://doi.org/10.3390/s21041537 - 23 Feb 2021

Cited by 23 | Viewed by 5180

Abstract

The traditional systems used in the physiotherapy rehabilitation process are evolving towards more advanced systems that use virtual reality (VR) environments so that the patient in the rehabilitation process can perform various exercises in an interactive way, thus improving the patient’s motivation and [...] Read more.

The traditional systems used in the physiotherapy rehabilitation process are evolving towards more advanced systems that use virtual reality (VR) environments so that the patient in the rehabilitation process can perform various exercises in an interactive way, thus improving the patient’s motivation and reducing the therapist’s work. The paper presents a VR simulator for an intelligent robotic system of physiotherapeutic rehabilitation of the ankle of a person who has had a stroke. This simulator can interact with a real human subject by attaching a sensor that contains a gyroscope and accelerometer to identify the position and acceleration of foot movement on three axes. An electromyography (EMG) sensor is also attached to the patient’s leg muscles to measure muscle activity because a patient who is in a worse condition has weaker muscle activity. The data collected from the sensors are taken by an intelligent module that uses machine learning to create new levels of exercise and control of the robotic rehabilitation structure of the virtual environment. Starting from these objectives, the virtual reality simulator created will have a low dependence on the therapist, this being the main improvement over other simulators already created for this purpose. Full article

(This article belongs to the Special Issue Wearable and Remote Sensing and Monitoring for Personal and Professional Healthcare)

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Review

Jump to: Research, Other

17 pages, 7931 KiB

Open AccessReview

Near-Field Communication in Biomedical Applications

by Sung-Gu Kang, Min-Su Song, Joon-Woo Kim, Jung Woo Lee and Jeonghyun Kim

Sensors 2021, 21(3), 703; https://doi.org/10.3390/s21030703 - 20 Jan 2021

Cited by 24 | Viewed by 6847

Abstract

Near-field communication (NFC) is a low-power wireless communication technology used in contemporary daily life. This technology contributes not only to user identification and payment methods, but also to various biomedical fields such as healthcare and disease monitoring. This paper focuses on biomedical applications [...] Read more.

Near-field communication (NFC) is a low-power wireless communication technology used in contemporary daily life. This technology contributes not only to user identification and payment methods, but also to various biomedical fields such as healthcare and disease monitoring. This paper focuses on biomedical applications among the diverse applications of NFC. It addresses the benefits of combining traditional and new sensors (temperature, pressure, electrophysiology, blood flow, sweat, etc.) with NFC technology. Specifically, this report describes how NFC technology, which is simply applied in everyday life, can be combined with sensors to present vision and opportunities to modern people. Full article

(This article belongs to the Special Issue Wearable and Remote Sensing and Monitoring for Personal and Professional Healthcare)

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Other

Jump to: Research, Review

10 pages, 935 KiB

Open AccessLetter

Principal Characteristics of Affected and Unaffected Side Trunk Movement and Gait Event Parameters during Hemiplegic Stroke Gait with IMU Sensor

by Jeong-Woo Seo, Seul-Gee Kim, Joong Il Kim, Boncho Ku, Kahye Kim, Sangkwan Lee and Jaeuk U. Kim

Sensors 2020, 20(24), 7338; https://doi.org/10.3390/s20247338 - 21 Dec 2020

Cited by 10 | Viewed by 3332

Abstract

This study describe the characteristics of hemiplegic stroke gait with principal component analysis (PCA) of trunk movement (TM) and gait event (GE) parameters by the inertial measurement unit (IMU) sensors: (1) Background: This process can determine dominant variables through multivariate examination to identify [...] Read more.

This study describe the characteristics of hemiplegic stroke gait with principal component analysis (PCA) of trunk movement (TM) and gait event (GE) parameters by the inertial measurement unit (IMU) sensors: (1) Background: This process can determine dominant variables through multivariate examination to identify the affected, unaffected, and healthy lower-limb sides; (2) Methods: The study monitored forty patients with stroke and twenty-eight healthy individuals comprising the control group for comparison. The IMU sensors were attached to each subject while performing a 6 m walking test. Sixteen variables extracted from the measured data were divided into 7 GE and 9 TM variables explaining pelvis tilt, oblique, and rotation. (3) Results: The tilt range variables of the trunk movement on the affected and unaffected sides were lower than those of the healthy side; this showed between-group differences in various GE variables. For the healthy and affected sides, 80% of variances were explained with 2 or 3 PCs involving only a few dominant variables; and (4) Conclusions: The difference between each side leg should be considered during the development of a diagnosis method. This research can be utilized to develop functional assessment tools for personalized treatment and to design appropriate training protocols. Full article

(This article belongs to the Special Issue Wearable and Remote Sensing and Monitoring for Personal and Professional Healthcare)

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11 pages, 3483 KiB

Open AccessLetter

Development of a Diagnosis and Evaluation System for Hemiplegic Patients Post-Stroke Based on Motion Recognition Tracking and Analysis of Wrist Joint Kinematics

by Subok Kim, Seoho Park and Onseok Lee

Sensors 2020, 20(16), 4548; https://doi.org/10.3390/s20164548 - 13 Aug 2020

Cited by 10 | Viewed by 3146

Abstract

An inexperienced therapist lacks the analysis of a patient’s movement. In addition, the patient does not receive objective feedback from the therapist due to the visual subjective judgment. The aim is to provide a guide for in-depth rehabilitation therapy in virtual space by [...] Read more.

An inexperienced therapist lacks the analysis of a patient’s movement. In addition, the patient does not receive objective feedback from the therapist due to the visual subjective judgment. The aim is to provide a guide for in-depth rehabilitation therapy in virtual space by continuously tracking the user’s wrist joint during Leap Motion Controller (LMC) activities and present the basic data to confirm steady therapy results in real-time. The conventional Box and Block Test (BBT) is commonly used in upper extremity rehabilitation therapy. It was modeled in proportion to the actual size and Auto Desk Inventor was used to perform the 3D modeling work. The created 3D object was then implemented in C # through Unity5.6.2p4 based on LMC. After obtaining a wrist joint motion value, the motion was analyzed by 3D graph. Healthy subjects (23 males and 25 females, n = 48) were enrolled in this study. There was no statistically significant counting difference between conventional BBT and system BBT. This indicates the possibility of effective diagnosis and evaluation of hemiplegic patients post-stroke. We can keep track of wrist joints, check real-time continuous feedback in the implemented virtual space, and provide the basic data for an LMC-based quantitative rehabilitation therapy guide. Full article

(This article belongs to the Special Issue Wearable and Remote Sensing and Monitoring for Personal and Professional Healthcare)

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