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Engineering Solutions for Digital Healthcare: From Health Monitoring to Health Enhancement

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 56346

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Special Issue Editors

Prof. Dr. Hyun Jae Baek

E-Mail Website
Guest Editor

Department of Biomedical Engineering, Soonchunhyang University, Asan-si 31538, Republic of Korea
Interests: medical electronics; health IoT; sleep engineering; brain–computer interface; digital therapeutics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Heenam Yoon

E-Mail Website
Guest Editor

Department of Human-Centered Artificial Intelligence, Sangmyung University, 20 Hongjimun 2-gil, Seoul, Republic of Korea
Interests: artificial intelligence; data science; sleep engineering; biomedical signal processing and control; cardiovascular engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The engagement of engineering technology in medicine is revolutionizing the way we monitor, diagnose, treat, and manage our health conditions. Emerging technologies not only expedite the development of new instrumentational solution for health examination, but also introduce a completely new class of healthcare solution—digital healthcare—which transforms the subject of medical practice from medical staff to the individual. It has a growing impact on the delivery of care and provides the opportunity to tackle the next frontier in healthcare by shifting the focus from hospital-oriented care to individually centered care. It covers everything from wearable devices to implantable sensors, from mobile healthcare apps to medical artificial intelligence, and from robot-assisted surgery to virtual therapists. In fact, it is about applying digital transformation to the medical field through disruptive technologies and innovative changes.

The goal of this Special Issue is to bring researchers and practitioners from engineering and clinical sides together to the forums and present their visions for the research and development of future digital healthcare technology by sharing cutting-edge research and applications on healthcare solutions such as sensors, devices, algorithm solutions, and clinical experiences. We invite high-quality research papers as well as review articles that describe current and expected challenges, along with potential solutions for digital healthcare.

Potential topics include but are not limited to the following:

Health Sensing and Processing:

New concepts in health sensing technology (e.g., mobile, wearable, nearable, attachable);
Biomedical signal processing methods to improve the reliability of consumer health devices;
Technological solutions to quantify individual health and wellness status;
Data science for healthcare application;
Digital (and electronic) approaches for health and disease surveillance.

Health Enhancement and Applications

User experience—how the general public or clinicians consume health data;
AI-driven interventions including AI chatbots in medicine and virtual therapists;
Digital (and electronic) approaches to health enhancement and disease treatment;
Technologies to induce self management and behavioral changes.

Prof. Dr. Hyun Jae Beak
Prof. Dr. Heenam Yoon
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

Digital healthcare
Health sensing
Health monitoring
Healthcare IoT
Biomedical signal processing and algorithms
Health enhancement
Artificial intelligence in medicine
Health data analytics
Digiceuticals
Digital therapeutics
Digital pharmaceutics

Published Papers (14 papers)

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Research

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13 pages, 1717 KiB

Open AccessArticle

Measuring Patient Similarity on Multiple Diseases by Joint Learning via a Convolutional Neural Network

by Sang Ho Oh, Seunghwa Back and Jongyoul Park

Sensors 2022, 22(1), 131; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010131 - 25 Dec 2021

Cited by 1 | Viewed by 2492

Abstract

Patient similarity research is one of the most fundamental tasks in healthcare, helping to make decisions without incurring additional time and costs in clinical practices. Patient similarity can also apply to various medical fields, such as cohort analysis and personalized treatment recommendations. Because of this importance, patient similarity measurement studies are actively being conducted. However, medical data have complex, irregular, and sequential characteristics, making it challenging to measure similarity. Therefore, measuring accurate similarity is a significant problem. Existing similarity measurement studies use supervised learning to calculate the similarity between patients, with similarity measurement studies conducted only on one specific disease. However, it is not realistic to consider only one kind of disease, because other conditions usually accompany it; a study to measure similarity with multiple diseases is needed. This research proposes a convolution neural network-based model that jointly combines feature learning and similarity learning to define similarity in patients with multiple diseases. We used the cohort data from the National Health Insurance Sharing Service of Korea for the experiment. Experimental results verify that the proposed model has outstanding performance when compared to other existing models for measuring multiple-disease patient similarity. Full article

(This article belongs to the Special Issue Engineering Solutions for Digital Healthcare: From Health Monitoring to Health Enhancement)

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20 pages, 2637 KiB

Open AccessArticle

Enhancing System Acceptance through User-Centred Design: Integrating Patient Generated Wellness Data

by Sarita Pais, Krassie Petrova and Dave Parry

Sensors 2022, 22(1), 45; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010045 - 22 Dec 2021

Cited by 2 | Viewed by 2750

Abstract

Gestational diabetes mellitus (GDM) is a condition that appears during pregnancy and is expected to be a temporary one. While patients are encouraged to manage it themselves, research findings indicate that GDM may negatively affect the foetus; in addition, there is an increased risk of women with GDM subsequently developing Type 2 diabetes. To alleviate the risks, women with GDM are advised to maintain a record of their diet and blood glucose levels and to attend regular clinical reviews. Rather than using a paper diary, women with GDM can maintain a record of their blood glucose level readings and other relevant data using a wellness mobile application (app). However, such apps are developed for general use and may not meet the specific needs of clinical staff (physicians, dietitians, obstetricians and midwives) involved in managing GDM; for example, an app may record glucose readings but not the details of a meal taken before or after the glucose reading. Second, the apps do not permanently store the data generated by the patient and do not support the transfer of these data to a clinical system or information portal. The mobile health (mHealth) system designed and developed in this research allows one to integrate different types of user generated wellness data into a centralised database. A user-centered design (UCD) approach informed by the technology acceptance model (TAM) was adopted. This paper investigates and evaluates the effectiveness of the approach with regard to facilitating system acceptance and future adoption through an early focus on enhancing system usefulness and ease of use. The functional system requirements of the proposed system were refined through a series of interviews with the perspective of clinical users; ease-of-use and usability issues were resolved through ‘think aloud’ sessions with clinicians and GDM patients. Full article

(This article belongs to the Special Issue Engineering Solutions for Digital Healthcare: From Health Monitoring to Health Enhancement)

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15 pages, 5701 KiB

Open AccessArticle

Assessment of ROI Selection for Facial Video-Based rPPG

by Dae-Yeol Kim, Kwangkee Lee and Chae-Bong Sohn

Sensors 2021, 21(23), 7923; https://0-doi-org.brum.beds.ac.uk/10.3390/s21237923 - 27 Nov 2021

Cited by 25 | Viewed by 5347

Abstract

In general, facial image-based remote photoplethysmography (rPPG) methods use color-based and patch-based region-of-interest (ROI) selection methods to estimate the blood volume pulse (BVP) and beats per minute (BPM). Anatomically, the thickness of the skin is not uniform in all areas of the face, so the same diffuse reflection information cannot be obtained in each area. In recent years, various studies have presented experimental results for their ROIs but did not provide a valid rationale for the proposed regions. In this paper, to see the effect of skin thickness on the accuracy of the rPPG algorithm, we conducted an experiment on 39 anatomically divided facial regions. Experiments were performed with seven algorithms (CHROM, GREEN, ICA, PBV, POS, SSR, and LGI) using the UBFC-rPPG and LGI-PPGI datasets considering 29 selected regions and two adjusted regions out of 39 anatomically classified regions. We proposed a BVP similarity evaluation metric to find a region with high accuracy. We conducted additional experiments on the TOP-5 regions and BOT-5 regions and presented the validity of the proposed ROIs. The TOP-5 regions showed relatively high accuracy compared to the previous algorithm’s ROI, suggesting that the anatomical characteristics of the ROI should be considered when developing a facial image-based rPPG algorithm. Full article

(This article belongs to the Special Issue Engineering Solutions for Digital Healthcare: From Health Monitoring to Health Enhancement)

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11 pages, 1848 KiB

Open AccessArticle

Novel Technique to Measure Pulse Wave Velocity in Brain Vessels Using a Fast Simultaneous Multi-Slice Excitation Magnetic Resonance Sequence

by Ju-Yeon Jung, Yeong-Bae Lee and Chang-Ki Kang

Sensors 2021, 21(19), 6352; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196352 - 23 Sep 2021

Cited by 3 | Viewed by 1795

Abstract

In this study, we proposed a novel pulse wave velocity (PWV) technique to determine cerebrovascular stiffness using a 3-tesla magnetic resonance imaging (MRI) to overcome the various shortcomings of existing PWV techniques for cerebral-artery PWV, such as long scan times and complicated procedures. The technique was developed by combining a simultaneous multi-slice (SMS) excitation pulse sequence with keyhole acquisition and reconstruction (SMS-K). The SMS-K technique for cerebral-artery PWV was evaluated using phantom and human experiments. In the results, common and internal carotid arteries (CCA and ICA) were acquired simultaneously in an image with a high temporal resolution-of 48 ms for one measurement. Vascular signals at 500 time points acquired within 30 s could generate pulse waveforms of CCA and ICA with 26 heartbeats, allowing for the detection of PWV changes over time. The results demonstrated that the SMS-K technique could provide more PWV information with a simple procedure within a short period of time. The procedural convenience and advantages of PWV measurements will make it more appropriate for clinical applications. Full article

(This article belongs to the Special Issue Engineering Solutions for Digital Healthcare: From Health Monitoring to Health Enhancement)

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15 pages, 7725 KiB

Open AccessCommunication

Pulse Rate Variability Analysis Using Remote Photoplethysmography Signals

by Su-Gyeong Yu, So-Eui Kim, Na Hye Kim, Kun Ha Suh and Eui Chul Lee

Sensors 2021, 21(18), 6241; https://0-doi-org.brum.beds.ac.uk/10.3390/s21186241 - 17 Sep 2021

Cited by 8 | Viewed by 3709

Abstract

Pulse rate variability (PRV) refers to the change in the interval between pulses in the blood volume pulse (BVP) signal acquired using photoplethysmography (PPG). PRV is an indicator of the health status of an individual’s autonomic nervous system. A representative method for measuring BVP is contact PPG (CPPG). CPPG may cause discomfort to a user, because the sensor is attached to the finger for measurements. In contrast, noncontact remote PPG (RPPG) extracts BVP signals from face data using a camera without the need for a sensor. However, because the existing RPPG is a technology that extracts a single pulse rate rather than a continuous BVP signal, it is difficult to extract additional health status indicators. Therefore, in this study, PRV analysis is performed using lab-based RPPG technology that can yield continuous BVP signals. In addition, we intended to confirm that the analysis of PRV via RPPG can be performed with the same quality as analysis via CPPG. The experimental results confirmed that the temporal and frequency parameters of PRV extracted from RPPG and CPPG were similar. In terms of correlation, the PRVs of RPPG and CPPG yielded correlation coefficients between 0.98 and 1.0. Full article

(This article belongs to the Special Issue Engineering Solutions for Digital Healthcare: From Health Monitoring to Health Enhancement)

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10 pages, 2178 KiB

Open AccessCommunication

Non-Contact Oxygen Saturation Measurement Using YCgCr Color Space with an RGB Camera

by Na Hye Kim, Su-Gyeong Yu, So-Eui Kim and Eui Chul Lee

Sensors 2021, 21(18), 6120; https://0-doi-org.brum.beds.ac.uk/10.3390/s21186120 - 12 Sep 2021

Cited by 18 | Viewed by 4467

Abstract

Oxygen saturation (SPO₂) is an important indicator of health, and is usually measured by placing a pulse oximeter in contact with a finger or earlobe. However, this method has a problem in that the skin and the sensor must be in contact, and an additional light source is required. To solve these problems, we propose a non-contact oxygen saturation measurement technique that uses a single RGB camera in an ambient light environment. Utilizing the fact that oxygenated and deoxygenated hemoglobin have opposite absorption coefficients at green and red wavelengths, the color space of photoplethysmographic (PPG) signals recorded from the faces of study participants were converted to the YCgCr color space. Substituting the peaks and valleys extracted from the converted Cg and Cr PPG signals into the Beer–Lambert law yields the SPO₂ via a linear equation. When the non-contact SPO₂ measurement value was evaluated based on the reference SPO₂ measured with a pulse oximeter, the mean absolute error was 0.537, the root mean square error was 0.692, the Pearson correlation coefficient was 0.86, the cosine similarity was 0.99, and the intraclass correlation coefficient was 0.922. These results confirm the feasibility of non-contact SPO₂ measurements. Full article

(This article belongs to the Special Issue Engineering Solutions for Digital Healthcare: From Health Monitoring to Health Enhancement)

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11 pages, 3448 KiB

Open AccessCommunication

Restoration of Remote PPG Signal through Correspondence with Contact Sensor Signal

by So-Eui Kim, Su-Gyeong Yu, Na Hye Kim, Kun Ha Suh and Eui Chul Lee

Sensors 2021, 21(17), 5910; https://0-doi-org.brum.beds.ac.uk/10.3390/s21175910 - 02 Sep 2021

Cited by 4 | Viewed by 3438

Abstract

Photoplethysmography (PPG) is an optical measurement technique that detects changes in blood volume in the microvascular layer caused by the pressure generated by the heartbeat. To solve the inconvenience of contact PPG measurement, a remote PPG technology that can measure PPG in a non-contact way using a camera was developed. However, the remote PPG signal has a smaller pulsation component than the contact PPG signal, and its shape is blurred, so only heart rate information can be obtained. In this study, we intend to restore the remote PPG to the level of the contact PPG, to not only measure heart rate, but to also obtain morphological information. Three models were used for training: support vector regression (SVR), a simple three-layer deep learning model, and SVR + deep learning model. Cosine similarity and Pearson correlation coefficients were used to evaluate the similarity of signals before and after restoration. The cosine similarity before restoration was 0.921, and after restoration, the SVR, deep learning model, and SVR + deep learning model were 0.975, 0.975, and 0.977, respectively. The Pearson correlation coefficient was 0.778 before restoration and 0.936, 0.933, and 0.939, respectively, after restoration. Full article

(This article belongs to the Special Issue Engineering Solutions for Digital Healthcare: From Health Monitoring to Health Enhancement)

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14 pages, 6471 KiB

Open AccessCommunication

Non-Contact Respiration Measurement Method Based on RGB Camera Using 1D Convolutional Neural Networks

by Hyeon-Sang Hwang and Eui-Chul Lee

Sensors 2021, 21(10), 3456; https://0-doi-org.brum.beds.ac.uk/10.3390/s21103456 - 15 May 2021

Cited by 8 | Viewed by 3346

Abstract

Conventional respiration measurement requires a separate device and/or can cause discomfort, so it is difficult to perform routinely, even for patients with respiratory diseases. The development of contactless respiration measurement technology would reduce discomfort and help detect and prevent fatal diseases. Therefore, we propose a respiration measurement method using a learning-based region-of-interest detector and a clustering-based respiration pixel estimation technique. The proposed method consists of a model for classifying whether a pixel conveys respiration information based on its variance and a method for classifying pixels with clear breathing components using the symmetry of the respiration signals. The proposed method was evaluated with the data of 14 men and women acquired in an actual environment, and it was confirmed that the average error was within approximately 0.1 bpm. In addition, a Bland–Altman analysis confirmed that the measurement result had no error bias, and regression analysis confirmed that the correlation of the results with the reference is high. The proposed method, designed to be inexpensive, fast, and robust to noise, is potentially suitable for practical use in clinical scenarios. Full article

(This article belongs to the Special Issue Engineering Solutions for Digital Healthcare: From Health Monitoring to Health Enhancement)

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15 pages, 2844 KiB

Open AccessArticle

Development of a Low-Cost, Modular Muscle–Computer Interface for At-Home Telerehabilitation for Chronic Stroke

by Octavio Marin-Pardo, Coralie Phanord, Miranda Rennie Donnelly, Christopher M. Laine and Sook-Lei Liew

Sensors 2021, 21(5), 1806; https://0-doi-org.brum.beds.ac.uk/10.3390/s21051806 - 05 Mar 2021

Cited by 12 | Viewed by 3040

Abstract

Stroke is a leading cause of long-term disability in the United States. Recent studies have shown that high doses of repeated task-specific practice can be effective at improving upper-limb function at the chronic stage. Providing at-home telerehabilitation services with therapist supervision may allow higher dose interventions targeted to this population. Additionally, muscle biofeedback to train patients to avoid unwanted simultaneous activation of antagonist muscles (co-contractions) may be incorporated into telerehabilitation technologies to improve motor control. Here, we present the development and feasibility of a low-cost, portable, telerehabilitation biofeedback system called Tele-REINVENT. We describe our modular electromyography acquisition, processing, and feedback algorithms to train differentiated muscle control during at-home therapist-guided sessions. Additionally, we evaluated the performance of low-cost sensors for our training task with two healthy individuals. Finally, we present the results of a case study with a stroke survivor who used the system for 40 sessions over 10 weeks of training. In line with our previous research, our results suggest that using low-cost sensors provides similar results to those using research-grade sensors for low forces during an isometric task. Our preliminary case study data with one patient with stroke also suggest that our system is feasible, safe, and enjoyable to use during 10 weeks of biofeedback training, and that improvements in differentiated muscle activity during volitional movement attempt may be induced during a 10-week period. Our data provide support for using low-cost technology for individuated muscle training to reduce unintended coactivation during supervised and unsupervised home-based telerehabilitation for clinical populations, and suggest this approach is safe and feasible. Future work with larger study populations may expand on the development of meaningful and personalized chronic stroke rehabilitation. Full article

(This article belongs to the Special Issue Engineering Solutions for Digital Healthcare: From Health Monitoring to Health Enhancement)

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16 pages, 2899 KiB

Open AccessArticle

Drowsiness Detection Based on Intelligent Systems with Nonlinear Features for Optimal Placement of Encephalogram Electrodes on the Cerebral Area

by Seunghyeok Hong and Hyun Jae Baek

Sensors 2021, 21(4), 1255; https://0-doi-org.brum.beds.ac.uk/10.3390/s21041255 - 10 Feb 2021

Cited by 7 | Viewed by 2217

Abstract

Drowsiness while driving can lead to accidents that are related to the loss of perception during emergencies that harm the health. Among physiological signals, brain waves have been used as informative signals for the analyses of behavioral observations, steering information, and other biosignals during drowsiness. We inspected the machine learning methods for drowsiness detection based on brain signals with varying quantities of information. The results demonstrated that machine learning could be utilized to compensate for a lack of information and to account for individual differences. Cerebral area selection approaches to decide optimal measurement locations could be utilized to minimize the discomfort of participants. Although other statistics could provide additional information in further study, the optimized machine learning method could prevent the dangers of drowsiness while driving by considering a transitional state with nonlinear features. Because brain signals can be altered not only by mental fatigue but also by health status, the optimization analysis of the system hardware and software will be able to increase the power-efficiency and accessibility in acquiring brain waves for health enhancements in daily life. Full article

(This article belongs to the Special Issue Engineering Solutions for Digital Healthcare: From Health Monitoring to Health Enhancement)

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19 pages, 37011 KiB

Open AccessArticle

Siamese Architecture-Based 3D DenseNet with Person-Specific Normalization Using Neutral Expression for Spontaneous and Posed Smile Classification

by Kunyoung Lee and Eui Chul Lee

Sensors 2020, 20(24), 7184; https://0-doi-org.brum.beds.ac.uk/10.3390/s20247184 - 15 Dec 2020

Viewed by 2049

Abstract

Clinical studies have demonstrated that spontaneous and posed smiles have spatiotemporal differences in facial muscle movements, such as laterally asymmetric movements, which use different facial muscles. In this study, a model was developed in which video classification of the two types of smile was performed using a 3D convolutional neural network (CNN) applying a Siamese network, and using a neutral expression as reference input. The proposed model makes the following contributions. First, the developed model solves the problem caused by the differences in appearance between individuals, because it learns the spatiotemporal differences between the neutral expression of an individual and spontaneous and posed smiles. Second, using a neutral expression as an anchor improves the model accuracy, when compared to that of the conventional method using genuine and imposter pairs. Third, by using a neutral expression as an anchor image, it is possible to develop a fully automated classification system for spontaneous and posed smiles. In addition, visualizations were designed for the Siamese architecture-based 3D CNN to analyze the accuracy improvement, and to compare the proposed and conventional methods through feature analysis, using principal component analysis (PCA). Full article

(This article belongs to the Special Issue Engineering Solutions for Digital Healthcare: From Health Monitoring to Health Enhancement)

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22 pages, 13938 KiB

Open AccessArticle

Automated White Blood Cell Counting in Nailfold Capillary Using Deep Learning Segmentation and Video Stabilization

by Byeonghwi Kim, Yuli-Sun Hariyani, Young-Ho Cho and Cheolsoo Park

Sensors 2020, 20(24), 7101; https://0-doi-org.brum.beds.ac.uk/10.3390/s20247101 - 11 Dec 2020

Cited by 8 | Viewed by 3321

Abstract

White blood cells (WBCs) are essential components of the immune system in the human body. Various invasive and noninvasive methods to monitor the condition of the WBCs have been developed. Among them, a noninvasive method exploits an optical characteristic of WBCs in a nailfold capillary image, as they appear as visual gaps. This method is inexpensive and could possibly be implemented on a portable device. However, recent studies on this method use a manual or semimanual image segmentation, which depends on recognizable features and the intervention of experts, hindering its scalability and applicability. We address and solve this problem with proposing an automated method for detecting and counting WBCs that appear as visual gaps on nailfold capillary images. The proposed method consists of an automatic capillary segmentation method using deep learning, video stabilization, and WBC event detection algorithms. Performances of the three segmentation algorithms (manual, conventional, and deep learning) with/without video stabilization were benchmarks. Experimental results demonstrate that the proposed method improves the performance of the WBC event counting and outperforms conventional approaches. Full article

(This article belongs to the Special Issue Engineering Solutions for Digital Healthcare: From Health Monitoring to Health Enhancement)

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22 pages, 5931 KiB

Open AccessArticle

Feature Analysis of Smart Shoe Sensors for Classification of Gait Patterns

by Unang Sunarya, Yuli Sun Hariyani, Taeheum Cho, Jongryun Roh, Joonho Hyeong, Illsoo Sohn, Sayup Kim and Cheolsoo Park

Sensors 2020, 20(21), 6253; https://0-doi-org.brum.beds.ac.uk/10.3390/s20216253 - 02 Nov 2020

Cited by 18 | Viewed by 7993

Abstract

Gait analysis is commonly used to detect foot disorders and abnormalities such as supination, pronation, unstable left foot and unstable right foot. Early detection of these abnormalities could help us to correct the walking posture and avoid getting injuries. This paper presents extensive feature analyses on smart shoes sensor data, including pressure sensors, accelerometer and gyroscope signals, to obtain the optimum combination of the sensors for gait classification, which is crucial to implement a power-efficient mobile smart shoes system. In addition, we investigated the optimal length of data segmentation based on the gait cycle parameters, reduction of the feature dimensions and feature selection for the classification of the gait patterns. Benchmark tests among several machine learning algorithms were conducted using random forest, k-nearest neighbor (KNN), logistic regression and support vector machine (SVM) algorithms for the classification task. Our experiments demonstrated the combination of accelerometer and gyroscope sensor features with SVM achieved the best performance with 89.36% accuracy, 89.76% precision and 88.44% recall. This research suggests a new state-of-the-art gait classification approach, specifically on detecting human gait abnormalities. Full article

(This article belongs to the Special Issue Engineering Solutions for Digital Healthcare: From Health Monitoring to Health Enhancement)

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Review

Jump to: Research

21 pages, 3735 KiB

Open AccessReview

External Auditory Stimulation as a Non-Pharmacological Sleep Aid

by Heenam Yoon and Hyun Jae Baek

Sensors 2022, 22(3), 1264; https://0-doi-org.brum.beds.ac.uk/10.3390/s22031264 - 07 Feb 2022

Cited by 8 | Viewed by 8475

Abstract

The increased demand for well-being has fueled interest in sleep. Research in technology for monitoring sleep ranges from sleep efficiency and sleep stage analysis to sleep disorder detection, centering on wearable devices such as fitness bands, and some techniques have been commercialized and are available to consumers. Recently, as interest in digital therapeutics has increased, the field of sleep engineering demands a technology that helps people obtain quality sleep that goes beyond the level of monitoring. In particular, interest in sleep aids for people with or without insomnia but who cannot fall asleep easily at night is increasing. In this review, we discuss experiments that have tested the sleep-inducing effects of various auditory stimuli currently used for sleep-inducing purposes. The auditory stimulations were divided into (1) colored noises such as white noise and pink noise, (2) autonomous sensory meridian response sounds such as natural sounds such as rain and firewood burning, sounds of whispers, or rubbing various objects with a brush, and (3) classical music or a preferred type of music. For now, the current clinical method of receiving drugs or cognitive behavioral therapy to induce sleep is expected to dominate. However, it is anticipated that devices or applications with proven ability to induce sleep clinically will begin to appear outside the hospital environment in everyday life. Full article

(This article belongs to the Special Issue Engineering Solutions for Digital Healthcare: From Health Monitoring to Health Enhancement)

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