Modeling of motion artifacts on PPG signals for heart-monitoring using wearable devices

2020 ◽  
Author(s):  
Sebastián Andrés Cajas ◽  
María Alejandra Landínez ◽  
Diego Mauricio López
Keyword(s):  
Wearable Devices ◽  
Motion Artifacts ◽  
Heart Monitoring

A Design Exploration of Intelligent Wearable Companion of Smartphone for Fitness and Healthcare

2019 ◽  
Vol 33 (08) ◽  
pp. 1959023
Author(s):  
Wen Qi
Keyword(s):  
Wearable Devices ◽  
Motion Artifacts ◽  
Signal Quality ◽  
Ecg Signals ◽  
Analog Front End ◽  
Fitness Industry ◽  
Different Types ◽  
Electrocardiogram Ecg ◽  
Product Forms ◽  
Future Topics

Wearable devices are increasingly gaining more attentions in healthcare and fitness industry due to their potentials to measure valuable physiological signals on the move. There are many researchers who have proposed different types of designs that embed biosensors into miniature wearable devices. In this paper, we present a wearable companion that monitors the cardiac activities of a wearer with smartphone. The device makes use of a single, integrated biosensor that is designed with a unique analog front-end circuitry and a dedicated signal processing pipeline. In order to meet the requirements of possible but different user scenarios, three types of product forms are presented. The experimental results show that electrocardiogram (ECG) signals collected are valid and consistent through the systems. Future topics include adding extra algorithms to remove motion artifacts in order to achieve better signal quality in various settings and include wireless communication through 4G.

scholarly journals Machine-learning analysis for discriminating radial pulse-waveform quality by applying different contact pressures

2021 ◽  
Author(s):  
Te OuYoung ◽  
Wan-Ling Weng ◽  
Ting-Yu Hu ◽  
Chia-Chien Lee ◽  
Li-Wei Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Contact Pressure ◽  
Screening Method ◽  
Wearable Devices ◽  
Motion Artifacts ◽  
Machine Learning Algorithms ◽  
Accurate Estimation ◽  
High Quality ◽  
Learning Analysis ◽  
Contact Pressures

Abstract Pulse measurements made using wearable devices can aid the monitoring of the human physiological condition. However, accurate estimation of waveforms is often difficult for nonexperts and motion artifacts may occur during tonometry measurements when the skin-sensor contact pressure is insufficient. An alternative approach is to extract only high-quality pulses for use in index calculations. The present study aimed to determine the effectiveness of using machine-learning analysis in discriminating between high-quality and low-quality pulse waveforms induced by applying different contact pressures. Radial blood pressure waveform (BPW) signals were measured noninvasively in healthy young subjects using a strain-gauge transducer. One-minute-long trains of pulse data were measured when applying the appropriate contact pressure (67.80±1.55 mmHg) and a higher contact pressure (151.80±3.19 mmHg). Eight machine-learning algorithms were employed to evaluate the following 40 harmonic pulse indices: amplitude proportions and their coefficients of variation, and phase angles and their standard deviations. Significant differences were noted in BPW indices between applying appropriate and higher skin-surface contact pressures. The present appropriate contact pressure could not only provide a suitable holding force for the wearable device, but also helped to maintain the physiological stability of the underlying tissues. Machine-learning analysis provides an effective method for distinguishing between the high-quality and low-quality pulses with excellent discrimination performance (leave-one-out test: random-forest AUC=0.96). This approach will aid the development of an automatic screening method for waveform quality, and thereby improve the reliability of pulse waveforms acquired by wearable devices. The effects of other possible interfering factors in practical wearable applications can also be systematically studied using a similar procedure.

Antenatal electronic fetal heart monitoring for extremely and very preterm newborns

2019 ◽  
Vol 4 (26) ◽  
pp. 20
Author(s):  
Ali Saphia ◽  
George Alexandru Filipescu ◽  
Anca A. Simionescu
Keyword(s):  
Fetal Heart ◽  
Very Preterm ◽  
Heart Monitoring ◽  
Preterm Newborns

DEVELOPMENT OF A DEVICE FOR CONTROL OF THE STATE OF THE ORGANISM BASED ON PHOTOPLETISMOGRAPHY USING TECHNOLOGIES OF DIGITAL ADAPTIVE FILTRATION

2020 ◽  
pp. 100-107
Author(s):  
Алексей Дмитриевич Акишин ◽  
Иван Павлович Семчук ◽  
Александр Петрович Николаев
Keyword(s):  
Heart Rate ◽  
Cardiovascular System ◽  
Adaptive Filtering ◽  
Least Squares Method ◽  
Optimal Algorithm ◽  
Heart Diseases ◽  
Cardiac Activity ◽  
Motion Artifacts ◽  
The Body ◽  
Advantages And Disadvantages

Постоянно растущий интерес к разработке новых неинвазивных и безманжетных методов измерения параметров сердечной деятельности, использование которых давало бы возможность непрерывного и удаленного контроля сердечно-сосудистой системы, обуславливает актуальность данной работы. В многочисленных публикациях продолжаются обсуждения преимуществ и недостатков различных методов ранней диагностики сердечно-сосудистых заболеваний. Однако артефакты движения являются сильной помехой, мешающей точной оценке показателей функционирования сердечно-сосудистой системы. Одним из перспективных методов контроля является метод оценки физиологических параметров с использованием фотоплетизмографии. Данная статья посвящена разработке устройства для фотоплетизмографических исследований и алгоритмических методов обработки регистрируемых сигналов для обеспечения мониторинга сердечного ритма с заданной точностью. В работе используются технологии цифровой адаптивной фильтрации полученных сигналов для мониторинга сердечного ритма в условиях внешних механических и электрических помеховых воздействий, ухудшающих точностные характеристики системы, а также разработана архитектура системы и изготовлен макет устройства, который позволил провести измерения для определения оптимального алгоритма цифровой обработки сигналов. При использовании устройства применялись методы адаптивной фильтрации на основе фильтров Винера, фильтров на основе метода наименьших квадратов и Калмановской фильтрации. Разработанное устройство для фотоплетизмографических исследований обеспечило возможность мониторинга сердечного ритма с заданной точностью, контроля текущего состояния организма и может быть использовано в качестве средства диагностики заболеваний сердца The constantly growing interest in the development of new non-invasive and cuff-free methods for measuring the parameters of cardiac activity, the use of which would give the possibility of continuous and remote monitoring of the cardiovascular system, determines the relevance of this work. Numerous publications continue to discuss the advantages and disadvantages of various methods of early diagnosis of cardiovascular disease. However, motion artifacts are a strong hindrance to the accurate assessment of the performance of the cardiovascular system. One of the promising control methods is the method for assessing physiological parameters using photoplethysmography. This article is devoted to the development of a device for photoplethysmographic studies and algorithmic methods for processing recorded signals to ensure monitoring of the heart rate with a given accuracy. The work uses technologies of digital adaptive filtering of the received signals to monitor the heart rate in conditions of external mechanical and electrical interference, which worsen the accuracy characteristics of the system, as well as the architecture of the system and a prototype of the device, which made it possible to carry out measurements to determine the optimal algorithm for digital signal processing. When using the device, the methods of adaptive filtering based on Wiener filters, filters based on the least squares method and Kalman filtering were used. The developed device for photoplethysmographic studies provided the ability to monitor the heart rate with a given accuracy, control the current state of the body and can be used as a means of diagnosing heart diseases

Systematic Analysis and Review of Magnetic Resonance Imaging (MRI) Reconstruction Techniques

2021 ◽  
Vol 16 ◽  
Author(s):  
Penta Anil Kumar ◽  
R. Gunasundari ◽  
R. Aarthi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Optimization Technique ◽  
Motion Artifacts ◽  
Reconstruction Technique ◽  
Resonance Imaging ◽  
Research Gaps ◽  
Conventional Mri ◽  
Mri Reconstruction ◽  
Magnetic Resonance Imaging Mri

Background: Magnetic Resonance Imaging (MRI) plays an important role in the field of medical diagnostic imaging as it poses non-invasive acquisition and high soft-tissue contrast. However, the huge time is needed for the MRI scanning process that results in motion artifacts, degrades image quality, misinterpretation of data, and may cause uncomfortable to the patient. Thus, the main goal of MRI research is to accelerate data acquisition processing without affecting the quality of the image. Introduction: This paper presents a survey based on distinct conventional MRI reconstruction methodologies. In addition, a novel MRI reconstruction strategy is proposed based on weighted Compressive Sensing (CS), Penalty-aided minimization function, and Meta-heuristic optimization technique. Methods: An illustrative analysis is done concerning adapted methods, datasets used, execution tools, performance measures, and values of evaluation metrics. Moreover, the issues of existing methods and the research gaps considering conventional MRI reconstruction schemes are elaborated to obtain improved contribution for devising significant MRI reconstruction techniques. Results: The proposed method will reduce conventional aliasing artifacts problems, may attain lower Mean Square Error (MSE), higher Peak Signal-to-Noise Ratio (PSNR), and Structural SIMilarity (SSIM) index. Conclusion: The issues of existing methods and the research gaps considering conventional MRI reconstruction schemes are elaborated to devising an improved significant MRI reconstruction technique.

Smart technology-assisted Home Nursing Care Program for Persons with Dementia or Hip Fracture: Implementation Difficulties: Thematic Analysis (Preprint)

2020 ◽  
Author(s):  
Yea-Ing Shyu ◽  
Chung-Chih Lin ◽  
Ching-Tzu Yang ◽  
Pei-Ling Su ◽  
Jung-Ling Hsu
Keyword(s):  
Hip Fracture ◽  
Family Caregivers ◽  
Nursing Care ◽  
Care Program ◽  
Wearable Devices ◽  
Elderly Persons ◽  
Home Nursing ◽  
Smart Technology ◽  
Smart Clothing ◽  
Home Nursing Care

BACKGROUND Wearable devices have been developed and implemented to improve data collection in remote health care and smart care. Wearable devices have the advantage of always being with individuals, enabling easy detection of their movements. In this study, we developed and implemented a smart-care system using smart clothing for persons with dementia and with hip fracture. We conducted a preliminary study to understand family caregivers’ and care receivers’ experiences of receiving a smart technology-assisted (STA) home-nursing care program. OBJECTIVE This paper reports the difficulties we encountered and strategies we developed during the feasibility phase of studies on the effectiveness of our STA home-nursing care program for persons with dementia and hip fracture. METHODS Our care model, a STA home-nursing care program for persons with dementia and those with hip fracture included a remote-monitoring system for elderly persons wearing smart clothing was used to facilitate family caregivers’ detection of elderly persons’ movements. These movements included getting up at night, staying in the bathroom for more than 30 minutes, not moving more than 2 hours during the day, leaving the house, and daily activities. Participants included 13 families with 5 patients with hip fracture and 7 with dementia. Research nurses documented the difficulties they encountered during the process. RESULTS Difficulties encountered in this smart-care study were categorized into problems setting up the smart-care environment, problems running the system, and problems with participant acceptance/adherence. These difficulties caused participants to drop out, the system to not function or delayed function, inability to collect data, extra costs of manpower, and financial burden. Strategies to deal with these problems are also reported. CONCLUSIONS During the implementation of smart care at home for persons with dementia or hip fracture, different aspects of difficulties were found and strategies were taken. The findings of this study can provide a reference for future implementation of similar smart-home devices.

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