Measurements of Wearable Systems and Antennas

2017 ◽  
pp. 387-414
Author(s):  
Albert Sabban
Keyword(s):  
Wearable Systems

scholarly journals Dye-Sensitized Solar Cell for Indoor Applications: A Mini-Review

2021 ◽  
Author(s):  
Dheeraj Devadiga ◽  
M. Selvakumar ◽  
Prakasha Shetty ◽  
M. S. Santosh
Keyword(s):  
Solar Cell ◽  
Dye Sensitized Solar Cell ◽  
Low Light ◽  
Dye Sensitized ◽  
Energy Demands ◽  
Roll To Roll ◽  
Wearable Systems ◽  
Recent Developments ◽  
Sustainable Power ◽  
The Internet Of Things

AbstractLightweight computing technologies such as the Internet of Things and flexible wearable systems have penetrated our everyday lives exponentially in recent years. Without a question, the running of such electronic devices is a major energy problem. Generally, these devices need power within the range of microwatts and operate mostly indoors. Thus, it is appropriate to have a self-sustainable power source, such as the photovoltaic (PV) cell, which can harvest indoor light. Among other PV cells, the dye-sensitized solar cell (DSSC) has immense capacity to satisfy the energy demands of most indoor electronics, making it a very attractive power candidates because of its many benefits such as readily available materials, relatively cheap manufacturing methods, roll-to-roll compatibility, easy processing capabilities on flexible substrates and exceptional diffuse/low-light performance. This review discusses the recent developments in DSSC materials for its indoor applications. Ultimately, the perspective on this topic is presented after summing up the current progress of the research. Graphic abstract

Internet of things-based smart wearable system to monitor sports person health

2021 ◽  
pp. 1-14
Author(s):  
Fen Li ◽  
Oscar Sanjuán Martínez ◽  
R.S. Aiswarya
Keyword(s):  
Internet Of Things ◽  
Experimental Result ◽  
Wearable System ◽  
Wearable Systems ◽  
Trading Decisions ◽  
Time Basis ◽  
And Performance ◽  
Individual Sports ◽  
Smart Wearable ◽  
The Internet Of Things

BACKGROUND: The modern Internet of Things (IoT) makes small devices that can sense, process, interact, connect devices, and other sensors ready to understand the environment. IoT technologies and intelligent health apps have multiplied. The main challenges in the sports environment are playing without injuries and healthily. OBJECTIVE: In this paper the Internet of Things-based Smart Wearable System (IoT-SWS) is introduced for monitoring sports person activity to improve sports person health and performance in a healthy way. METHOD: Wearable systems are commonly used to capture individual sports details on a real-time basis. Collecting data from wearable devices and IoT technologies can help organizations learn how to optimize in-game strategies, identify opponents’ vulnerabilities, and make smarter draft choices and trading decisions for a sportsperson. RESULTS: The experimental result shows that IoT-SWS achieve the highest accuracy of 98.22% and efficient in predicting the sports person’s health to improve sports person performance reliably.

scholarly journals A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1562
Author(s):  
Syed Anas Imtiaz
Keyword(s):  
Systematic Review ◽  
Classification Accuracy ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Regulatory Requirements ◽  
Wearable System ◽  
Wearable Systems

Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are identified. Each sensing modality is explored to identify signals that can be obtained from it, the sleep stages that can be reliably identified, the classification accuracy of systems and methods using the sensing modality, as well as the usability constraints of the sensor in a wearable system. It concludes that the two most common sensing modalities in use are those based on electroencephalography (EEG) and photoplethysmography (PPG). EEG-based systems are the most accurate, with EEG being the only sensing modality capable of identifying all the stages of sleep. PPG-based systems are much simpler to use and better suited for wearable monitoring but are unable to identify all the sleep stages.

scholarly journals Recent Advances in Wearable Devices for Non-Invasive Sensing

2021 ◽  
Vol 11 (3) ◽  
pp. 1235
Author(s):  
Su Min Yun ◽  
Moohyun Kim ◽  
Yong Won Kwon ◽  
Hyobeom Kim ◽  
Mi Jung Kim ◽  
...  
Keyword(s):  
Health Monitoring ◽  
Data Transmission ◽  
Wearable Sensors ◽  
Wearable Devices ◽  
Wireless Power ◽  
Non Invasive ◽  
Sensing Systems ◽  
Recent Advances ◽  
Wearable Systems ◽  
Precise Diagnosis

The development of wearable sensors is aimed at enabling continuous real-time health monitoring, which leads to timely and precise diagnosis anytime and anywhere. Unlike conventional wearable sensors that are somewhat bulky, rigid, and planar, research for next-generation wearable sensors has been focused on establishing fully-wearable systems. To attain such excellent wearability while providing accurate and reliable measurements, fabrication strategies should include (1) proper choices of materials and structural designs, (2) constructing efficient wireless power and data transmission systems, and (3) developing highly-integrated sensing systems. Herein, we discuss recent advances in wearable devices for non-invasive sensing, with focuses on materials design, nano/microfabrication, sensors, wireless technologies, and the integration of those.

scholarly journals Optimization of Physical Activity Recognition for Real-Time Wearable Systems: Effect of Window Length, Sampling Frequency and Number of Features

2019 ◽  
Vol 9 (22) ◽  
pp. 4833 ◽  
Author(s):  
Ardo Allik ◽  
Kristjan Pilt ◽  
Deniss Karai ◽  
Ivo Fridolin ◽  
Mairo Leier ◽  
...  
Keyword(s):  
Physical Activity ◽  
Feature Selection ◽  
Real Time ◽  
Sampling Frequency ◽  
Classification Performance ◽  
Window Length ◽  
Decision Tree Classifier ◽  
Acceleration Signal ◽  
Wearable Systems ◽  
Tree Classifier

The aim of this study was to develop an optimized physical activity classifier for real-time wearable systems with the focus on reducing the requirements on device power consumption and memory buffer. Classification parameters evaluated in this study were the sampling frequency of the acceleration signal, window length of the classification fragment, and the number of classification features, found with different feature selection methods. For parameter evaluation, a decision tree classifier was created based on the acceleration signals recorded during tests, where 25 healthy test subjects performed various physical activities. Overall average F1-score achieved in this study was about 0.90. Similar F1-scores were achieved with the evaluated window lengths of 5 s (0.92 ± 0.02) and 3 s (0.91 ± 0.02), while classification performance with 1 s were lower (0.87 ± 0.02). Tested sampling frequencies of 50 Hz, 25 Hz, and 13 Hz had similar results with most classified activity types, with an exception of outdoor cycling, where differences were significant. Using forward sequential feature selection enabled the decreasing of the number of features from initial 110 features to about 12 features without lowering the classification performance. The results of this study have been used for developing more efficient real-time physical activity classifiers.

Individual Use of Smartwatches with Self-Monitoring EKG Functionality for Diagnosing Arrhythmias: A Scoping Review (Preprint)

2021 ◽  
Author(s):  
Mohammed Ali
Keyword(s):  
Heart Rate ◽  
Cardiac Arrhythmias ◽  
Healthcare Systems ◽  
Wearable Devices ◽  
Health Conditions ◽  
Self Monitoring ◽  
Policy Makers ◽  
Wearable Systems ◽  
The World ◽  
Individual Use

BACKGROUND cardiovascular diseases (CVDs) have become prevalent in the world. They cause millions of deaths globally with the World Health Organization putting the figure at 17.9 million people every year. These statistics indicate the need for healthcare systems to leverage contemporary advanced technology to detect and diagnose CVDs and provide appropriate and timely care to reduce mortality rates. OBJECTIVE To conduct a scoping review exploring individual use of smartwatches with self-monitoring ECG functionality for diagnosing arrhythmias. METHODS Source were selected from six credible bibliographic databases: PubMed, Medline, EMBASE, PsycInfo, CINAHL, and Google Scholar. Intervention-related terms were used to identify relevant sources. Additionally, a forward search strategy was used to search the databases and identify appropriate peer-reviewed journals. RESULTS The research returned 230 sources, out of which 40 met the inclusion criterion. The studies revealed that increased research, development, and adoption of smartwatches and other wearable devices have intensified in the past two decades. The studies showed that using smartwatches can detect cardiac arrhythmias although this depends on the algorithms and biometric sensors utilized in the smartwatches. Watches with advanced algorithms, PPG, and EKG functionalities exhibit high accuracy, sensitivity, and specificity, detecting AFib and other arrhythmias with high efficacy. Therefore, the best way for technology companies to improve their watches’ accuracy is to design and use advanced algorithms and combine PPG, EKG, activity, and biochemical sensors. Conclusion: The contemporary healthcare space is replete with wearable and non-wearable ¬systems and devices central to detecting health conditions and informing the relevant stakeholders to take corrective actions. Smartwatches are wearable devices used chiefly by patients, health, and fitness enthusiasts to detect and monitor a series of conditions, such as heart rate. Their use has fostered timely detection of cardiac arrhythmias, and therefore, caregivers and policy-makers should emphasize their use. CONCLUSIONS Technological systems have proliferated many human spaces in the last three decades, including education, healthcare, and entertainment. Their use has improved operational efficiency, reduced costs, saved lives, and increased organizations’ bottom lines. Healthcare systems use technological devices and appliances to diagnose patients, perform surgeries, improve pharmacy operations, and reduce medical errors. That way, most healthcare facilities provide quality care, attaining positive clinical outcomes. The contemporary healthcare space is replete with wearable and non-wearable ¬systems and devices central to detecting health conditions and informing the relevant stakeholders – caregivers, patients, and family members – to take corrective actions. Smartwatches are wearable devices used chiefly by patients, health, and fitness enthusiasts to detect and monitor a series of conditions, such as heart rate. They are highly effective in detecting cardiac arrhythmias, and therefore, caregivers and policy-makers should emphasize their use.

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