Wearable Technologies as a Path to Single-Pilot Part 121 Operations

2015 ◽  
Vol 8 (1) ◽  
pp. 81-88 ◽  
Author(s):  
Robert Moehle ◽  
Jason Clauss
Keyword(s):  
Wearable Technologies ◽  
Part 121

Effects of wearable technology on patients with diabetes: A systematic review and meta-analysis (Preprint)

2020 ◽  
Author(s):  
Dongjun Wu ◽  
Nicholas Buys ◽  
Guandong Xu ◽  
Jing Sun
Keyword(s):  
Systematic Review ◽  
Blood Pressure ◽  
Relapse Prevention ◽  
Diabetes Management ◽  
Meta Analysis ◽  
Intervention Group ◽  
Wearable Technologies ◽  
Patients With Diabetes ◽  
Hba1c Levels

UNSTRUCTURED Aims: This systematic review and meta-analysis aimed to evaluate the effects of wearable technologies on HbA1c, blood pressure, body mass index (BMI), and fastening blood glucose (FBG) in patients with diabetes. Methods: We searched PubMed, Scopus, Embase, the Cochrane database, and the Chinese CNKI database from last 15 years until August 2021. The quality of the 16 included studies was assessed using the PEDro scale, and random effect models were used to estimate outcomes, with I2 used for heterogeneity testing. Results: A significant reduction in HbA1c (-0.475% [95% CI -0.692 to -0.257, P<0.001]) was found following telemonitoring. However, the results of the meta-analysis did not show significant changes in blood pressure, BMI, and glucose, in the intervention group (P>0.05), although the effect size for systolic blood pressure (0.389) and diastolic blood pressure may indicate a significant effect. Subgroup analysis revealed statistically significant effects of wearable technologies on HbA1c when supported by dietetic interventions (P<0.001), medication monitoring (P<0.001), and relapse prevention (P<0.001). Online messages and telephone interventions significantly affected HbA1c levels (P<0.001). Trials with additional online face-to-face interventions showed greater reductions in HbA1c levels. Remote interventions including dietetic advice (P<0.001), medication (P<0.001), and relapse prevention (P<0.001) during telemonitoring showed a significant effect on HbA1c, particularly in patients attending ten or more intervention sessions (P<0.001). Conclusion: Wearable technologies can improve diabetes management by simplifying self-monitoring, allowing patients to upload their live measurement results frequently and thereby improving the quality of telemedicine. Wearable technologies also facilitate remote medication management, dietetic interventions, and relapse prevention.

Neuroethics

2017 ◽  
Keyword(s):  
Brain Function ◽  
Ethical Issues ◽  
Wearable Technologies ◽  
Definition Of Death ◽  
Populations At Risk ◽  
The Future ◽  
Aging Adults ◽  
Definition Of ◽  
The Brain ◽  
New Criteria

We have new answers to how the brain works and tools which can now monitor and manipulate brain function. Rapid advances in neuroscience raise critical questions with which society must grapple. What new balances must be struck between diagnosis and prediction, and invasive and noninvasive interventions? Are new criteria needed for the clinical definition of death in cases where individuals are eligible for organ donation? How will new mobile and wearable technologies affect the future of growing children and aging adults? To what extent is society responsible for protecting populations at risk from environmental neurotoxins? As data from emerging technologies converge and are made available on public databases, what frameworks and policies will maximize benefits while ensuring privacy of health information? And how can people and communities with different values and perspectives be maximally engaged in these important questions? Neuroethics: Anticipating the Future is written by scholars from diverse disciplines—neurology and neuroscience, ethics and law, public health, sociology, and philosophy. With its forward-looking insights and considerations for the future, the book examines the most pressing current ethical issues.

Enhancing Athlete Tracking Using Data Fusion in Wearable Technologies

2021 ◽  
Vol 70 ◽  
pp. 1-13
Author(s):  
Adnan Waqar ◽  
Iftekhar Ahmad ◽  
Daryoush Habibi ◽  
Nicolas Hart ◽  
Quoc Viet Phung
Keyword(s):  
Data Fusion ◽  
Wearable Technologies ◽  
Using Data

scholarly journals Ultralow magnetostrictive flexible ferromagnetic nanowires

Nanoscale ◽  
2021 ◽  
Author(s):  
Giuseppe Muscas ◽  
Petra Jönsson ◽  
Ismael Garcia Serrano ◽  
Örjan Vallin ◽  
M. Venkata Kamalakar
Keyword(s):  
Flexible Electronics ◽  
Magnetic Nanowires ◽  
Wearable Technologies ◽  
Ferromagnetic Nanowires ◽  
Core Components

The integration of magneto-electric and spintronic sensors to flexible electronics presents massive potential for advancing flexible and wearable technologies. Magnetic nanowires are core components for building such devices. Therefore, realizing...

scholarly journals Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3461
Author(s):  
Blake Anthony Hickey ◽  
Taryn Chalmers ◽  
Phillip Newton ◽  
Chin-Teng Lin ◽  
David Sibbritt ◽  
...  
Keyword(s):  
Mental Health ◽  
Systematic Review ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Smart Devices ◽  
Stress Detection ◽  
Wearable Technologies ◽  
Detection Of Stress ◽  
Anxiety Depression

Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.

scholarly journals Gait analysis for early Parkinson’s disease detection based on deep learning

2019 ◽  
Vol 5 (1) ◽  
pp. 9-12
Author(s):  
Jyothsna Kondragunta ◽  
Christian Wiede ◽  
Gangolf Hirtz
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Gait Analysis ◽  
Pose Estimation ◽  
Early Identification ◽  
Human Gait ◽  
3D Pose Estimation ◽  
Wearable Technologies ◽  
Advantages And Disadvantages

AbstractBetter handling of neurological or neurodegenerative disorders such as Parkinson’s Disease (PD) is only possible with an early identification of relevant symptoms. Although the entire disease can’t be treated but the effects of the disease can be delayed with proper care and treatment. Due to this fact, early identification of symptoms for the PD plays a key role. Recent studies state that gait abnormalities are clearly evident while performing dual cognitive tasks by people suffering with PD. Researches also proved that the early identification of the abnormal gaits leads to the identification of PD in advance. Novel technologies provide many options for the identification and analysis of human gait. These technologies can be broadly classified as wearable and non-wearable technologies. As PD is more prominent in elderly people, wearable sensors may hinder the natural persons movement and is considered out of scope of this paper. Non-wearable technologies especially Image Processing (IP) approaches captures data of the person’s gait through optic sensors Existing IP approaches which perform gait analysis is restricted with the parameters such as angle of view, background and occlusions due to objects or due to own body movements. Till date there exists no researcher in terms of analyzing gait through 3D pose estimation. As deep leaning has proven efficient in 2D pose estimation, we propose an 3D pose estimation along with proper dataset. This paper outlines the advantages and disadvantages of the state-of-the-art methods in application of gait analysis for early PD identification. Furthermore, the importance of extracting the gait parameters from 3D pose estimation using deep learning is outlined.

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