scholarly journals Wearable Sensors: Large‐Scale Patterning of Reactive Surfaces for Wearable and Environmentally Deployable Sensors (Adv. Mater. 28/2020)

2020 ◽  
Vol 32 (28) ◽  
pp. 2070213
Author(s):  
Giusy Matzeu ◽  
Laia Mogas‐Soldevila ◽  
Wenyi Li ◽  
Arin Naidu ◽  
Trent H. Turner ◽  
...  
Keyword(s):  
Large Scale ◽  
Wearable Sensors ◽  
Reactive Surfaces

A Survey of Railyard Worker Protection Approaches and System Design Considerations

2017 ◽  
Author(s):  
Subharthi Banerjee ◽  
Michael Hempel ◽  
Hamid Sharif
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
System Design ◽  
Large Scale ◽  
Wearable Sensors ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Railroad Industry ◽  
Proximity Detection ◽  
Non Line Of Sight

Railroad environments are generally considered to be among the most dynamic workplace environments, even with constant improvement efforts by the railroad industry. While there has been great progress in equipment safety, personnel safety is a significantly harder challenge. These challenges are primarily derived from the presence of heavy moving machinery in close proximity to personnel and the difficulty of designing reliable wearable protection devices. Additionally, variable weather conditions, challenging walking conditions (ballast, trip hazards, etc.), and difficulty to focus on environment, moving objects, and on tasks at hand place the employees in constant peril. Therefore, our survey is focused on exploring solutions for protecting employees through unified system modeling and design that makes the employee integral to the process and results in personal protective devices that work with the environment and the employee, not against them. The optimal system design integrates not only protection of the employees from falls, unsafe practices, or collisions, but also aids in resource planning, safe operation and accounting of “near-miss” situations. In recent years the railroads have made significant investments in process automation and monitoring solutions such as Wireless Sensor Networks. These technologies are becoming increasingly cloud-connected and autonomous. They provide a plethora of information about equipment positions, movement, railcar lading, and many other factors, all of which are highly useful in the design and implementation of a railyard worker protection system. They allow us to predict position and movement, and can thus be used to provide effective proximity detection and alerting in some railyard regions where these systems are installed. Additionally, we discuss several technologies addressing near-collision, fall, and proximity situations through RF and non-RF-based techniques. The railroad industry has been advancing efforts leveraging these technologies to improve the safety of their workers. However, there are also many challenges that remain largely unaddressed. For example, in railroads, a detailed and exhaustive causation analysis for worker incidents has yet to be conducted. Therefore, in an environment like a railyard there is no solution to detect or prevent Employee on Duty (EOD) fall, collision, or health issues such as dehydration, psychological issues and high blood pressure. Protective devices worn by workers is believed to be one of the most important, cost-effective, and scalable potential candidate solutions. Recent advances are making wearable wireless body area networks (WBAN) and wireless sensor networks (WSNs) that are distributed and large-scale a reality. Such distributed networks consist of wearable sensors, fixed-installation sensors and communication links between all of them. The challenges are found in selecting wearable sensors, researching reliable communication among nodes without interfering with proximity detection and suitable for high-multipath, non-line of sight channel conditions, wearable antenna designs, power supply requirements, etc. A dense, distributed, large-scale environment like a railyard requires comprehensive workspace modelling and safety analysis. Interference related to RF sensor deployment, blind spots in vision-based approaches, and wireless propagation in intra and inter-WBAN communication due to dense non-Line-of-Sight workspace environments, metallic heavy machinery and the use of RF sensors, are all individual research challenges in this domain. This paper reviews these challenges, explores potential solutions, and thus provides a comprehensive survey of a holistic system design approach for a wearable railyard worker protection system that is unobtrusive, effective, and reliable.

scholarly journals A new circular spinneret system for electrospinning numerical approach and electric field optimization

2019 ◽  
Vol 23 (4) ◽  
pp. 2229-2235 ◽  
Author(s):  
Ziming Zhu ◽  
Han Wang ◽  
Guojie Xu ◽  
Rouxi Chen ◽  
Lixiong Huang ◽  
...  
Keyword(s):  
Tissue Engineering ◽  
Finite Element ◽  
Electric Field ◽  
Large Scale ◽  
Energy Harvester ◽  
Wearable Sensors ◽  
Element Model ◽  
Numerical Approach ◽  
Electrospinning Process ◽  
Effective Technique

Electrospinning is believed to be the most effective technique to produce microfibers or nanofibers at large scale, which can be applied in various hightech areas, including energy harvester, tissue engineering, and wearable sensors. To enhance nanofiber throughput during a multi-needle electrospinning process, it is an effective way to keep the electric field uniform by optimizing electrospinning spinnerets. For this purpose, a novel circular spinneret system is designed and optimized numerically by a 3-D finite element model, the optimal collector shape is also obtained.

scholarly journals Smart Distance Lab Art Fair - An experimental data set on social distancing during the COVID-19 pandemic

2020 ◽  
Author(s):  
Charlotte Coosje Tanis ◽  
Nina Leach ◽  
Sandra Jeanette Geiger ◽  
Floor H Nauta ◽  
Fabian Dablander ◽  
...  
Keyword(s):  
Experimental Data ◽  
Large Scale ◽  
Wearable Sensors ◽  
Face Mask ◽  
Data Set ◽  
Behavioural Interventions ◽  
Social Distancing ◽  
Behavioural Experiment ◽  
Environmental Measures ◽  
Insight Into

In the absence of a vaccine, social distancing behaviour is pivotal to mitigate COVID-19 virus spread. In this large-scale behavioural experiment, we gathered data during Smart Distance Lab: The Art Fair (n = 787) between August 28 and 30, 2020 in Amsterdam, the Netherlands. We varied walking directions (bidirectional, unidirectional, and no directions) and supplementary interventions (face mask and buzzer to alert visitors of 1.5 metres distance). We captured visitors' movements using cameras, registered their contacts (defined as within 1.5 metres) using wearable sensors, and assessed their attitudes toward COVID-19 as well as their experience during the event using questionnaires. We also registered environmental measures (e.g., humidity). In this paper, we describe this unprecedented, multi-modal experimental data set on social distancing, including psychological, behavioural, and environmental measures. The data set is available on Figshare and in a MySQL database. It can be used to gain insight into (attitudes toward) behavioural interventions promoting social distancing, to calibrate pedestrian models, and to inform new studies on behavioural interventions.

scholarly journals Large‐Scale Patterning of Reactive Surfaces for Wearable and Environmentally Deployable Sensors

2020 ◽  
Vol 32 (28) ◽  
pp. 2001258 ◽  
Author(s):  
Giusy Matzeu ◽  
Laia Mogas‐Soldevila ◽  
Wenyi Li ◽  
Arin Naidu ◽  
Trent H. Turner ◽  
...  
Keyword(s):  
Large Scale ◽  
Reactive Surfaces

scholarly journals Predicting Fall Risk Through Automatic Wearable Monitoring

2021 ◽  
Vol 12 (4) ◽  
Author(s):  
Markey Olson ◽  
Thurmon Lockhart
Keyword(s):  
Machine Learning ◽  
Fall Risk ◽  
Large Scale ◽  
Meta Analysis ◽  
Wearable Sensors ◽  
Vital Signs ◽  
Step Length ◽  
Free Living ◽  
Learning Methods ◽  
Machine Learning Methods

Falls represent a major burden on elderly individuals and society as a whole. Technologies that are able to detect individuals at risk of fall before occurrence could help reduce this burden by targeting those individuals for rehabilitation to reduce risk of falls. Wearable technologies especially, which can continuously monitor aspects of gait, balance, vital signs, and other aspects of health known to be related to falls, may be useful and are in need of study. A systematic review was conducted in accordance with the Preferred Reporting Items for Systematics Reviews and Meta-Analysis (PRISMA) 2009 guidelines to identify articles related to the use of wearable sensors to predict fall risk. Fifty four studies were analyzed. The majority of studies (98.0%) utilized inertial measurement units (IMUs) located at the lower back (58.0%), sternum (28.0%), and shins (28.0%). Most assessments were conducted in a structured setting (67.3%) instead of with free-living data. Fall risk was calculated based on retrospective falls history (48.9%), prospective falls reporting (36.2%), or clinical scales (19.1%). Measures of the duration spent walking and standing during free-living monitoring, linear measures such as gait speed and step length, and nonlinear measures such as entropy correlate with fall risk, and machine learning methods can distinguish between falls. However, because many studies generating machine learning models did not list the exact factors being considered, it is difficult to compare these models directly. Few studies to date have utilized results to give feedback about fall risk to the patient or to supply treatment or lifestyle suggestions to prevent fall, though these are considered important by end users. Wearable technology demonstrates considerable promise in detecting subtle changes in biomarkers of gait and balance related to an increase in fall risk. However, more large-scale studies measuring increasing fall risk before first fall are needed, and exact biomarkers and machine learning methods used need to be shared to compare results and pursue the most promising fall risk measurements. There is a great need for devices measuring fall risk also to supply patients with information about their fall risk and strategies and treatments for prevention.

scholarly journals Digital health in the age of The Infinite Network

2016 ◽  
Vol 5 ◽  
Author(s):  
Nikhil Balram ◽  
Ivana Tošić ◽  
Harsha Binnamangalam
Keyword(s):  
Large Scale ◽  
Digital Health ◽  
Wearable Sensors ◽  
Embedded Sensors ◽  
Individual Level ◽  
Continuous Approach ◽  
Infinite Network ◽  
Remote Care ◽  
The Impact

The exponential growth in digital technology is leading us to a future in which all things and all people are connected all the time, something we refer to as The Infinite Network (TIN), which will cause profound changes in every industry. Here, we focus on the impact it will have in healthcare. TIN will change the essence of healthcare to a data-driven continuous approach as opposed to the event-driven discrete approach used today. At a micro or individual level, smart sensing will play a key role, in the form of embedded sensors, wearable sensors, and sensing from smart medical devices. At a macro or aggregate level, healthcare will be provided by Intelligent Telehealth Networks that evolve from the telehealth networks that are available today. Traditional telemedicine has delivered remote care to patients in the area where doctors are not readily available, but has not achieved at large scale. New advanced networks will deliver care at a much larger scale. The long-term future requires intelligent hybrid networks that combine artificial intelligence with human intelligence to provide continuity of care at higher quality and lower cost than is possible today.

scholarly journals Wearable Sensors for Monitoring Human Motion: A Review on Mechanisms, Materials, and Challenges

2019 ◽  
Vol 25 (1) ◽  
pp. 9-24 ◽  
Author(s):  
S. Zohreh Homayounfar ◽  
Trisha L. Andrew
Keyword(s):  
Large Scale ◽  
Large Range ◽  
State Of The Art ◽  
Wearable Sensors ◽  
Human Locomotion ◽  
Human Motion ◽  
Human Gait ◽  
Wearable Electronics ◽  
New Horizons ◽  
User Friendly

The emergence of flexible wearable electronics as a new platform for accurate, unobtrusive, user-friendly, and longitudinal sensing has opened new horizons for personalized assistive tools for monitoring human locomotion and physiological signals. Herein, we survey recent advances in methodologies and materials involved in unobtrusively sensing a medium to large range of applied pressures and motions, such as those encountered in large-scale body and limb movements or posture detection. We discuss three commonly used methodologies in human gait studies: inertial, optical, and angular sensors. Next, we survey the various kinds of electromechanical devices (piezoresistive, piezoelectric, capacitive, triboelectric, and transistive) that are incorporated into these sensor systems; define the key metrics used to quantitate, compare, and optimize the efficiency of these technologies; and highlight state-of-the-art examples. In the end, we provide the readers with guidelines and perspectives to address the current challenges of the field.

scholarly journals Feasibility of large-scale deployment of multiple wearable sensors in Parkinson's disease

PLoS ONE ◽  
2017 ◽  
Vol 12 (12) ◽  
pp. e0189161 ◽  
Author(s):  
Ana Lígia Silva de Lima ◽  
Tim Hahn ◽  
Luc J. W. Evers ◽  
Nienke M. de Vries ◽  
Eli Cohen ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Large Scale ◽  
Wearable Sensors

scholarly journals Non-motor correlates of wrist-worn wearable sensor use in Parkinson’s disease: an exploratory analysis

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Daniel J. van Wamelen ◽  
Shweta Hota ◽  
Aleksandra Podlewska ◽  
Valentina Leta ◽  
Dhaval Trivedi ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gastrointestinal Tract ◽  
Large Scale ◽  
Disease Duration ◽  
Wearable Sensors ◽  
International Study ◽  
Exploratory Analysis ◽  
College Hospital ◽  
Gastrointestinal Problems

Abstract Wearable sensors are becoming increasingly more available in Parkinson’s disease and are used to measure motor function. Whether non-motor symptoms (NMS) can also be measured with these wearable sensors remains unclear. We therefore performed a retrospective, exploratory, analysis of 108 patients with a diagnosis of idiopathic Parkinson’s disease enroled in the Non-motor Longitudinal International Study (UKCRN No. 10084) at King’s College Hospital, London, to determine the association between the range and nature of NMS and an accelerometer-based outcome measure of bradykinesia (BKS) and dyskinesia (DKS). NMS were assessed by the validated NMS Scale, and included, e.g., cognition, mood and sleep, and gastrointestinal, urinary and sexual problems. Multiple linear regression modelling was used to identify NMS associated with BKS and DKS. We found that BKS was associated with domains 6 (gastrointestinal tract; p = 0.006) and 8 (sexual function; p = 0.003) of the NMS scale. DKS was associated with domains 3 (mood/cognition; p = 0.016), 4 (perceptual problems; p = 0.025), 6 (gastrointestinal tract; p = 0.029) and 9 (miscellaneous, p = 0.003). In the separate domains, constipation was significantly associated with BKS. Delusions, dysphagia, hyposmia, weight change and hyperhidrosis were identified as significantly associated with DKS. None of the NMSS domains were associated with disease duration (p ≥ 0.08). In conclusion, measures of BKS and DKS were mainly associated with gastrointestinal problems, independent of disease duration, showing the potential for wearable devices to pick up on these symptoms. These exploratory results deserve further exploration, and more research on this topic in the form of comprehensive large-scale studies is needed.

scholarly journals Feasibility of continuous fever monitoring using wearable devices

2020 ◽  
Author(s):  
Benjamin Smarr ◽  
Kirstin Aschbacher ◽  
Sarah M. Fisher ◽  
Anoushka Chowdhary ◽  
Stephan Dilchert ◽  
...  
Keyword(s):  
Large Scale ◽  
Wearable Sensors ◽  
Wearable Devices ◽  
Temperature Sensors ◽  
Physiological Data ◽  
High Temporal Resolution ◽  
The Novel ◽  
Illness Onset ◽  
Novel Coronavirus ◽  
Peripheral Temperature

Abstract Elevated core temperature constitutes an important biomarker for COVID-19 infection; however, no standards currently exist to monitor fever using wearable peripheral temperature sensors. Evidence that sensors could be used to develop fever monitoring capabilities would enable large-scale health-monitoring research and provide high-temporal resolution data on fever responses across heterogeneous populations. We launched the TemPredict study in March of 2020 to capture continuous physiological data, including peripheral temperature, from a commercially available wearable device during the novel coronavirus pandemic. We coupled these data with symptom reports and COVID-19 diagnosis data. Here we report findings from the first 50 subjects who reported COVID-19 infections. These cases provide the first evidence that illness-associated elevations in peripheral temperature are observable using wearable devices and correlate with self-reported fever. Our analyses support the hypothesis that wearable sensors can detect illnesses in the absence of symptom recognition. Finally, these data support the hypothesis that prediction of illness onset is possible using continuously generated physiological data collected by wearable sensors. Our findings should encourage further research into the role of wearable sensors in public health efforts aimed at illness detection, and underscore the importance of integrating temperature sensors into commercially available wearables.

Sign in / Sign up

Export Citation Format

Share Document