Evaluating the Impact of Location-Aware Sensor Data Imperfections on Autonomous Jobsite Safety Monitoring

The growing demand for extensive and reliable structural health monitoring resulted in the development of advanced optical sensing systems (OSS) that in conjunction with wireless optical networks (WON) are capable of extending the reach of optical sensing to places where fibre provision is not feasible. To support this effort, the paper proposes a new type of a variable weight code called multiweight zero cross-correlation (MW-ZCC) code for its application in wireless optical networks based optical code division multiple access (WON-OCDMA). The code provides improved quality of service (QoS) and better support for simultaneous transmission of video surveillance, comms and sensor data by reducing the impact of multiple access interference (MAI). The MW-ZCC code’s power of two code-weight properties provide enhanced support for the needed service differentiation provisioning. The performance of this novel code has been studied by simulations. This investigation revealed that for a minimum allowable bit error rate of 10−3, 10−9 and 10−12 when supporting triple-play services (sensing, datacomms and video surveillance, respectively), the proposed WON-OCDMA using MW-ZCC codes could support up to 32 simultaneous services over transmission distances up to 32 km in the presence of moderate atmospheric turbulence.

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Ambient Air Quality Measurement with Low-Cost Optical and Electrochemical Sensors: An Evaluation of Continuous Year-Long Operation

Environments ◽

10.3390/environments8110114 ◽

2021 ◽

Vol 8 (11) ◽

pp. 114

Author(s):

Jiří Bílek ◽

Ondřej Bílek ◽

Petr Maršolek ◽

Pavel Buček

Keyword(s):

Air Pollution ◽

Air Quality ◽

Sensor Network ◽

Electrochemical Sensors ◽

Reference Method ◽

Ambient Air ◽

Sensor Data ◽

Dust Particles ◽

Concentration Changes ◽

The Impact

Sensor technology is attractive to the public due to its availability and ease of use. However, its usage raises numerous questions. The general trustworthiness of sensor data is widely discussed, especially with regard to accuracy, precision, and long-term signal stability. The VSB-Technical University of Ostrava has operated an air quality sensor network for more than two years, and its large sets of valid results can help in understanding the limitations of sensory measurement. Monitoring is focused on the concentrations of dust particles, NO2, and ozone to verify the impact of newly planted greenery on the reduction in air pollution. The sensor network currently covers an open field on the outskirts of Ostrava, between Liberty Ironworks and the nearby ISKO1650 monitoring station, where some of the worst air pollution levels in the Czech Republic are regularly measured. In the future, trees should be allowed to grow over the sensors, enabling assessment of the green barrier effect on air pollution. As expected, the service life of the sensors varies from 1 to 3 years; therefore, checks are necessary both prior to the measurement and regularly during operation, verifying output stability and overall performance. Results of the PMx sensory measurements correlated well with the reference method. Concentration values measured by NO2 sensors correlated poorly with the reference method, although timeline plots of concentration changes were in accordance. We suggest that a comparison of timelines should be used for air quality evaluations, rather than particular values. The results showed that the sensor measurements are not yet suitable to replace the reference methods, and dense sensor networks proved useful and robust tools for indicative air quality measurements (AQM).

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Internet of Things in E-Health

Emerging Trends and Applications of the Internet of Things - Advances in Wireless Technologies and Telecommunication ◽

10.4018/978-1-5225-2437-3.ch007 ◽

2017 ◽

pp. 191-197 ◽

Cited By ~ 1

Author(s):

Branka Rodić Trmčić ◽

Aleksandra Labus ◽

Svetlana Mitrović ◽

Vesna Buha ◽

Gordana Stanojević

Keyword(s):

Internet Of Things ◽

Health Behavior ◽

Well Being ◽

Sensor Data ◽

Main Task ◽

Preventive Actions ◽

Framework Model ◽

The Impact ◽

Health And Well Being ◽

Improve Health

The main task of Internet of Things in eHealth solutions is to collect data, connect people, things and processes. This provides a wealth of information that can be useful in decision-making, improving health and well-being. The aim of this study is to identify framework of sensors and application health services to detect sources of stress and stressors and make them visible to users. Also, we aim at extracting relationship between event and sensor data in order to improve health behavior. Evaluation of the proposed framework model will be performed. Model is based on Internet of Things in eHealth and is going to aim to improve health behavior. Following the established pattern of behavior realized through wearable system users will be proposed a preventive actions model. Further, it will examine the impact of changing health behavior on habits, condition and attitudes in relation to well-being and prevention.

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Making Location-Aware Computing Working Accurately in Smart Spaces

Handbook of Research on Mobility and Computing ◽

10.4018/978-1-60960-042-6.ch035 ◽

2011 ◽

pp. 539-557

Author(s):

Teddy Mantoro ◽

Media Ayu ◽

Maarten Weyn

Keyword(s):

Dynamic Environment ◽

Sensor Data ◽

Smart Environment ◽

Nearest Neighbour ◽

Localization Algorithm ◽

Personal Computing ◽

Location Aware ◽

Computing Vision ◽

User Location

In smart environment, making a location-aware personal computing working accurately is a way of getting close to the pervasive computing vision. The best candidate to determine a user location in indoor environment is by using IEEE 802.11 (Wi-Fi) signals, since it is more and more widely available and installed on most mobile devices used by users. Unfortunately, the signal strength, signals quality and noise of Wi-Fi, in worst scenario, it fluctuates up to 33% because of the reflection, refraction, temperature, humidity, the dynamic environment, etc. We present our current development on a light-weight algorithm, which is easy, simple but robust in producing the determination of user location using WiFi signals. The algorithm is based on “multiple observers” on ?k-Nearest Neighbour. We extend our approach in the estimation indoor-user location by using combination of different technologies, i.e. WiFi, GPS, GSM and Accelerometer. The algorithm is based on opportunistic localization algorithm and fuse different sensor data in order to be able to use the data which is available at the user position and processable in a mobile device.

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Challenges in Tuberculosis Clinical Trials in the Face of the COVID-19 Pandemic: A Sponsor’s Perspective

Tropical Medicine and Infectious Disease ◽

10.3390/tropicalmed5020086 ◽

2020 ◽

Vol 5 (2) ◽

pp. 86

Author(s):

I.D. Rusen

Keyword(s):

Clinical Trials ◽

Safety Monitoring ◽

Chain Management ◽

Mdr Tb ◽

The Face ◽

System Functioning ◽

Treatment Continuity ◽

The Impact ◽

Trial Participants

The COVID-19 pandemic has caused unforeseen and extreme changes in societal and health system functioning not previously experienced in most countries in a lifetime. The impact of the pandemic on clinical trials can be especially profound given their complexities and operational requirements. The STREAM Clinical Trial is the largest trial for MDR-TB ever conducted. Currently operating in seven countries, the trial had 126 participants on treatment and 312 additional participants in active follow up as of March 31, 2020. Areas of particular concern during this global emergency include treatment continuity, supply chain management and participant safety monitoring. This commentary highlights some of the challenges faced due to the pandemic and the steps taken to protect the safety of trial participants and the integrity of the trial.

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Optical Wireless Network Design for Off-Body-Sensor Based Monitoring

Wireless Communications and Mobile Computing ◽

10.1155/2019/5473923 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 7

Author(s):

T. B. Hoang ◽

S. Sahuguede ◽

A. Julien-Vergonjanne

Keyword(s):

Health Monitoring ◽

Visible Light Communication ◽

The Body ◽

Sensor Data ◽

Body Parts ◽

Optical Technology ◽

Wireless Network Design ◽

Response Sets ◽

Trade Offs ◽

The Impact

In this article, we propose an all-optical bidirectional wireless communication system for off-body sensor communication. Optical technology uses infrared (IR) for uplinks and visible light communication (VLC) for downlinks. From numerical simulations, we discuss the impact of body sensor positions on IR and VLC channels. Our goal is to evaluate the possibilities of using optical technology to transmit sensor data for extreme positions such as the ankle, for which the presence of the body creates blockages. In addition, we also consider the variations in orientation of transceivers due to random mobility of body parts during normal movement. Based on a statistical approach, we evaluate performance in terms of outage probability using channel impulse response sets corresponding to the studied scenario, which is health monitoring. Considering a given quality of service, we address trade-offs related to emitting power and data rate. We discuss the results regarding sensor node position and body reflectivity specifically for ankle sensors, corresponding to an extreme but realistic position in the health-monitoring context.

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Practical Considerations for Accuracy Evaluation in Sensor-Based Machine Learning and Deep Learning

Sensors ◽

10.3390/s19163491 ◽

2019 ◽

Vol 19 (16) ◽

pp. 3491 ◽

Cited By ~ 1

Author(s):

Issam Hammad ◽

Kamal El-Sankary

Keyword(s):

Machine Learning ◽

Thermal Noise ◽

Error Resilience ◽

Sensor Data ◽

Accuracy Evaluation ◽

Sensor Failure ◽

Learning Models ◽

Analog To Digital ◽

The Impact ◽

Machine Learning Models

Accuracy evaluation in machine learning is based on the split of data into a training set and a test set. This critical step is applied to develop machine learning models including models based on sensor data. For sensor-based problems, comparing the accuracy of machine learning models using the train/test split provides only a baseline comparison in ideal situations. Such comparisons won’t consider practical production problems that can impact the inference accuracy such as the sensors’ thermal noise, performance with lower inference quantization, and tolerance to sensor failure. Therefore, this paper proposes a set of practical tests that can be applied when comparing the accuracy of machine learning models for sensor-based problems. First, the impact of the sensors’ thermal noise on the models’ inference accuracy was simulated. Machine learning algorithms have different levels of error resilience to thermal noise, as will be presented. Second, the models’ accuracy using lower inference quantization was compared. Lowering inference quantization leads to lowering the analog-to-digital converter (ADC) resolution which is cost-effective in embedded designs. Moreover, in custom designs, analog-to-digital converters’ (ADCs) effective number of bits (ENOB) is usually lower than the ideal number of bits due to various design factors. Therefore, it is practical to compare models’ accuracy using lower inference quantization. Third, the models’ accuracy tolerance to sensor failure was evaluated and compared. For this study, University of California Irvine (UCI) ‘Daily and Sports Activities’ dataset was used to present these practical tests and their impact on model selection.

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Rapid Multi-Dimensional Impact Assessment of Floods

Sustainability ◽

10.3390/su12104246 ◽

2020 ◽

Vol 12 (10) ◽

pp. 4246 ◽

Cited By ~ 1

Author(s):

David Pastor-Escuredo ◽

Yolanda Torres ◽

María Martínez-Torres ◽

Pedro J. Zufiria

Keyword(s):

Social Media ◽

Impact Assessment ◽

Rural Areas ◽

Disaster Response ◽

Heterogeneous Data ◽

Data Driven ◽

Sensor Data ◽

Mobile Phone Data ◽

Aggregated Data ◽

The Impact

Natural disasters affect hundreds of millions of people worldwide every year. The impact assessment of a disaster is key to improve the response and mitigate how a natural hazard turns into a social disaster. An actionable quantification of impact must be integratively multi-dimensional. We propose a rapid impact assessment framework that comprises detailed geographical and temporal landmarks as well as the potential socio-economic magnitude of the disaster based on heterogeneous data sources: Environment sensor data, social media, remote sensing, digital topography, and mobile phone data. As dynamics of floods greatly vary depending on their causes, the framework may support different phases of decision-making during the disaster management cycle. To evaluate its usability and scope, we explored four flooding cases with variable conditions. The results show that social media proxies provide a robust identification with daily granularity even when rainfall detectors fail. The detection also provides information of the magnitude of the flood, which is potentially useful for planning. Network analysis was applied to the social media to extract patterns of social effects after the flood. This analysis showed significant variability in the obtained proxies, which encourages the scaling of schemes to comparatively characterize patterns across many floods with different contexts and cultural factors. This framework is presented as a module of a larger data-driven system designed to be the basis for responsive and more resilient systems in urban and rural areas. The impact-driven approach presented may facilitate public–private collaboration and data sharing by providing real-time evidence with aggregated data to support the requests of private data with higher granularity, which is the current most important limitation in implementing fully data-driven systems for disaster response from both local and international actors.

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THUR 198 Safe monitoring of natalizumab therapy in multiple sclerosis

Journal of Neurology Neurosurgery & Psychiatry ◽

10.1136/jnnp-2018-abn.99 ◽

2018 ◽

Vol 89 (10) ◽

pp. A29.1-A29

Author(s):

Hosty J ◽

Kass-Iliyya L ◽

Bell S ◽

Barker L ◽

Packwood S ◽

...

Keyword(s):

Multiple Sclerosis ◽

Jc Virus ◽

Safety Monitoring ◽

Brain Infection ◽

Audit Data ◽

Relapsing Remitting ◽

Mri Scans ◽

Specialist Nurses ◽

Relapsing Remitting Multiple Sclerosis ◽

The Impact

Natalizumab is one of the most effective therapies for relapsing-remitting Multiple Sclerosis. One complication is Progressive Multifocal Leucoencephalopathy (PML), a viral brain infection in patients already infected with JC virus. Monitoring of neurological symptoms, JC virus serology and regular brain imaging are required to ensure safe use of this therapy. Local audit data from 2015 indicated poor compliance with safety monitoring, with less than 25% of patients undergoing required investigations within the recommended time intervals. Subsequently a protocol was implemented to improve monitoring, with specialist nurses coordinating the requests for MRI scans and arranging JC virus serology, the frequency of which was determined according to the JC virus index. The records of all patients receiving Natalizumab at the centre were audited to assess the impact of this protocol (n=155). 99.2% of patients were appropriately tested for JC virus and 95.3% were imaged within the recommended interval. Additional work with the informatics and virology team ensured serology results became more easily accessible. The use of a standardised nurse-led operating procedure has resulted in marked improvement in the safety monitoring of Natalizumab.

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