scholarly journals Impact of Multi-Sensor Technology for Enhancing Global Security in Closed Environments Using Cloud-Based Resources

2019 ◽  
Vol 8 (1) ◽  
pp. 4 ◽  
Author(s):  
Saleh Altowaijri ◽  
Mohamed Ayari ◽  
Yamen El Touati
Keyword(s):  
Health Status ◽  
Human Motion ◽  
Cloud Services ◽  
Sensor Data ◽  
Sensor Technology ◽  
Easy Access ◽  
Critical Material ◽  
Professional Activities ◽  
Novel Approach ◽  
Oxygen Ratio

By nature, some jobs are always in closed environments and employees may stay for long periods. This is the case for many professional activities such as military watch tours of borders, civilian buildings and facilities that need efficient control processes. The role assigned to personnel in such environments is usually sensitive and of high importance, especially in terms of security and protection. With this in mind, we proposed in our research a novel approach using multi-sensor technology to monitor many safety and security parameters including the health status of indoor workers, such as those in watchtowers and at guard posts. In addition, the data gathered for those employees (heart rate, temperature, eye movement, human motion, etc.) combined with the room’s sensor data (temperature, oxygen ratio, toxic gases, air quality, etc.) were saved by appropriate cloud services, which ensured easy access to the data without ignoring the privacy protection aspect of such critical material. This information can be used later by specialists to monitor the evolution of the worker’s health status as well as its cost-effectiveness, which gives the possibility to improve productivity in the workplace and general employee health.

Quality by Design: Concept to Applications

2019 ◽  
Vol 16 (3) ◽  
pp. 240-250 ◽  
Author(s):  
Suryakanta Swain ◽  
Rabinarayan Parhi ◽  
Bikash Ranjan Jena ◽  
Sitty Manohar Babu
Keyword(s):  
Quality By Design ◽  
Pharmaceutical Products ◽  
Assessment Tools ◽  
Basic Knowledge ◽  
Target Product ◽  
Target Product Profile ◽  
Critical Material ◽  
Novel Approach ◽  
Product Profile ◽  
Performance Report

Background: Quality by Design (QbD) is associated with a modern, systematic, scientific and novel approach which is concerned with pre-distinct objectives that not only focus on product, process understanding but also lead to process control. It predominantly signifies the design and product improvement and the manufacturing process in order to fulfill the predefined manufactured goods or final products quality characteristics. It is quite essential to identify the desired and required product performance report, such as Target Product Profile, typical Quality Target Product Profile (QTPP) and Critical Quality Attributes (CQA). Methods: This review highlighted the concepts of QbD design space, for critical material attributes (CMAs) as well as the critical process parameters that can totally affect the CQAs within which the process shall be unaffected thus, consistently manufacturing the required product. Risk assessment tools and design of experiments are its prime components. Results: This paper outlines the basic knowledge of QbD, the key elements; steps as well as various tools for QbD implementation in pharmaceutics field are presented briefly. In addition to this, quite a lot of applications of QbD in numerous pharmaceutical related unit operations are discussed and summarized. Conclusion: This article provides a complete data as well as the roadmap for universal implementation and application of QbD for pharmaceutical products.

scholarly journals Non-Linear Chaotic Features-Based Human Activity Recognition

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 111
Author(s):  
Pengjia Tu ◽  
Junhuai Li ◽  
Huaijun Wang ◽  
Ting Cao ◽  
Kan Wang
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Human Motion ◽  
Sensor Data ◽  
Largest Lyapunov Exponent ◽  
Optimal Delay ◽  
Motion Time ◽  
Non Linear ◽  
Optimal Delay Time

Human activity recognition (HAR) has vital applications in human–computer interaction, somatosensory games, and motion monitoring, etc. On the basis of the human motion accelerate sensor data, through a nonlinear analysis of the human motion time series, a novel method for HAR that is based on non-linear chaotic features is proposed in this paper. First, the C-C method and G-P algorithm are used to, respectively, compute the optimal delay time and embedding dimension. Additionally, a Reconstructed Phase Space (RPS) is formed while using time-delay embedding for the human accelerometer motion sensor data. Subsequently, a two-dimensional chaotic feature matrix is constructed, where the chaotic feature is composed of the correlation dimension and largest Lyapunov exponent (LLE) of attractor trajectory in the RPS. Next, the classification algorithms are used in order to classify and recognize the two different activity classes, i.e., basic and transitional activities. The experimental results show that the chaotic feature has a higher accuracy than traditional time and frequency domain features.

scholarly journals Machine Learning Solutions for Bridge Scour Forecast Based on Monitoring Data

2021 ◽  
pp. 036119812110126
Author(s):  
Negin Yousefpour ◽  
Steve Downie ◽  
Steve Walker ◽  
Nathan Perkins ◽  
Hristo Dikanski
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring Data ◽  
Sensor Data ◽  
Monitoring Systems ◽  
Bridge Scour ◽  
Scour Monitoring ◽  
Novel Approach ◽  
Bayesian Inference Method ◽  
Water Level Variations

Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.

scholarly journals Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

2021 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Parag Narkhede ◽  
Rahee Walambe ◽  
Shruti Mandaokar ◽  
Pulkit Chandel ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Network Architecture ◽  
Cost Effective ◽  
Sensor Data ◽  
Dense Layer ◽  
Gaseous Emissions ◽  
Multiple Sensors ◽  
Single Output ◽  
Novel Approach ◽  
Single Sensor

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.

scholarly journals Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure

AI Magazine ◽  
2012 ◽  
Vol 33 (2) ◽  
pp. 55 ◽  
Author(s):  
Nisarg Vyas ◽  
Jonathan Farringdon ◽  
David Andre ◽  
John Ivo Stivoric
Keyword(s):  
Machine Learning ◽  
Energy Expenditure ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Sensor Technology ◽  
Health Goals ◽  
System A ◽  
Multi Sensor Data Fusion ◽  
Learning Techniques ◽  
Insight Into

In this article we provide insight into the BodyMedia FIT armband system — a wearable multi-sensor technology that continuously monitors physiological events related to energy expenditure for weight management using machine learning and data modeling methods. Since becoming commercially available in 2001, more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight-loss. We describe several challenges that arise in applying machine learning techniques to the health care domain and present various solutions utilized in the armband system. We demonstrate how machine learning and multi-sensor data fusion techniques are critical to the system’s success.

Validation of Wearable Sensor Technology to Measure Social Proximity of Young Children With Autism Spectrum Disorders

2021 ◽  
pp. 108835762110282
Author(s):  
Sarah N. Douglas ◽  
Yan Shi ◽  
Saptarshi Das ◽  
Subir Biswas
Keyword(s):  
Autism Spectrum Disorders ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Sensor System ◽  
Sensor Data ◽  
Sensor Technology ◽  
Future Research ◽  
Social Proximity ◽  
Children With Asd ◽  
Spectrum Disorders

Children with autism spectrum disorders (ASD) struggle to develop appropriate social skills, which can lead to later social rejection, isolation, and mental health concerns. Educators play an important role in supporting and monitoring social skill development for children with ASD, but the tools used by educators are often tedious, lack suitable sensitivity, provide limited information to plan interventions, and are time-consuming. Therefore, we conducted a study to evaluate the use of a sensor system to measure social proximity between three children with ASD and their peers in an inclusive preschool setting. We compared video-coded data with sensor data using point-by-point agreement to measure the accuracy of the sensor system. Results suggest that the sensor system can adequately measure social proximity between children with ASD and their peers. The next steps for sensor system validation are discussed along with clinical and educational implications, limitations, and future research directions.

scholarly journals Predicting Parkinson’s Disease Medication Regimen Using Sensor Technology

2021 ◽  
Author(s):  
Jeremy Watts ◽  
Anahita Khojandi ◽  
Rama Vasudevan ◽  
Fatta B. Nahab ◽  
Ritesh Ramdhani
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Classification Model ◽  
Sensor Data ◽  
Sensor Technology ◽  
Medication Regimen ◽  
Levodopa Dose ◽  
Subjective Data ◽  
Random Forest Classification

Abstract Parkinson’s disease (PD) medication treatment planning is generally based on subjective data through in-office, physicianpatient interactions. The Personal KinetiGraphTM (PKG) has shown promise in enabling objective, continuous remote health monitoring for Parkinson’s patients. In this proof-of-concept study, we propose to use objective sensor data from the PKG and apply machine learning to subtype patients based on levodopa regimens and response. We apply k-means clustering to a dataset of with-in-subject Parkinson’s medication changes—clinically assessed by the PKG and Hoehn & Yahr (H&Y) staging. A random forest classification model was then used to predict patients’ cluster allocation based on their respective PKG data and demographic information. Clinically relevant clusters were developed based on longitudinal dopaminergic regimens—partitioned by levodopa dose, administration frequency, and total levodopa equivalent daily dose—with the PKG increasing cluster granularity compared to the H&Y staging. A random forest classifier was able to accurately classify subjects of the two most demographically similar clusters with an accuracy of 87:9 ±1:3

scholarly journals Seasonal Influence on the Performance of Low-Cost NO2 Sensor Calibrations

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7919
Author(s):  
Sjoerd van Ratingen ◽  
Jan Vonk ◽  
Christa Blokhuis ◽  
Joost Wesseling ◽  
Erik Tielemans ◽  
...  
Keyword(s):  
Low Cost ◽  
Added Value ◽  
Sensor Data ◽  
Sensor Technology ◽  
Monitoring Networks ◽  
Technical Problems ◽  
Eu Directive ◽  
Short Period ◽  
No2 Sensor ◽  
One Year

Low-cost sensor technology has been available for several years and has the potential to complement official monitoring networks. The current generation of nitrogen dioxide (NO2) sensors suffers from various technical problems. This study explores the added value of calibration models based on (multiple) linear regression including cross terms on the performance of an electrochemical NO2 sensor, the B43F manufactured by Alphasense. Sensor data were collected in duplicate at four reference sites in the Netherlands over a period of one year. It is shown that a calibration, using O3 and temperature in addition to a reference NO2 measurement, improves the prediction in terms of R2 from less than 0.5 to 0.69–0.84. The uncertainty of the calibrated sensors meets the Data Quality Objective for indicative methods specified by the EU directive in some cases and it was verified that the sensor signal itself remains an important predictor in the multilinear regressions. In practice, these sensors are likely to be calibrated over a period (much) shorter than one year. This study shows the dependence of the quality of the calibrated signal on the choice of these short (monthly) calibration and validation periods. This information will be valuable for determining short-period calibration strategies.

scholarly journals A market-oriented database design for critical material research

2021 ◽  
Vol 1 (1) ◽  
pp. 34-49
Author(s):  
Ruby T. Nguyen ◽  
◽  
Ange-Lionel Toba ◽  
Michael H. Severson ◽  
Ethan M. Woodbury ◽  
...  
Keyword(s):  
Supply Chain ◽  
Raw Materials ◽  
Raw Material ◽  
Easy Access ◽  
Material Research ◽  
Supply Chain Risk ◽  
Quality Of Data ◽  
Critical Material ◽  
Market Focus ◽  
End Use

<abstract> <p>Material databases are important tools to provide and store information from material research. Rising concerns about supply-chain risks to raw materials presents a need to incorporate raw-material market and end-use application data, beyond basic chemical and physical properties, into a material database. One key challenge for researchers working on critical materials is information scarcity and inconsistency. This paper introduces, as a result of a two-year project, a critical-material commodity database (CMCD) incorporated with a low-code web-based platform that allows easy access for users and simple updates for the authors. The main goal of this project was to educate material scientists on the applications having the most impact on the supply chain and current industrial specifications/markets for each application. The objective was to provide material researchers with harmonized information so that they could gain a better understanding of the market, focus their technologies on an application with a high potential for commercialization, and better contribute to supply-chain risk reduction. While the goal was met with high receptivity, several limitations stemmed from query design, distribution platform, and quality of data source. To overcome some of these limitations and expand on CMCD's potential, we are building a public webpage with an improved interface, better data organization, and higher extensibility.</p> </abstract>

scholarly journals NanoSen-AQM: From Sensors to Users

2020 ◽  
Vol 16 (04) ◽  
pp. 51
Author(s):  
Pedro Lucas ◽  
Jorge Silva ◽  
Filipe Araujo ◽  
Catarina Silva ◽  
Paulo Gil ◽  
...  
Keyword(s):  
Air Quality ◽  
Low Cost ◽  
Sensor Data ◽  
Quality Data ◽  
Easy Access ◽  
The Public ◽  
Nano Sensors ◽  
The Cost ◽  
Air Quality Data ◽  
Monitoring Platform

With the raising of environmental concerns regarding pollution, interest in monitoring air quality is increasing. However, air pollution data is mostly originated from a limited number of government-owned sensors, which can only capture a small fraction of reality. Improving air quality coverage in-volves reducing the cost of sensors and making data widely available to the public. To this end, the NanoSen-AQM project proposes the usage of low-cost nano-sensors as the basis for an air quality monitoring platform, capa-ble of collecting, aggregating, processing, storing, and displaying air quality data. Being an end-to-end system, the platform allows sensor owners to manage their sensors, as well as define calibration functions, that can im-prove data reliability. The public can visualize sensor data in a map, define specific clusters (groups of sensors) as favorites and set alerts in the event of bad air quality in certain sensors. The NanoSen-AQM platform provides easy access to air quality data, with the aim of improving public health.

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