scholarly journals Autonomous Detection System for Non-Hard-Hat Use at Construction Sites Using Sensor Technology

2021 ◽  
Vol 13 (3) ◽  
pp. 1102
Author(s):  
Jung Hoon Kim ◽  
Byung Wan Jo ◽  
Jun Ho Jo ◽  
Yun Sung Lee ◽  
Do Keun Kim
Keyword(s):  
Heart Rate ◽  
Detection System ◽  
Low Cost ◽  
Sensor Data ◽  
Sensor Technology ◽  
Construction Sites ◽  
Novel Method ◽  
Validation Tests ◽  
Microcontroller Unit ◽  
Hard Hat

In this study, we present a novel method of detecting hard hat use on construction sites using a modified version of an off-the-shelf wearable device. The data-transmitting node of the device contained two sensors, a photoplethysmogram (PPG) and accelerometers (Acc), along with two modules, a global positioning system (GPS) and a low-power wide-area (LoRa) network module. All the components were embedded into a microcontroller unit (MCU) in addition to the power supply. The receiving node included a server that displayed the results via both the Internet of Things (IoT) and smartphones. The LoRa network connected two nodes so that it could function in larger areas such as construction sites at a relatively low cost. The proposed method analyzes the data from a PPG sensor located on the hard hat chin strap and automatically notifies a manager when a worker is not wearing the required hard hat at the site. In addition, by utilizing the PPG sensor data, a heart rate abnormality-detecting feature was added based on an age-adjusted maximum heart rate formula. In validation tests, various PPG sensor locations and shapes were studied, and the results demonstrated the smallest error in the circular shaped sensor located at the upper neck (0.56%). Finally, an IoT monitoring page was created to monitor heart rate abnormalities while identifying hard hat use violations via both PCs and smart phones.

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 Comparing Airborne Particulate Matter Intake Dose Assessment Models Using Low-Cost Portable Sensor Data

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1406
Author(s):  
Rok Novak ◽  
David Kocman ◽  
Johanna Amalia Robinson ◽  
Tjaša Kanduč ◽  
Dimosthenis Sarigiannis ◽  
...  
Keyword(s):  
Heart Rate ◽  
Particulate Matter ◽  
Low Cost ◽  
Minute Ventilation ◽  
Dose Assessment ◽  
Sensor Data ◽  
Activity Data ◽  
Time Activity ◽  
Activity Monitors ◽  
Coarse Information

Low-cost sensors can be used to improve the temporal and spatial resolution of an individual’s particulate matter (PM) intake dose assessment. In this work, personal activity monitors were used to measure heart rate (proxy for minute ventilation), and low-cost PM sensors were used to measure concentrations of PM. Intake dose was assessed as a product of PM concentration and minute ventilation, using four models with increasing complexity. The two models that use heart rate as a variable had the most consistent results and showed a good response to variations in PM concentrations and heart rate. On the other hand, the two models using generalized population data of minute ventilation expectably yielded more coarse information on the intake dose. Aggregated weekly intake doses did not vary significantly between the models (6–22%). Propagation of uncertainty was assessed for each model, however, differences in their underlying assumptions made them incomparable. The most complex minute ventilation model, with heart rate as a variable, has shown slightly lower uncertainty than the model using fewer variables. Similarly, among the non-heart rate models, the one using real-time activity data has less uncertainty. Minute ventilation models contribute the most to the overall intake dose model uncertainty, followed closely by the low-cost personal activity monitors. The lack of a common methodology to assess the intake dose and quantifying related uncertainties is evident and should be a subject of further research.

scholarly journals Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4448 ◽  
Author(s):  
Günther Sagl ◽  
Bernd Resch ◽  
Andreas Petutschnig ◽  
Kalliopi Kyriakou ◽  
Michael Liedlgruber ◽  
...  
Keyword(s):  
Heart Rate ◽  
Galvanic Skin Response ◽  
Low Cost ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Quantified Self ◽  
High Quality ◽  
Laboratory Equipment ◽  
Skin Response ◽  
Physiological Sensors

Wearable sensors are increasingly used in research, as well as for personal and private purposes. A variety of scientific studies are based on physiological measurements from such rather low-cost wearables. That said, how accurate are such measurements compared to measurements from well-calibrated, high-quality laboratory equipment used in psychological and medical research? The answer to this question, undoubtedly impacts the reliability of a study’s results. In this paper, we demonstrate an approach to quantify the accuracy of low-cost wearables in comparison to high-quality laboratory sensors. We therefore developed a benchmark framework for physiological sensors that covers the entire workflow from sensor data acquisition to the computation and interpretation of diverse correlation and similarity metrics. We evaluated this framework based on a study with 18 participants. Each participant was equipped with one high-quality laboratory sensor and two wearables. These three sensors simultaneously measured the physiological parameters such as heart rate and galvanic skin response, while the participant was cycling on an ergometer following a predefined routine. The results of our benchmarking show that cardiovascular parameters (heart rate, inter-beat interval, heart rate variability) yield very high correlations and similarities. Measurement of galvanic skin response, which is a more delicate undertaking, resulted in lower, but still reasonable correlations and similarities. We conclude that the benchmarked wearables provide physiological measurements such as heart rate and inter-beat interval with an accuracy close to that of the professional high-end sensor, but the accuracy varies more for other parameters, such as galvanic skin response.

scholarly journals DEVELOPMENT OF AUTOMATIC MODE DETECTION SYSTEM BY IMPLEMENTING THE STATISTICAL ANALYSIS OF SHIP DATA TO MONITOR THE PERFORMANCE

2021 ◽  
Vol 159 (A3) ◽  
Author(s):  
I Zaman ◽  
K Pazouki ◽  
R Norman ◽  
S Younessi ◽  
S Coleman
Keyword(s):  
Statistical Analysis ◽  
Technology Implementation ◽  
Human Error ◽  
Performance Monitoring ◽  
Detection System ◽  
Sensor Data ◽  
Sensor Technology ◽  
Shipping Industry ◽  
Autonomous Operation ◽  
Marine Industry

The shipping industry depends on a global regulatory framework to operate efficiently. The industry is currently facing various technical and regulatory challenges. Performance monitoring, vessel optimisation, reduction of emissions and maintenance have become high priorities for ship operators. The marine industry is also moving towards autonomous operation to reduce human error. The rate of sensor technology implementation has increased and also raised new technological challenges. The analysis of sensor data creates new challenges to achieve operational excellence. This paper presents the implementation of statistical analysis on ship data and develops a system to automatically detect the vessel operational modes based on sensor data.

scholarly journals Static, Portable and Smart Detection of Water Quality in Panchayath Distribution Tank

2019 ◽  
Vol 8 (2S5) ◽  
pp. 103-108
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Cost Effective ◽  
Daily Basis ◽  
Quality Parameters ◽  
Sensor Data ◽  
Sensor Technology ◽  
Real Time System ◽  
Quality Of Water ◽  
Physical And Chemical

The significant crunch in the Current world is Water pollution. It has created an abundant influence on the Environment. With the intention of the non-toxic distribution of the water and its eminence should be monitored at real time. This paper suggested the smart detection with low cost real time system which is used to monitor the quality of water through IOT(internet of things). The system entail of different sensors which are used to measure the physical and chemical parameters of the water. The quality parameters are temperature, pH, turbidity, conductivity and Total dissolved solids of the water are measured. Commercially available products capable of monitoring such parameters are usually somewhat expensive and the data’s are collected by mobile van. Using Sensor technology provides a cost-effective and pre-eminent reliable as they can provide real time output. The measured values from the sensors can be observed by the core controller. The controller was programmed to monitor the distribution tank on a daily basis to hour basis monitoring. The TIVA C series is used as a core controller. The Controller is mounted on the side of the distribution tank. Finally, the sensor data from the controller is sent to Wi-Fi module through UART protocol. Wi-fi Module is connected to a public Wi-Fi system through which data is seen by the locals who are all connected to that Wi-Fi network.

scholarly journals Development of Automatic Mode Detection System by Implementing the Statistical Analysis of Ship Data to Monitor the Performance

2017 ◽  
Vol Vol 159 (A3) ◽  
Author(s):  
I Zaman ◽  
K Pazouki ◽  
R Norman ◽  
S Younessi ◽  
S Coleman
Keyword(s):  
Statistical Analysis ◽  
Technology Implementation ◽  
Human Error ◽  
Performance Monitoring ◽  
Detection System ◽  
Sensor Data ◽  
Sensor Technology ◽  
Shipping Industry ◽  
Autonomous Operation ◽  
Marine Industry

The shipping industry depends on a global regulatory framework to operate efficiently. The industry is currently facing various technical and regulatory challenges. Performance monitoring, vessel optimisation, reduction of emissions and maintenance have become high priorities for ship operators. The marine industry is also moving towards autonomous operation to reduce human error. The rate of sensor technology implementation has increased and also raised new technological challenges. The analysis of sensor data creates new challenges to achieve operational excellence. This paper presents the implementation of statistical analysis on ship data and develops a system to automatically detect the vessel operational modes based on sensor data.

Determination of pyruvic acid concentration using a bioluminescence system from Photobacterium leiognathi

2015 ◽  
Vol 14 (6) ◽  
pp. 1163-1168 ◽  
Author(s):  
Guanhua Xuan ◽  
Xiaodong Lu ◽  
Jingxue Wang ◽  
Hong Lin ◽  
Huihui Liu
Keyword(s):  
Acid Concentration ◽  
Pyruvic Acid ◽  
Detection System ◽  
Low Cost ◽  
Bacterial Bioluminescence ◽  
Photobacterium Leiognathi ◽  
Novel Method

A novel method combining LDH catalysis with a bacterial bioluminescence system was developed for pyruvic acid detection. The detection system was expected to be an attractive substitute technology because of its rapidity, low cost and operational ease.

MULTISENSORY DETECTION SYSTEM FOR DAMAGE CONTROL AND SITUATIONAL AWARENESS

2008 ◽  
Vol 18 (03) ◽  
pp. 575-592
Author(s):  
CHRISTIAN P. MINOR ◽  
DANIEL A. STEINHURST ◽  
KEVIN J. JOHNSON ◽  
SUSAN L. ROSE-PEHRSSON ◽  
JEFFREY C. OWRUTSKY ◽  
...  
Keyword(s):  
Data Fusion ◽  
Situational Awareness ◽  
Detection System ◽  
Damage Control ◽  
Low Cost ◽  
Sensor Data ◽  
Sensor Systems ◽  
Volume Sensor ◽  
Communications Protocol ◽  
And Control

A data fusion-based, multisensory detection system, called “Volume Sensor”, was developed under the Advanced Damage Countermeasures (ADC) portion of the US Navy's Future Naval Capabilities program (FNC) to meet reduced manning goals. A diverse group of sensing modalities was chosen to provide an automated damage control monitoring capability that could be constructed at a relatively low cost and also easily integrated into existing ship infrastructure. Volume Sensor employs an efficient, scalable, and adaptable design framework that can serve as a template for heterogeneous sensor network integration for situational awareness. In the development of Volume Sensor, a number of challenges were addressed and met with solutions that are applicable to heterogeneous sensor networks of any type. These solutions include: 1) a uniform, but general format for encapsulating sensor data, 2) a communications protocol for the transfer of sensor data and command and control of networked sensor systems, 3) the development of event specific data fusion algorithms, and 4) the design and implementation of modular and scalable system architecture. In full-scale testing on a shipboard environment, two prototype Volume Sensor systems demonstrated the capability to provide highly accurate and timely situational awareness regarding damage control events while simultaneously imparting a negligible footprint on the ship's 100 Mbps Ethernet network and maintaining smooth and reliable operation in a real-time fashion.

scholarly journals Detection of Sleep Biosignals Using an Intelligent Mattress Based on Piezoelectric Ceramic Sensors

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3843 ◽  
Author(s):  
Min Peng ◽  
Zhizhong Ding ◽  
Lusheng Wang ◽  
Xusheng Cheng
Keyword(s):  
Heart Rate ◽  
Wavelet Analysis ◽  
Respiratory Rate ◽  
Piezoelectric Ceramic ◽  
Detection System ◽  
Low Cost ◽  
Ensemble Empirical Mode Decomposition ◽  
High Accuracy ◽  
Health Condition ◽  
Sleep State

Physiological information such as respiratory rate and heart rate in the sleep state can be used to evaluate the health condition of the sleeper. Traditional sleep monitoring systems need body contact and are intrusive, which limits their applicability. Thus, a comfortable sleep biosignals detection system with both high accuracy and low cost is important for health care. In this paper, we design a sleep biosignals detection system based on low-cost piezoelectric ceramic sensors. 18 piezoelectric ceramic sensors are deployed under the mattress to capture the pressure data. The appropriate sensor that captures respiration and heartbeat sensitively is selected by the proposed channel-selection algorithm. Then, we propose a dynamic smoothing algorithm to extract respiratory rate and heart rate using the selected data. The dynamic smoothing can separate heartbeat signals from respiratory signals with low complexity by dynamically choosing the smooth window, and it is suitable for real-time implementation in low-cost embedded systems. For comparison, wavelet analysis and ensemble empirical mode decomposition (EEMD) are performed in a personal computer (PC). Experimental results show that data collected by piezoelectric ceramic sensors can be used for respiratory-rate and heart-rate detection with high accuracy. In addition, the dynamic smoothing can achieve high accuracy close to wavelet analysis and EEMD, while it has much lower complexity.

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