Ethics and Privacy Considerations Before Deploying Sensor Technologies for Exposure Assessment in the Workplace: Results of a Structured Discussion Amongst Dutch Stakeholders

2020 ◽  
Author(s):  
Maaike le Feber ◽  
Trishala Jadoenathmisier ◽  
Henk Goede ◽  
Eelco Kuijpers ◽  
Anjoeka Pronk
Keyword(s):  
Exposure Assessment ◽  
Low Cost ◽  
Contextual Information ◽  
Embedded Sensors ◽  
Dangerous Substances ◽  
Negative Side Effects ◽  
Continuous Sensor ◽  
Privacy Issues ◽  
Safety And Environment ◽  
Exposure Measurements

Abstract Will sensor-based exposure assessment be the future in workplace settings? Static instruments with embedded sensors are already applied to monitor levels of dangerous substances—in the context of acute health effects—at critical locations. However, with wearable, lightweight, miniaturized (low-cost) sensors developing quickly, much more is possible with sensors in relation to exposure assessment. Sensors can be applied in the work environment, on machines, or on employees and may include sensors that measure chemical exposures, but also sensors or other technologies that collect contextual information to support the exposure measurements. Like every technology it also has downsides. Sensors collect data on individuals that, depending on the purpose, need to be shared with others (e.g. health, safety and environment manager). One can imagine that people are afraid of misuse. To explore possible ethical and privacy issues that may come along with the introduction of sensors in the workplace, we organized a workshop with stakeholders (n = 32) to discuss three possible sensor-based scenarios in a structured way around five themes: purpose, efficacy, intrusiveness, proportionality, and fairness. The main conclusion of the discussions was that stakeholders currently see benefits in using sensors for applied targeted studies (short periods, clear reasons). In order to find acceptance for the implementation of sensors, all individuals affected by the sensors or its data need to be involved in the decisions on the purpose and application of sensors. Possible negative side effects need to be discussed and addressed. Continuous sensor-based monitoring of workers currently appears to be a bridge too far for the participants of this workshop.

Janus – A New Approach to Air Combat Pilot Training

2019 ◽  
Vol 2019 (4) ◽  
pp. 7-22
Author(s):  
Georges Bridel ◽  
Zdobyslaw Goraj ◽  
Lukasz Kiszkowiak ◽  
Jean-Georges Brévot ◽  
Jean-Pierre Devaux ◽  
...  
Keyword(s):  
Low Cost ◽  
Cost Effective ◽  
High Energy ◽  
Training System ◽  
Embedded Sensors ◽  
Pilot Training ◽  
New Approach ◽  
Combat Aircraft ◽  
Air Combat ◽  
The Cost

Abstract Advanced jet training still relies on old concepts and solutions that are no longer efficient when considering the current and forthcoming changes in air combat. The cost of those old solutions to develop and maintain combat pilot skills are important, adding even more constraints to the training limitations. The requirement of having a trainer aircraft able to perform also light combat aircraft operational mission is adding unnecessary complexity and cost without any real operational advantages to air combat mission training. Thanks to emerging technologies, the JANUS project will study the feasibility of a brand-new concept of agile manoeuvrable training aircraft and an integrated training system, able to provide a live, virtual and constructive environment. The JANUS concept is based on a lightweight, low-cost, high energy aircraft associated to a ground based Integrated Training System providing simulated and emulated signals, simulated and real opponents, combined with real-time feedback on pilot’s physiological characteristics: traditionally embedded sensors are replaced with emulated signals, simulated opponents are proposed to the pilot, enabling out of sight engagement. JANUS is also providing new cost effective and more realistic solutions for “Red air aircraft” missions, organised in so-called “Aggressor Squadrons”.

scholarly journals Deployment, Calibration, and Cross-Validation of Low-Cost Electrochemical Sensors for Carbon Monoxide, Nitrogen Oxides, and Ozone for an Epidemiological Study

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4214
Author(s):  
Christopher Zuidema ◽  
Cooper S. Schumacher ◽  
Elena Austin ◽  
Graeme Carvlin ◽  
Timothy V. Larson ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Exposure Assessment ◽  
Epidemiological Study ◽  
Electrochemical Sensors ◽  
Low Cost ◽  
Puget Sound ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Calibration Models ◽  
Study Participants

We designed and built a network of monitors for ambient air pollution equipped with low-cost gas sensors to be used to supplement regulatory agency monitoring for exposure assessment within a large epidemiological study. This paper describes the development of a series of hourly and daily field calibration models for Alphasense sensors for carbon monoxide (CO; CO-B4), nitric oxide (NO; NO-B4), nitrogen dioxide (NO2; NO2-B43F), and oxidizing gases (OX-B431)—which refers to ozone (O3) and NO2. The monitor network was deployed in the Puget Sound region of Washington, USA, from May 2017 to March 2019. Monitors were rotated throughout the region, including at two Puget Sound Clean Air Agency monitoring sites for calibration purposes, and over 100 residences, including the homes of epidemiological study participants, with the goal of improving long-term pollutant exposure predictions at participant locations. Calibration models improved when accounting for individual sensor performance, ambient temperature and humidity, and concentrations of co-pollutants as measured by other low-cost sensors in the monitors. Predictions from the final daily models for CO and NO performed the best considering agreement with regulatory monitors in cross-validated root-mean-square error (RMSE) and R2 measures (CO: RMSE = 18 ppb, R2 = 0.97; NO: RMSE = 2 ppb, R2 = 0.97). Performance measures for NO2 and O3 were somewhat lower (NO2: RMSE = 3 ppb, R2 = 0.79; O3: RMSE = 4 ppb, R2 = 0.81). These high levels of calibration performance add confidence that low-cost sensor measurements collected at the homes of epidemiological study participants can be integrated into spatiotemporal models of pollutant concentrations, improving exposure assessment for epidemiological inference.

scholarly journals Reducing the Influence of Environmental Factors on Performance of a Diffusion-Based Personal Exposure Kit

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4637
Author(s):  
Huixin Zong ◽  
Peter Brimblecombe ◽  
Li Sun ◽  
Peng Wei ◽  
Kin-Fai Ho ◽  
...  
Keyword(s):  
Exposure Assessment ◽  
Real Time ◽  
Low Cost ◽  
Personal Exposure ◽  
Sensor Technology ◽  
Heterogeneous Environments ◽  
Mathematical Algorithms ◽  
Personal Exposure Assessment ◽  
Address System ◽  
The Impact

Sensor technology has enabled the development of portable low-cost monitoring kits that might supplement many applications in conventional monitoring stations. Despite the sensitivity of electrochemical gas sensors to environmental change, they are increasingly important in monitoring polluted microenvironments. The performance of a compact diffusion-based Personal Exposure Kit (PEK) was assessed for real-time gaseous pollutant measurement (CO, O3, and NO2) under typical environmental conditions encountered in the subtropical city of Hong Kong. A dynamic baseline tracking method and a range of calibration protocols to address system performance were explored under practical scenarios to assess the performance of the PEK in reducing the impact of rapid changes in the ambient environment in personal exposure assessment applications. The results show that the accuracy and stability of the ppb level gas measurement is enhanced even in heterogeneous environments, thus avoiding the need for data post-processing with mathematical algorithms, such as multi-linear regression. This establishes the potential for use in personal exposure monitoring, which has been difficult in the past, and for reporting more accurate and reliable data in real-time to support personal exposure assessment and portable air quality monitoring applications.

scholarly journals Evaluation of Exposure Assessment Tools under REACH: Part II—Higher Tier Tools

2018 ◽  
Vol 63 (2) ◽  
pp. 230-241 ◽  
Author(s):  
Eun Gyung Lee ◽  
Judith Lamb ◽  
Nenad Savic ◽  
Ioannis Basinas ◽  
Bojan Gasic ◽  
...  
Keyword(s):  
Exposure Assessment ◽  
Assessment Tools ◽  
Worst Case ◽  
Local Exhaust Ventilation ◽  
Limit Value ◽  
Exposure Category ◽  
Input Parameters ◽  
Full Shift ◽  
Exposure Levels ◽  
Exposure Measurements

Abstract Stoffenmanager®v4.5 and Advanced REACH Tool (ART) v1.5, two higher tier exposure assessment tools for use under REACH, were evaluated by determining accuracy and robustness. A total of 282 exposure measurements from 51 exposure situations (ESs) were collected and categorized by exposure category. In this study, only the results of liquids with vapor pressure (VP) > 10 Pa category having a sufficient number of exposure measurements (n = 251 with 42 ESs) were utilized. In addition, the results were presented by handling/activity description and input parameters for the same exposure category. It should be noted that the performance results of Stoffenmanager and ART in this study cannot be directly compared for some ESs because ART allows a combination of up to four subtasks (and nonexposed periods) to be included, whereas the database for Stoffenmanager, separately developed under the permission of the legal owner of Stoffenmanager, permits the use of only one task to predict exposure estimates. Thus, it would be most appropriate to compare full-shift measurements against ART predictions (full shift including nonexposed periods) and task-based measurements against task-based Stoffenmanager predictions. For liquids with VP > 10 Pa category, Stoffenmanager®v4.5 appeared to be reasonably accurate and robust when predicting exposures [percentage of measurements exceeding the tool’s 90th percentile estimate (%M > T) was 15%]. Areas that could potentially be improved include ESs involving the task of handling of liquids on large surfaces or large work pieces, allocation of high and medium VP inputs, and absence of local exhaust ventilation input. Although the ART’s median predictions appeared to be reasonably accurate for liquids with VP > 10 Pa, the %M > T for the 90th percentile estimates was 41%, indicating that variance in exposure levels is underestimated by ART. The %M > T using the estimates of the upper value of 90% confidence interval (CI) of the 90th percentile estimate (UCI90) was considerably reduced to 18% for liquids with VP > 10 Pa. On the basis of this observation, users might be to consider using the upper limit value of 90% CI of the 90th percentile estimate for predicting reasonable worst case situations. Nevertheless, for some activities and input parameters, ART still shows areas to be improved. Hence, it is suggested that ART developers review the assumptions in relation to exposure variability within the tool, toward improving the tool performance in estimating percentile exposure levels. In addition, for both tools, only some handling/activity descriptions and input parameters were considered. Thus, further validation studies are still necessary.

Thematic Analysis Using A Machine Learning Approach on User Reviews for Depression and Anxiety Chatbot Applications (Preprint)

2021 ◽  
Author(s):  
Arfan Ahmed ◽  
Sarah Aziz ◽  
Uzair Shah ◽  
Asmaa Hassan ◽  
Alaa Abd-Alrazaq ◽  
...  
Keyword(s):  
Thematic Analysis ◽  
User Interfaces ◽  
Latent Dirichlet Allocation ◽  
Low Cost ◽  
Methodological Approach ◽  
Anxiety And Depression ◽  
Depression Symptoms ◽  
Machine Learning Approach ◽  
Google Play ◽  
Privacy Issues

BACKGROUND Anxiety and depression are amongst the most commonly prevalent mental health disorders (CMDs) worldwide. Chatbot apps can play an important role in relieving anxiety and depression. Users’ reviews of chatbot apps are considered an important source of data to explore users’ opinion and satisfaction of chatbot apps. OBJECTIVE This study aims to explore users’ opinions, satisfaction, and attitudes about anxiety and depression chatbot apps through conducting a thematic analysis of users’ reviews of 11 anxiety and depression chatbot apps collected from Google play and Apple store. In addition, we propose a workflow to provide a methodological approach for future analysis of review comments. METHODS We analyzed 205,881 user review comments from chatbots dedicated for users with anxiety and depression symptoms. Using scrapper tools (Google Play Scraper and App Store Scraper python libraries), we extracted text and metadata. The reviews were divided into positive and negative meta themes, based on users rating per review. We analysed the reviews using word frequencies of bigrams (words in pair).A topic modelling technique, Latent Dirichlet Allocation (LDA) was applied to identify topics in the reviews, and analysed for detecting themes and subthemes. RESULTS A thematic analysis was conducted on 5 topics for each sentimental set. Reviews were categorized as either positive or negative. For positive reviews, the main themes were confidence and affirmation building, adequate analysis, and consultation, caring as a friend, and easy to use. Whereas for negative reviews results revealed the following themes: usability issues, update Issues, Privacy and Non-creative conversation. CONCLUSIONS Chatbots appear to have the ability to provide users suffering from anxiety and depression feel confident and give them support via a tool that is easy to use, low cost, containing adequate symptom detection whilst providing feeling of having a close friend to converse with. Users tend to dislike technical and privacy issues. Users expect engaging and creative conversations via appealing user interfaces.

Security, Privacy, and Trust for Pervasive Computing Applications

2016 ◽  
pp. 327-342
Author(s):  
Sheikh I. Ahamed ◽  
Mohammad Zulkernine ◽  
Munirul M. Haque
Keyword(s):  
Communication Networks ◽  
Pervasive Computing ◽  
Ad Hoc ◽  
Low Cost ◽  
Wireless Communication Networks ◽  
Security Measures ◽  
Embedded Devices ◽  
Trust Models ◽  
Networks Structure ◽  
Privacy Issues

Pervasive computing has progressed significantly during this decade due to the developments and advances in portable, low-cost, and light-weight devices along with the emergence of short range and low-power wireless communication networks. Pervasive computing focuses on combining computing and communications with the surrounding physical environment to make computing and communication transparent to the users in day-to-day activities. In pervasive computing, numerous, casually accessible, often invisible, frequently mobile or embedded devices form an ad-hoc network that occasionally connects to fixed networks structure too. These pervasive computing devices often collect information about the surrounding environment using various sensors. Pervasive computing has the inherent disadvantages of slow, expensive connections, frequent line disconnections, limited host bandwidth, location dependent data, and so forth. These challenges make pervasive computing applications more vulnerable to various security-related threats. However, traditional security measures do not fit well in pervasive computing applications. Since location and context are key attributes of pervasive computing applications, privacy issues need to be handled in a sophisticated manner. The devices in a pervasive computing network leave and join in an ad-hoc manner. This device behavior creates a need for new trust models for pervasive computing applications. In this chapter, we address the challenges and requirements of security, privacy, and trust for pervasive applications. We also discuss the state-of-the-art of pervasive security, privacy, and trust along with some open issues.

scholarly journals Design and Implementation of a Connection between Augmented Reality and Sensors

Robotics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 3
Author(s):  
Marlon Aguero ◽  
Dilendra Maharjan ◽  
Maria del Pilar Rodriguez ◽  
David Dennis Lee Mascarenas ◽  
Fernando Moreu
Keyword(s):  
Augmented Reality ◽  
Data Collection ◽  
Real Time ◽  
Strain Gauge ◽  
Low Cost ◽  
Contextual Information ◽  
Wireless Sensor ◽  
Infrastructure Management ◽  
Data Collection And Analysis ◽  
The Way

Wireless sensor networks (WSN) are used by engineers to record the behavior of structures. The sensors provide data to be used by engineers to make informed choices and prioritize decisions concerning maintenance procedures, required repairs, and potential infrastructure replacements. However, reliable data collection in the field remains a challenge. The information obtained by the sensors in the field frequently needs further processing, either at the decision-making headquarters or in the office. Although WSN allows data collection and analysis, there is often a gap between WSN data analysis results and the way decisions are made in industry. The industry depends on inspectors’ decisions, so it is of vital necessity to improve the inspectors’ access in the field to data collected from sensors. This paper presents the results of an experiment that shows the way Augmented Reality (AR) may improve the availability of WSN data to inspectors. AR is a tool which overlays the known attributes of an object with the corresponding position on the headset screen. In this way, it allows the integration of reality with a virtual representation provided by a computer in real time. These additional synthetic overlays supply data that may be unavailable otherwise, but it may also display additional contextual information. The experiment reported in this paper involves the application of a smart Strain Gauge Platform, which automatically measures strain for different applications, using a wireless sensor. In this experiment, an AR headset was used to improve actionable data visualization. The results of the reported experiment indicate that since the AR headset makes it possible to visualize information collected from the sensors in a graphic form in real time, it enables automatic, effective, reliable, and instant communication from a smart low-cost sensor strain gauge to a database. Moreover, it allows inspectors to observe augmented data and compare it across time and space, which then leads to appropriate prioritization of infrastructure management decisions based on accurate observations.

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