Search results

1 – 10 of over 50000
To view the access options for this content please click here
Article
Publication date: 12 August 2013

Anders Nordgren

This paper has three purposes: to identify and discuss values that should be promoted and respected in personal health monitoring, to formulate an ethical checklist that…

Abstract

Purpose

This paper has three purposes: to identify and discuss values that should be promoted and respected in personal health monitoring, to formulate an ethical checklist that can be used by stakeholders, and to construct an ethical matrix that can be used for identifying values, among those in the ethical checklist, that are particularly important to various stakeholders.

Design/methodology/approach

On the basis of values that empirical studies have found important to various stakeholders in personal health monitoring, the author constructs an ethical checklist and an ethical matrix. The author carries out a brief conceptual analysis and discusses the implications.

Findings

The ethical checklist consists of three types of values: practical values that a technical product in personal health monitoring must have, quality of life values to be promoted by the development and use of the product, and moral values to be respected in this development and use. To give guidance in practice, the values in the checklist must be interpreted and balanced. The ethical matrix consists of the values in the checklist and a number of stakeholders.

Originality/value

The overall ambition is to suggest a way of categorizing values that can be useful for stakeholders in personal health monitoring. In order to achieve this, the study takes empirical studies as a starting-point and includes a conceptual analysis. This means that the proposals are founded on practice rather than mere abstract thinking, and this improves its usability.

Details

Journal of Information, Communication and Ethics in Society, vol. 11 no. 3
Type: Research Article
ISSN: 1477-996X

Keywords

To view the access options for this content please click here
Article
Publication date: 5 November 2019

Mohammad Javad Ershadi, Reza Edrisabadi and Aghileh Shakouri

Project management generally covers many important areas such as cost, quality and time in different industrial settings, but it is deficient in relation to integration of…

Abstract

Purpose

Project management generally covers many important areas such as cost, quality and time in different industrial settings, but it is deficient in relation to integration of health, safety and environmental risks. Poor knowledge of project managers about HSE management necessitates the studying on the mutual effects of HSE and project management. Hence, investigating the impact of project management on health monitoring programs, safety prevention monitoring, environmental monitoring plans and finally the effectiveness of professional health monitoring programs and determining their importance are main objectives of this research. The paper aims to discuss these issues.

Design/methodology/approach

A model based on structural equations was designed and developed. The constructs of this model are project management, health monitoring and safety prevention monitoring program. Based on the conceptual model, some questionnaires were prepared and distributed among the experts of strategic project management.

Findings

The results of applied structural modeling suggest that project management focuses on each aspect of HSE management, including health monitoring programs, safety prevention monitoring programs, environmental monitoring plans and effectiveness of professional health monitoring programs. HSE management can also be strengthened by empowering project management. Checking fire protection systems, using appropriate techniques to identify contamination and disposal of waste and incorporating techniques for brainstorming or other ideas creation in the group are the most important tasks in HSE-enabled project management frameworks.

Originality/value

Since there is still no strategic alignment model that includes components of project management and HSE management, a model for achieving this goal is vital. This paper elaborates this alignment based on literature and using a field study.

Details

Built Environment Project and Asset Management, vol. 10 no. 1
Type: Research Article
ISSN: 2044-124X

Keywords

To view the access options for this content please click here
Article
Publication date: 30 January 2009

Edwin Vijay Kumar, S.K. Chaturvedi and A.W. Deshpandé

The purpose of this paper is to ascertain overall system health and maintenance needs with degree of certainty using condition‐monitoring data with hierarchical fuzzy…

Downloads
1290

Abstract

Purpose

The purpose of this paper is to ascertain overall system health and maintenance needs with degree of certainty using condition‐monitoring data with hierarchical fuzzy inference system.

Design/methodology/approach

In process plants, equipment condition is ascertained using condition‐monitoring data for each condition indicator. For large systems with multiple condition indicators, estimating the overall system health becomes cumbersome. The decision of selecting the equipment for an overhaul is mostly determined by generic guidelines, and seldom backed up by condition‐monitoring data. The proposed approach uses a hierarchical system health assessment using fuzzy inference on condition‐monitoring data collected over a period. Each subsystem health is ascertained with degree of certainty using degree of match operation performed on fuzzy sets of condition‐monitoring data and expert opinion. Fuzzy sets and approximate reasoning are used to handle the uncertainty/imprecision in data and subjectivity/vagueness of expert domain knowledge.

Findings

The proposed approach has been applied to a large electric motor (> 500kW), which is treated as four subsystems i.e. power transmission system, electromagnetic system, ventilation system and support system. Fuzzy set of condition‐monitoring data of each condition indicator on each subsystem is used to ascertain the degree of match with the expert opinion fuzzy set, thus inferring the need for periodical overhaul. Subjective expert opinion and quantitative condition‐monitoring data have been evaluated using hierarchical fuzzy inference system with a rule base. It is found that the certainty of each subsystem's health is not the same at the end of 600 days of monitoring and can be classified as “very good”, “good”, “marginal” and “sick”. Degree of certainty has helped in taking a managerial decision to avoid “over‐maintenance” and to ensure reliability. Large volumes of condition‐monitoring data not only helped in assessing motor overhaul health, but also guide the maintenance engineer to suitably review maintenance/monitoring strategy on similar systems to achieve desired reliability goals.

Practical implications

Condition‐monitoring data collected for long periods can be utilized to understand the degree of certainty of degradation pattern in the longer time frame with reference to domain knowledge to improve effectiveness of predictive maintenance towards reliability.

Originality/value

The paper gives an opportunity to evaluate quantitative condition‐monitoring data and subjective/qualitative domain expertise using fuzzy sets. The predictive maintenance cycle “Monitor‐analyse‐plan‐repair‐restore‐operate” is scientifically regulated with a degree of certainty. Approach is generic and can be applied to a variety of process equipment to ensure reliability through effective predictive maintenance.

Details

International Journal of Quality & Reliability Management, vol. 26 no. 2
Type: Research Article
ISSN: 0265-671X

Keywords

To view the access options for this content please click here
Article
Publication date: 5 March 2018

Xu Kang and Dechang Pi

The purpose of this paper is to detect the occurrence of anomaly and fault in a spacecraft, investigate various tendencies of telemetry parameters and evaluate the…

Abstract

Purpose

The purpose of this paper is to detect the occurrence of anomaly and fault in a spacecraft, investigate various tendencies of telemetry parameters and evaluate the operation state of the spacecraft to monitor the health of the spacecraft.

Design/methodology/approach

This paper proposes a data-driven method (empirical mode decomposition-sample entropy-principal component analysis [EMD-SE-PCA]) for monitoring the health of the spacecraft, where EMD is used to decompose telemetry data and obtain the trend items, SE is utilised to calculate the sample entropies of trend items and extract the characteristic data and squared prediction error and statistic contribution rate are analysed using PCA to monitor the health of the spacecraft.

Findings

Experimental results indicate that the EMD-SE-PCA method could detect characteristic parameters that appear abnormally before the anomaly or fault occurring, could provide an abnormal early warning time before anomaly or fault appearing and summarise the contribution of each parameter more accurately than other fault detection methods.

Practical implications

The proposed EMD-SE-PCA method has high level of accuracy and efficiency. It can be used in monitoring the health of a spacecraft, detecting the anomaly and fault, avoiding them timely and efficiently. Also, the EMD-SE-PCA method could be further applied for monitoring the health of other equipment (e.g. attitude control and orbit control system) in spacecraft and satellites.

Originality/value

The paper provides a data-driven method EMD-SE-PCA to be applied in the field of practical health monitoring, which could discover the occurrence of anomaly or fault timely and efficiently and is very useful for spacecraft health diagnosis.

Details

Aircraft Engineering and Aerospace Technology, vol. 90 no. 2
Type: Research Article
ISSN: 1748-8842

Keywords

To view the access options for this content please click here
Article
Publication date: 25 January 2013

Jianghong Yu, Daping Wang and Chengwu Hu

The purpose of the paper is to propose a novel approach, based on grey clustering decision, to fill in an omission of quantitative monitoring parameter selection methods.

Downloads
225

Abstract

Purpose

The purpose of the paper is to propose a novel approach, based on grey clustering decision, to fill in an omission of quantitative monitoring parameter selection methods.

Design/methodology/approach

The basic monitoring parameter selection criteria and the corresponding calculation methods are presented. Then, the grey clustering decision model for monitoring parameter optimization selection is constructed, and an integrated weight determination method based on analytic hierarchy process (AHP) and information entropy is provided.

Findings

Basic principle for monitoring parameter selection is proposed and quantitative description is carried out for selection principle in engineering application. Grey clustering decision‐making model for monitoring parameter optimization selection is established. Comprehensive weight ascertainment method based on AHP and information entropy is provided to make the index weight more scientific.

Practical implications

At system design stage, it is of significance to carry out selection and optimization of monitoring parameters. After the optimization of monitoring parameters is confirmed, measurability analysis and design in parallel are carried out for convenience of timely information feedback and system design revision. Therefore, the system integration efficiency is improved and the cost of research and manufacturing is reduced.

Originality/value

Monitoring parameter optimization selection process based on grey clustering decision‐making model is described and the analysis result shows that the proposed method has certain degree of effectiveness, rationality and universality.

To view the access options for this content please click here
Article
Publication date: 16 June 2021

Jeremy Hale and Mingzhou Jin

Inconsistencies in build quality part-to-part and build-to-build continue to be a problem in additive manufacturing (AM). The flexibility of AM often enables low-volume…

Abstract

Purpose

Inconsistencies in build quality part-to-part and build-to-build continue to be a problem in additive manufacturing (AM). The flexibility of AM often enables low-volume and custom production, making conventional methods of machine qualification and health monitoring challenging to implement. Machine health has been difficult to separate from the effects of design and process decisions, and therefore inferring machine health through part quality has been similarly complicated.

Design/methodology/approach

This conceptual paper proposes a framework for monitoring machine health by monitoring two types of witness parts, in the form of witness builds and witness artifacts, to provide sources of data for potential indicators of machine health.

Findings

The proposed conceptual framework with witness builds and witness artifacts permits the implementation into AM techniques to monitor machine health according to part quality. Subsequently, probabilistic models can be used to optimize machine costs and repairs, as opposed to statistical approaches that are less ideal for AM. Bayesian networks, hidden Markov models and Markov decision processes may be well-suited to accomplishing this task.

Originality/value

Though variations of witness builds have been created for use in AM to measure build quality and machine capabilities, the literature contains no previously proposed framework that permits the evaluation of machine health and its influence on quality through a combination of witness builds and witness artifacts, both of which can be easily added into AM production.

Details

Rapid Prototyping Journal, vol. 27 no. 6
Type: Research Article
ISSN: 1355-2546

Keywords

To view the access options for this content please click here
Article
Publication date: 10 April 2019

Yan Hong, Xuechun Cao, Yan Chen, Zhijuan Pan, Yu Chen and Xianyi Zeng

The purpose of this paper is to investigate physiological indices related to comfort and health condition, based on which corresponding electronic equipment are selected…

Abstract

Purpose

The purpose of this paper is to investigate physiological indices related to comfort and health condition, based on which corresponding electronic equipment are selected and applied. A wearable monitoring system using sensor and liquid crystal display (LCD) techniques are then designed. Sensors are used to collect and transmit recording required signals from the wearer. A microcomputer with the type of AT89C52 is used to record and analyze the collected data. LCD is applied to display the health and comfort condition of the wearer.

Design/methodology/approach

A novel wearable monitoring system for the measurement of physiological indices and clothing microclimate is proposed in this study in order to monitoring both health and comfort condition of the wearer.

Findings

The proposed system provides reference for the application of sensor and display technologies in the field of smart clothing, which can be further applied to infant and child care, health care, home entertainment, military and industry.

Originality/value

This paper, first, investigated a framework of a wearable monitoring system considering both comfort and health condition and summarized the related physiological indices. The requirements of both comfort and health condition monitoring are analyzed to select appropriate electronic elements.

Details

International Journal of Clothing Science and Technology, vol. 31 no. 3
Type: Research Article
ISSN: 0955-6222

Keywords

To view the access options for this content please click here
Article
Publication date: 6 July 2018

Y.P. Tsang, K.L. Choy, C.H. Wu, G.T.S. Ho, Cathy H.Y. Lam and P.S. Koo

Since the handling of environmentally sensitive products requires close monitoring under prescribed conditions throughout the supply chain, it is essential to manage…

Downloads
3012

Abstract

Purpose

Since the handling of environmentally sensitive products requires close monitoring under prescribed conditions throughout the supply chain, it is essential to manage specific supply chain risks, i.e. maintaining good environmental conditions, and ensuring occupational safety in the cold environment. The purpose of this paper is to propose an Internet of Things (IoT)-based risk monitoring system (IoTRMS) for controlling product quality and occupational safety risks in cold chains. Real-time product monitoring and risk assessment in personal occupational safety can be then effectively established throughout the entire cold chain.

Design/methodology/approach

In the design of IoTRMS, there are three major components for risk monitoring in cold chains, namely: wireless sensor network; cloud database services; and fuzzy logic approach. The wireless sensor network is deployed to collect ambient environmental conditions automatically, and the collected information is then managed and applied to a product quality degradation model in the cloud database. The fuzzy logic approach is applied in evaluating the cold-associated occupational safety risk of the different cold chain parties considering specific personal health status. To examine the performance of the proposed system, a cold chain service provider is selected for conducting a comparative analysis before and after applying the IoTRMS.

Findings

The real-time environmental monitoring ensures that the products handled within the desired conditions, namely temperature, humidity and lighting intensity so that any violation of the handling requirements is visible among all cold chain parties. In addition, for cold warehouses and rooms in different cold chain facilities, the personal occupational safety risk assessment is established by considering the surrounding environment and the operators’ personal health status. The frequency of occupational safety risks occurring, including cold-related accidents and injuries, can be greatly reduced. In addition, worker satisfaction and operational efficiency are improved. Therefore, it provides a solid foundation for assessing and identifying product quality and occupational safety risks in cold chain activities.

Originality/value

The cold chain is developed for managing environmentally sensitive products in the right conditions. Most studies found that the risks in cold chain are related to the fluctuation of environmental conditions, resulting in poor product quality and negative influences on consumer health. In addition, there is a lack of occupational safety risk consideration for those who work in cold environments. Therefore, this paper proposes IoTRMS to contribute the area of risk monitoring by means of the IoT application and artificial intelligence techniques. The risk assessment and identification can be effectively established, resulting in secure product quality and appropriate occupational safety management.

Details

Industrial Management & Data Systems, vol. 118 no. 7
Type: Research Article
ISSN: 0263-5577

Keywords

To view the access options for this content please click here
Article
Publication date: 27 April 2012

Kamini Vasudev, Pratish B. Thakkar and Nicola Mitcheson

Patients with severe mental illness (SMI) treated with antipsychotic medication are at increased risk of metabolic side‐effects like weight gain, diabetes mellitus and…

Downloads
1396

Abstract

Purpose

Patients with severe mental illness (SMI) treated with antipsychotic medication are at increased risk of metabolic side‐effects like weight gain, diabetes mellitus and dyslipidaemia. This study aims to examine the feasibility of maintaining a physical health monitoring sheet in patients' records and its impact on physical health of patients with SMI, over a period of one year.

Design/methodology/approach

A physical health monitoring sheet was introduced in all the patients' records on a 15‐bedded male medium secure forensic psychiatric rehabilitation unit, as a prompt to regularly monitor physical health parameters. An audit cycle was completed over a one year period. The data between baseline and re‐audit were compared.

Findings

At baseline, 80 per cent of the patients were identified as smokers, 80 per cent had increased body mass index (BMI) and 87 per cent had raised cardiovascular risk over the next ten years. Appropriate interventions were offered to address the risks. At re‐audit, the physical health monitoring sheets were up to date in 100 per cent of patients' records. The serum lipids and cardiovascular risk over the next ten years reduced over time. No significant change was noted on the parameters including BMI, central obesity, high blood pressure and smoking status.

Research limitations/implications

This was a pilot study and was limited by the small sample size, male gender only and the specific nature of the ward.

Practical implications

There is a need for improved access to physical health care in long‐stay psychiatric settings. A more robust lifestyle modification programme is required to positively influence the physical health parameters in this cohort of patients.

Originality/value

Introduction of a physical health monitoring sheet in patients' records led to regular screening of cardiovascular risks and subsequent increased prescribing of hypolipidaemic agents in individuals with severe mental illness.

Details

International Journal of Health Care Quality Assurance, vol. 25 no. 4
Type: Research Article
ISSN: 0952-6862

Keywords

To view the access options for this content please click here
Article
Publication date: 23 September 2020

Rajeesh Kumar N.V., Arun M., Baraneetharan E., Stanly Jaya Prakash J., Kanchana A. and Prabu S.

Many investigations are going on in monitoring, contact tracing, predicting and diagnosing the COVID-19 disease and many virologists are urgently seeking to create a…

Abstract

Purpose

Many investigations are going on in monitoring, contact tracing, predicting and diagnosing the COVID-19 disease and many virologists are urgently seeking to create a vaccine as early as possible. Even though there is no specific treatment for the pandemic disease, the world is now struggling to control the spread by implementing the lockdown worldwide and giving awareness to the people to wear masks and use sanitizers. The new technologies, including the Internet of things (IoT), are gaining global attention towards the increasing technical support in health-care systems, particularly in predicting, detecting, preventing and monitoring of most of the infectious diseases. Similarly, it also helps in fighting against COVID-19 by monitoring, contract tracing and detecting the COVID-19 pandemic by connection with the IoT-based smart solutions. IoT is the interconnected Web of smart devices, sensors, actuators and data, which are collected in the raw form and transmitted through the internet. The purpose of this paper is to propose the concept to detect and monitor the asymptotic patients using IoT-based sensors.

Design/methodology/approach

In recent days, the surge of the COVID-19 contagion has infected all over the world and it has ruined our day-to-day life. The extraordinary eruption of this pandemic virus placed the World Health Organization (WHO) in a hazardous position. The impact of this contagious virus and scarcity among the people has forced the world to get into complete lockdown, as the number of laboratory-confirmed cases is increasing in millions all over the world as per the records of the government.

Findings

COVID-19 patients are either symptomatic or asymptotic. Symptomatic patients have symptoms such as fever, cough and difficulty in breathing. But patients are also asymptotic, which is very difficult to detect and monitor by isolating them.

Originality/value

Asymptotic patients are very hazardous because without knowing that they are infected, they might spread the infection to others, also asymptotic patients might be having very serious lung damage. So, earlier prediction and monitoring of asymptotic patients are mandatory to save their life and prevent them from spreading.

Details

International Journal of Pervasive Computing and Communications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1742-7371

Keywords

1 – 10 of over 50000