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This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Selecting and using the same health monitoring devices for a particular problem is a tedious task. This paper aims to provide a comprehensive review of 40 research papers giving the Smart health monitoring system using Internet of things (IoT) and Deep learning.

Health Monitoring Systems play a significant role in the healthcare sector. The development and testing of health monitoring devices using IoT and deep learning dominate the healthcare sector.

In addition, the detailed conversation and investigation are finished by techniques and development framework. Authors have identified the research gap and presented future research directions in IoT, edge computing and deep learning.

The gathered research articles are examined, and the gaps and issues that the current research papers confront are discussed. In addition, based on various research gaps, this assessment proposes the primary future scope for deep learning and IoT health monitoring model.

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.

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.

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.

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.

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.

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.

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.

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.

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.

This case was developed from secondary sources. The secondary sources included company websites, social media and news reports. This case has been classroom tested in multiple executive master of business administration (MBA) courses on business model innovation and entrepreneurship.

The case traces the entrepreneurial journey of Dozee, a remote patient monitoring system in India. Dozee was manufactured by Turtle Shell Technologies Private Limited, cofounded by Mudit and Gaurav. The primary customers of Dozee’s offering were households with elderly citizens and health-conscious individuals who sought preventive health care. The cofounders identified the unmet need for a convenient and user-friendly contactless health tracker. Dozee team built a thin sensor-embedded sheet and module that can be placed beneath the mattress to track sleep patterns and health vitals. They also provided data analysis and data interpretation services. After four years of conceptualization, Dozee launched its product and service in 2019. Although the initial response was lukewarm, the onset of the COVID-19 crisis led to significant changes in the health-care industry. Demand for virtual assistance and contactless monitoring devices became increasingly important elements of COVID-19 treatment. Unlike other sensor-based fitness trackers, the sheet could be easily placed under the patient’s bed to capture health vitals. Choosing to pivot from a home-based individual customer segment to a medical-grade device provider for hospitals could significantly increase the scale and scope of the offering for Dozee, but it would also place Dozee in direct competition with other health monitoring devices from different business categories.

This case is appropriate for MBA and executive-level courses related to entrepreneurship and business model innovation. The case explores issues such as digital disruption and how start-ups can design a go-to-market strategy. The case works well in the classroom, even if people are unfamiliar with the health-care industry. Participants can certainly relate to the concept of adopting artificial intelligence–enabled devices for monitoring their health. The instructor should be able to quickly engage participants in a lively discussion about Dozee’s vision and the opportunities and challenges in adopting digital solutions in health care.

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.

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.

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.

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.