Smart Workplaces for older adults: coping 'ethically' with technology pervasiveness

Exploration on human resource management and prediction model of data-driven information security in Internet of Things

The advent of the Internet of Things (IoT) has accelerated the pace of economic development across all sectors. However, it has also brought significant challenges to traditional human resource management, revealing an increasing number of problems and making it unable to meet the needs of contemporary enterprise management. The IoT has brought numerous conveniences to human society, but it has also led to security issues in communication networks. To ensure the security of these networks, it is necessary to integrate data-driven technologies to address this issue. In response to the current state of human resource management, this paper proposes the application of IoT technology in enterprise human resource management and combines it with radial basis function neural networks to construct a model for predicting enterprise human resource needs. The model was also experimentally analyzed. The results show that under this algorithm, the average prediction accuracy for the number of employees over five years is 90.2 %, and the average prediction accuracy for sales revenue is 93.9 %. These data indicate that the prediction accuracy of the model under this study's algorithm has significantly improved. This paper also conducted evaluation experiments on a wireless communication network security risk prediction model. The average prediction accuracy of four tests is 91.21 %, indicating that the model has high prediction accuracy. By introducing data-driven technology and IoT applications, this study provides new solutions for human resource management and communication network security, promoting technological innovation in the fields of traditional human resource management and information security management. The research not only improves the accuracy of the prediction models but also provides strong support for decision-making and risk management in related fields, demonstrating the great potential of big data and artificial intelligence technology in the future of enterprise management and security.

A machine learning approach to evaluate the impact of virtual balance/cognitive training on fall risk in older women

Introduction

Novel technologies based on virtual reality (VR) are creating attractive virtual environments with high ecological value, used both in basic/clinical neuroscience and modern medical practice. The study aimed to evaluate the effects of VR-based training in an elderly population.

Materials and methods

The study included 36 women over the age of 60, who were randomly divided into two groups subjected to balance-strength and balance-cognitive training. The research applied both conventional clinical tests, such as (a) the Timed Up and Go test, (b) the five-times sit-to-stand test, and (c) the posturographic exam with the Romberg test with eyes open and closed. Training in both groups was conducted for 10 sessions and embraced exercises on a bicycle ergometer and exercises using non-immersive VR created by the ActivLife platform. Machine learning methods with a k-nearest neighbors classifier, which are very effective and popular, were proposed to statistically evaluate the differences in training effects in the two groups.

Results and conclusion

The study showed that training using VR brought beneficial improvement in clinical tests and changes in the pattern of posturographic trajectories were observed. An important finding of the research was a statistically significant reduction in the risk of falls in the study population. The use of virtual environments in exercise/training has great potential in promoting healthy aging and preventing balance loss and falls among seniors.

Being in Virtual Reality and Its Influence on Brain Health-An Overview of Benefits, Limitations and Prospects

BACKGROUND

Dynamic technological development and its enormous impact on modern societies are posing new challenges for 21st-century neuroscience. A special place is occupied by technologies based on virtual reality (VR). VR tools have already played a significant role in both basic and clinical neuroscience due to their high accuracy, sensitivity and specificity and, above all, high ecological value.

OBJECTIVE

Being in a digital world affects the functioning of the body as a whole and its individual systems. The data obtained so far, both from experimental and modeling studies, as well as (clinical) observations, indicate their great and promising potential, but apart from the benefits, there are also losses and negative consequences for users.

METHODS

This review was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework across electronic databases (such as Web of Science Core Collection; PubMed; and Scopus, Taylor & Francis Online and Wiley Online Library) to identify beneficial effects and applications, as well as adverse impacts, especially on brain health in human neuroscience.

RESULTS

More than half of these articles were published within the last five years and represent state-of-the-art approaches and results (e.g., 54.7% in Web of Sciences and 63.4% in PubMed), with review papers accounting for approximately 16%. The results show that in addition to proposed novel devices and systems, various methods or procedures for testing, validation and standardization are presented (about 1% of articles). Also included are virtual developers and experts, (bio)(neuro)informatics specialists, neuroscientists and medical professionals.

CONCLUSIONS

VR environments allow for expanding the field of research on perception and cognitive and motor imagery, both in healthy and patient populations. In this context, research on neuroplasticity phenomena, including mirror neuron networks and the effects of applied virtual (mirror) tasks and training, is of interest in virtual prevention and neurogeriatrics, especially in neurotherapy and neurorehabilitation in basic/clinical and digital neuroscience.

Review of ethics for occupational hygiene hazard monitoring surveys using sensors

This review is about the ethical use of sensors to monitor occupational exposure to hazards. It considers whether the same or different, ethical measures apply to using sensors, compared to conventional hazard monitoring surveys. To undertake the review, subject experts developed a research question, identified suitable search terms, and set the scope of these searches. Candidate research papers dating from 2000 to mid-2022 that met inclusion criteria were identified and reviewed by each author. Ethical concerns were identified by the authors of studies in which sensors were used to monitor employee health and well-being, but most of the studies that used them to monitor employee exposure to hazards focused on the technical aspects of their deployment. These ethical concerns included questions about employee rights and privacy, the anonymity of the data collected with sensors, and how the security of this data is managed. The review considers ethical standards and codes of practice for occupational hygiene work and the ethical risks when sensors are used to gather data. Sensors may provide insight into occupational exposure to hazards, but their use is not always adequately explained to employees by those managing this monitoring work. The ethical concerns identified were relevant to many areas of industrial hygiene work, but more studies are required that consider the ethical use of sensors in workplaces. Studies that monitored employee health, well-being, and productivity, identified ethical risks in using sensors to monitor these endpoints. An ethical framework and checklist for hygienists are proposed including a set of questions that consider the risks of using sensors to monitor occupational hazards. Industrial hygiene professional bodies provide ethical codes of practice for their members but may also need to consider the implications of using sensors in workplaces. Ethical standards support the collection of industrial hygiene exposure data whilst maintaining the privacy rights of employees.

The Need for Smart Architecture Caused by the Impact of COVID-19 upon Architecture and City: A Systematic Literature Review

The recent pandemic era of COVID-19 has shown social adjustment on a global scale in an attempt to reduce contamination. In response, academic studies relating to smart technologies have increased to assist with governmental restrictions such as social distancing. Despite the restrictions, architectural, engineering and construction industries have shown an increase in budget and activity. An investigation of the adjustments made in response to the pandemic through utilizing new technologies, such as the internet of things (IoT) and smart technologies, is necessary to understand the research trends of the new normal. This study should address various sectors, including business, healthcare, architecture, education, tourism and transportation. In this study, a literature review was performed on two web-based, peer-reviewed journal databases, SCOPUS and Web of Science, to identify a trend in research for the pandemic era in various sectors. The results from 123 papers revealed a focused word group of IoT, smart technologies, architecture, building, space and COVID-19. Overlapping knowledges of IoT systems, within the design of a building which was designed for a specific purpose, were discovered. The findings justify the need for a new sub-category within the field of architecture called “smart architecture”. This aims to categorize the knowledge which is required to embed IoT systems in three key architectural topics—planning, design, and construction—for building design with specific purposes, tailored to various sectors.

Ethical Decision Making in Iot Data Driven Research: A Case Study of a Large-Scale Pilot

IoT technologies generate intelligence and connectivity and develop knowledge to be used in the decision-making process. However, research that uses big data through global interconnected infrastructures, such as the ‘Internet of Things’ (IoT) for Active and Healthy Ageing (AHA), is fraught with several ethical concerns. A large-scale application of IoT operating in diverse piloting contexts and case studies needs to be orchestrated by a robust framework to guide ethical and sustainable decision making in respect to data management of AHA and IoT based solutions. The main objective of the current article is to present the successful completion of a collaborative multiscale research work, which addressed the complicated exercise of ethical decision making in IoT smart ecosystems for older adults. Our results reveal that among the strong enablers of the proposed ethical decision support model were the participatory and deliberative procedures complemented by a set of regulatory and non-regulatory tools to operationalize core ethical values such as transparency, trust, and fairness in real care settings for older adults and their caregivers.