Workers, especially those involved in jobs requiring extended standing or repetitive movements, often face significant health challenges due to Musculoskeletal Disorders (MSDs). To mitigate MSD risks, enhancing workplace ergonomics is vital, which includes forecasting long-term employee postures, educating workers about related occupational health risks, and offering relevant recommendations. However, research gaps remain, such as the lack of a sustainable AI/ML pipeline that combines sensor-based, uncertainty-aware posture prediction with large language models for natural language communication of occupational health risks and recommendations. We introduce ERG-AI, a machine learning pipeline designed to predict extended worker postures using data from multiple wearable sensors. Alongside providing posture prediction and uncertainty estimates, ERG-AI also provides personalized health risk assessments and recommendations by generating prompts based on its performance and prompting Large Language Model (LLM) APIs, like GPT-4, to obtain user-friendly output. We used the Digital Worker Goldicare dataset to assess ERG-AI, which includes data from 114 home care workers who wore five tri-axial accelerometers in various bodily positions for a cumulative 2913 hours. The evaluation focused on the quality of posture prediction under uncertainty, energy consumption and carbon footprint of ERG-AI and the effectiveness of personalized recommendations rendered in easy-to-understand language.

A security indicator is a sign that shows us what something is like or how a situation is changing and can aid us in making informed estimations on cyber risks. There are many different breeds of security indicators, but, unfortunately, they are not always easy to apply due to a lack of available or credible sources of data. This paper undertakes a systematic mapping study on the academic literature related to cyber security indicator data. We identified 117 primary studies from the past five years as relevant to answer our research questions. They were classified according to a set of categories related to research type, domain, data openness, usage, source, type and content. Our results show a linear growth of publications per year, where most indicators are based on free or internal technical data that are domain independent. While these indicators can give valuable information about the contemporary cyber risk, the increasing usage of unconventional data sources and threat intelligence feeds of more strategic and tactical nature represent a more forward-looking trend. In addition, there is a need to take methods and techniques developed by the research community from the conceptual plane and make them practical enough for real-world application.

The proliferation of IoT devices on the Internet is currently in the billions, and projections anticipate that there will be 50 billion connected IoT devices in the IoT market by 2030. This rapid expansion will result in a substantial increase in data, which includes personal data, generated by these IoT devices. It is estimated that the data volume will reach 73.1 zettabytes by 2025. To fully realize the substantial benefits of the IoT, it is imperative to facilitate the responsible utilization and sharing of this data among various stakeholders. However, it is crucial to establish robust security and trust mechanisms to ensure data integrity and privacy during data sharing. Our objective is to summarize and assess the research efforts that address secure IoT data sharing. We systematically review the state-of-the-art techniques ensuring and preserving security in the IoT data-sharing environment through a systematic literature review (SLR) study. We pose three research questions, define selection and exclusion criteria for primary studies, and extract and synthesize data from these studies to answer our research questions. Our SLR results can help readers to obtain (i) an overview of existing secure IoT data-sharing approaches and related issues, (ii) a deep-dive into Edge-focused secure IoT data sharing solutions, and (iii) research directions that require attention from the research community for follow-up work.

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