This project originates from the Challenge C4DX‑in‑OHS 2024, focusing on the development of software applications to carry out a predictive prevention process. It seeks to combine worker participation in the prevention workflow (backed by the EU Whistleblowing Directive) with analytical techniques, algorithms, and data‑driven models to anticipate and avoid risks before they occur.

Working at height is one of the most critical risk areas in Occupational Health and Safety. In Europe, slips, trips and falls accounted for 15.6 % of all fatal accidents in 2022, and in Spain there were 716 occupational fatalities that same year, with falls from height involved in 10.3 % of cases. Despite ongoing efforts to improve safety, provisional 2023 figures indicate this trend is still rising. Factors such as low awareness, limited resources and insufficient time to implement preventive measures exacerbate the situation, underscoring the need for innovative, sustainable solutions.

In recent years, Artificial Intelligence (AI) has transformed Occupational Risk Prevention (ORP). Tools like Machine Learning can predict accidents from historical data, while Computer Vision can verify correct use of PPE or control access to restricted zones. These technologies automate hazard identification, reducing severe incidents and enhancing worker well‑being. Democratizing such solutions could lower both direct and indirect costs for companies and public health systems. Moreover, this aligns perfectly with Challenge C4DX‑in‑OHS 2025, as a predictive‑prevention tool could help SMEs embrace ORP and cut their high accident rates.

Develop a software tool that, using Deep Learning and multimodal AI techniques, analyzes images from at‑height operations to detect hazards and recommend preventive measures. The aim is to forecast accidents and foster a comprehensive safety culture by integrating a reporting channel through which workers—or any observer—can flag risks for timely evaluation and action.