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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.<\/jats:p>","DOI":"10.1007\/s10489-024-05796-1","type":"journal-article","created":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T11:20:16Z","timestamp":1725967216000},"page":"12128-12155","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["ERG-AI: enhancing occupational ergonomics with uncertainty-aware ML and LLM feedback"],"prefix":"10.1007","volume":"54","author":[{"given":"Sagar","family":"Sen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4988-2425","authenticated-orcid":false,"given":"Victor","family":"Gonzalez","sequence":"additional","affiliation":[]},{"given":"Erik Johannes","family":"Husom","sequence":"additional","affiliation":[]},{"given":"Simeon","family":"Tverdal","sequence":"additional","affiliation":[]},{"given":"Shukun","family":"Tokas","sequence":"additional","affiliation":[]},{"given":"Svein O","family":"Tj\u00f8svoll","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,10]]},"reference":[{"issue":"15","key":"5796_CR1","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1093\/eurheartj\/ehab087","volume":"42","author":"A Holtermann","year":"2021","unstructured":"Holtermann A, Schnohr P, Nordestgaard BG, Marott JL (2021) The physical activity paradox in cardiovascular disease and all-cause mortality: the contemporary copenhagen general population study with 104 046 adults. 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