Wearable sensors for classification of load-handling tasks with machine learning algorithms in occupational safety and health: a systematic literature review

Resumo original em inglês

O que aconteceu

Ergonomic assessments are critical to preventing work-related musculoskeletal disorders. The integration of machine learning with wearable sensor technology offers new approaches to risk assessment by capturing external forces and non-ergonomic working conditions. We conducted a systematic literature search, reviewing 851 studies from PubMed, Web of Science and Embase, and included 15 studies in our analysis. This review summarises and critically discusses these studies, which focus on posture classification, activity duration and external weight estimation in load handling tasks. Although the results are promising, current research covers only a few aspects, with limited emphasis on the measurement of external forces. Furthermore, many studies faced fundamental issues such as small sample sizes and limited access to research data and algorithms. Future advances in this area could greatly benefit from the sharing of datasets and algorithms, thereby increasing the comparability and robustness of findings.

Resumo publicado sob licença CC BY. A tradução para o português ainda não chegou a este estudo.