Hidden Markov Modelling And Recognition Of Euler-Based Motion Patterns For Automatically Detecting Risks Factors From The European Assembly Worksheet
| Autor: | Alina Glushkova, Dimitrios Menychtas, Brenda Elizabeth Olivas-Padilla, Sotiris Manitsaris |
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| Rok vydání: | 2022 |
| Předmět: |
business.product_category
Observer (quantum physics) Computer science 0211 other engineering and technologies 02 engineering and technology Kinematics Machine learning computer.software_genre Motion capture Motion (physics) Set (abstract data type) Gesture recognition 021105 building & construction medicine 0501 psychology and cognitive sciences Hidden Markov model 050107 human factors Hidden Markov Models Worksheet Heuristic business.industry Wearables 05 social sciences Human factors and ergonomics Torso medicine.anatomical_structure Artificial intelligence Ergonomics business Work-related musculoskeletal disorders computer |
| Zdroj: | ICIP |
| DOI: | 10.5281/zenodo.6566272 |
| Popis: | To prevent work-related musculoskeletal disorders (WMSD) the ergonomists apply manual heuristic methods to determine when the worker is exposed to risk factors. However, these methods require an observer and the results can be subjective. This paper proposes a method to automatically evaluate the ergonomic risk factors when performing a set of postures from the ergonomic assessment worksheet (EAWS). Joint angle motion data have been recorded with a full-body motion capture system. These data modeled the motion patterns of four different risk factors, with the use of hidden Markov models (HMMs). Based on the EAWS, automated scores were assigned by the HMMs and were compared to the scores calculated manually. Because the method proposed here is intrusive and requires expensive equipment, kinematic data from a reduced set of two sensors was also evaluated. |
| Databáze: | OpenAIRE |
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