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Learning from Acceleration Data to Differentiate the Posture, Dynamic and Static Work of the Back: An Experimental
Elena Camelia Muşat1, Stelian Alexandru Borz1
1Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, Şirul Beethoven 1, 500123 Brasov, Romania.
Healthcare (Basel, Switzerland)
|May 28, 2022
Summary
Machine learning effectively classifies back postures and differentiates dynamic from static work using triaxial acceleration data. The multilayer perceptron with back propagation (MLPBNN) algorithm shows promise for real-world applications in postural analysis.
Area of Science:
- Biomechanics
- Ergonomics
- Machine Learning
Background:
- Understanding body posture and work type is crucial for assessing biomechanical exposure in ergonomics and healthcare.
- Triaxial acceleration data offers a potential method for analyzing posture and work dynamics.
Purpose of the Study:
- To evaluate the efficacy of machine learning (ML) using triaxial acceleration data for classifying back postures.
- To differentiate between dynamic and static work of the back in an experimental setting.
Main Methods:
- A movement protocol was developed to capture essential back movements.
- A subject wore a triaxial accelerometer; data was filtered and analyzed using Multilayer Perceptron with Back Propagation (MLPBNN) and Random Forest (RF) algorithms.
- ML algorithms were trained and tested to assess their learning and generalization capabilities.
Main Results:
- Machine learning demonstrates significant potential in distinguishing dynamic from static work, contingent on algorithm choice, architecture, and data quality.
- The MLPBNN algorithm, when properly tuned, excelled at differentiating dynamic and static work and generalizing static postures.
- MLPBNN showed superior learning and generalization for static work and postures, suggesting potential for cost-effective offline applications.
Conclusions:
- ML, particularly MLPBNN, is a viable tool for analyzing back posture and work dynamics from triaxial acceleration data.
- The findings support the development of practical, low-cost applications for real-time postural profiling.
- Further research with larger datasets is recommended to optimize performance and address computational costs.
