Título
Physical exertions recognition using surface electromyography and inertial measurements for occupational ergonomics
Autor
HUGO GUSTAVO GONZALEZ HERNANDEZ
Nivel de Acceso
Acceso Abierto
Materias
Resumen o descripción
Automation of ergonomic risk level assessments using predictive models is limited to the recognition of certain activities with the intention of calculating some ergonomic risk factors, this means that the predictive models are only useful to recognize the activities that were taught. In this thesis, a framework was developed to automatically recognize the physical exertions done by an
operator during a manual work task towards the automation of Job Strain Index (JSI) assessment. The framework includes the use of a wearable device that captures surface electromyography (sEMG) signals and inertial measurements called Mindrove armband, and provides the data treatments that maximized the training accuracy of a Cubic Support Vector Machine (CSVM) model, which was responsible for predicting the exertions depending on the behavior of the data. To determine the best data treatments, full factorial experiments were designed and analyses of variance (ANOVA) were performed. Thus, the best data treatments to obtain a maximum average training accuracy of 93.29% and testing accuracy of 94.31% of the CSVM were the filtering of raw signals with a 4th order Butterworth filter, the rectification of sEMG signals, the outliers' removal in data via Hampel identifier, the computation of the RMS envelope and normalization of sEMG, the zero calibration for inertial measurements, and the extraction of 126 statistical features. Additionally, the Visual Signal Analyzer App (SIANA) was developed for data processing, which works under the proposed framework. Automatic recognition of any physical exertion means that an automated JSI can
be applied to any manual work task, thereby identifying more quickly the risk level of a work task in order to modify it to avoid occupational diseases and accidents.
Master of Science in Engineering
Editor
Instituto Tecnológico y de Estudios Superiores de Monterrey
Fecha de publicación
2 de diciembre de 2022
Tipo de publicación
Tesis de maestría
Recurso de información
Formato
application/pdf
Idioma
Inglés
Audiencia
Público en general
Repositorio Orígen
Repositorio Institucional del Tecnológico de Monterrey
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