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5 results on '"Maria Lua Nunes"'

1. Posture Risk Assessment in an Automotive Assembly Line Using Inertial Sensors

Author

Maria Lua Nunes, Duarte Folgado, Carlos Fujao, Luis Silva, Joao Rodrigues, Pedro Matias, Marilia Barandas, Andre V. Carreiro, Sara Madeira, and Hugo Gamboa

Subjects
Inertial sensors, ergonomics, motion analysis, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Musculoskeletal disorders (MSD) are a highly prevalent work-related health problem. Biomechanical exposure to hazardous postures during work is a risk factor for the development of MSD. This study focused on developing an inertial sensor-based approach to evaluate posture in industrial contexts, particularly in automotive assembly lines. The analysis was divided into two stages: 1) a comparative study of joint angles calculated during movements of the upper body segments using the proposed motion tracking framework and the ones provided by a state-of-the-art inertial motion capture system and 2) a work-related posture risk evaluation of operators working in an automative assembly line. For the comparative study, we selected data collected in laboratory (N = 8 participants) and assembly line settings (N = 9 participants), while for the work-related posture risk evaluation, we only considered data acquired within the automotive assembly line. The results revealed that the proposed framework could be applied to track industrial tasks movements performed on the sagittal plane, and the posture evaluation uncovered posture risk differences among different operators that are not considered in traditional posture risk assessment instruments.

Published
2022
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2. TSSEARCH: Time Series Subsequence Search Library

Author

Duarte Folgado, Marília Barandas, Margarida Antunes, Maria Lua Nunes, Hui Liu, Yale Hartmann, Tanja Schultz, and Hugo Gamboa

Subjects
Time series, Subsequence search, Distances, Similarity measurements, Query-based search, Segmentation, Computer software, QA76.75-76.765
Abstract

Subsequence search and distance measures are crucial tools in time series data mining. This paper presents our Python package entitled TSSEARCH, which provides a comprehensive set of methods for subsequence search and similarity measurement in time series. These methods are user-customizable for more flexibility and efficient integration into real deployment scenarios. TSSEARCH enables fast exploratory time series data analysis and was validated in the context of human activity recognition and indoor localization.

Published
2022
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5. Cardiovascular Reactivity (CVR) During Repetitive Work in the Presence of Fatigue

Author

Diogo Carvalho, Luis Silva, Miguel Carvalho, Mariana Dias, Nélson Costa, Duarte Folgado, Maria Lua Nunes, Hugo Gamboa, Kristine Andza, and Elazer Edelman

Abstract

Fatigue during repetitive tasks in the workplace has been intrinsically connected with occupational risk and a reduction in productivity. Currently, the measures taken are based on subjective interpretations of fatigue by the workers or on direct muscular activity, which then make up for a cumulative evaluation of fatigue. The concept of “Industry 4.0” wearable devices would allow a continuous monitoring and thus, a more realistic representation of their fatigue levels.Aim: To quantify heart rate variability, measuring cardiovascular responsiveness, during repetitive work when fatigue is present. Tasks: A protocol was developed to simulate a real-life workplace scenario with a set of low-intensity repetitive tasks that are commonly practiced. The signals obtained were then processed, and heart rate variability was calculated using fractal analysis and time domain variables. Hypothesis: It was hypothesized that 1) the amount of variability and 2) the structure of variability will change during repetitive work in the presence of fatigue.Methodology: Participants were asked to perform three 10-minute trials of a repetitive task. Between each trial, a muscular fatigue protocol was carried out, targeting the main agonist muscle. An ECG was collected during the trials (Baseline, Fatigue 1, and Fatigue 2) through a wearable band placed on the level of the xiphoid appendix. Results: The nonlinearity of the heart rate variability showed no statistically significant changes, unlike the time domain measures that significantly differentiated the baseline trial from the fatigue trials, namely the Standard Deviation of NN intervals, the Root Mean Square of successive RR interval differences, Coefficient of Variation, and Heart Rate itself. Conclusions: These results are enthusiastic for applying algorithms that use heart rate variability to quantify cardiovascular responsiveness to fatigue during repetitive work. They show that with information in the time domain, it is possible to verify physiological changes that the worker is undergoing. Additionally, these changes are also related to the amount of variability and not to the fractal structure of heart rate variability.

Published
2023
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