Self-Similarity Matrix of Morphological Features for Motion Data Analysis in Manufacturing Scenarios

António Santos, João Rodrigues, João Rodrigues, Duarte Folgado, Duarte Folgado, Sara Santos, Carlos Alberto Rosado Fujão, Hugo Gamboa, Hugo Gamboa

2021

Abstract

There is a significant interest to evaluate the exposure that operators are subjected throughout the working day. The objective evaluation of occupational exposure with direct measurements and the need for automatic annotation of relevant events arose. Using time series retrieved from inertial sensors, this work proposes a method that is able to automatically: (1) detect anomalies, (2) segment the working cycles and (3) by means of query-by-example, identify sub segments along the working cycle. In a short summary, this technique firstly organizes the dataset provided by all inertial measurement units (IMUs) sensors placed over the dominant upper limb. After this, it retrieves a wide variety of features to an organized matrix and then calculates the respective self-similarity matrix (SSM). This method provides information by comparing each subsequence of the time series with the remaining subsequences. As the identified structures will provide information about how repetitive or anomalous is the behaviour of the data in function of time. The results show that the presented method is capable of identifying anomalies on this dataset with an accuracy of 82%, detect working cycles with a duration error of about 6% of the working cycle, and has the ability to find matches of sub-sequences of the working cycle.

Paper Citation

in Harvard Style

Santos A., Rodrigues J., Folgado D., Santos S., Alberto Rosado Fujão C. and Gamboa H. (2021). Self-Similarity Matrix of Morphological Features for Motion Data Analysis in Manufacturing Scenarios. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS; ISBN 978-989-758-490-9, SciTePress, pages 80-90. DOI: 10.5220/0010252800002865

in Bibtex Style

@conference{biosignals21,
author={António Santos and João Rodrigues and Duarte Folgado and Sara Santos and Carlos Alberto Rosado Fujão and Hugo Gamboa},
title={Self-Similarity Matrix of Morphological Features for Motion Data Analysis in Manufacturing Scenarios},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS},
year={2021},
pages={80-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010252800002865},
isbn={978-989-758-490-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS
TI - Self-Similarity Matrix of Morphological Features for Motion Data Analysis in Manufacturing Scenarios
SN - 978-989-758-490-9
AU - Santos A.
AU - Rodrigues J.
AU - Folgado D.
AU - Santos S.
AU - Alberto Rosado Fujão C.
AU - Gamboa H.
PY - 2021
SP - 80
EP - 90
DO - 10.5220/0010252800002865
PB - SciTePress