Building robust models for Human Activity Recognition from raw accelerometers data using Gated Recurrent Units and Long Short Term Memory Neural Networks

Jeremiah Okai, Stylianos Paraschiakos, Marian Beekman, Arno Knobbe, Claudio Frederico Pinho Rebelo de Sá

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Human Activity Recognition (HAR) is a growing field of research in biomedical engineering and it has many potential applications in the treatment and prevention of several diseases. Due to the recent advancement in technology, devices that collect position and orientation measurements (e.g. accelerometers and gyroscopes) are becoming ubiquitous. These measurements can then be used to train machine learning models for HAR. In this research, we propose one recurrent neural network architecture and a data augmentation approach for building robust and accurate models for HAR. We compared models with Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers. The proposed data augmentation approach was used to make the models robust to the cases where one or more sensors are missing. In this empirical study, we could also understand some relations between the ideal locations of the sensors in the participants and the types of activities performed. The proposed approaches were tested in the GOTOv dataset from a study which involved 35 participants performing 16 sedentary, ambulatory and lifestyle activities in a semi-structured environment. The results presented, clearly show that the models are able to detect these activities in a robust way.
Original languageEnglish
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-5386-1311-5
ISBN (Print)978-1-5386-1312-2
DOIs
Publication statusPublished - 23 Jul 2019
Event41st International Engineering in Medicine and Biology Conference, EMBC 2019: Biomedical Engineering Ranging from Wellness to Intensive Care Medicine - CityCube Berlin, Berlin, Germany
Duration: 23 Jul 201927 Jul 2019
Conference number: 41
https://embc.embs.org/2019/

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PublisherIEEE
Number41
Volume2019
ISSN (Print)1557-170X
ISSN (Electronic)1558-4615

Conference

Conference41st International Engineering in Medicine and Biology Conference, EMBC 2019
Abbreviated titleEMBC
CountryGermany
CityBerlin
Period23/07/1927/07/19
Internet address

Fingerprint

Accelerometers
Neural networks
Biomedical engineering
Recurrent neural networks
Bioelectric potentials
Gyroscopes
Sensors
Network architecture
Learning systems
Long short-term memory

Cite this

Okai, J., Paraschiakos, S., Beekman, M., Knobbe, A., & Pinho Rebelo de Sá, C. F. (2019). Building robust models for Human Activity Recognition from raw accelerometers data using Gated Recurrent Units and Long Short Term Memory Neural Networks. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Vol. 2019, No. 41). Piscataway, NJ: IEEE. https://doi.org/10.1109/EMBC.2019.8857288
Okai, Jeremiah ; Paraschiakos, Stylianos ; Beekman, Marian ; Knobbe, Arno ; Pinho Rebelo de Sá, Claudio Frederico. / Building robust models for Human Activity Recognition from raw accelerometers data using Gated Recurrent Units and Long Short Term Memory Neural Networks. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Piscataway, NJ : IEEE, 2019. (Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 41).
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abstract = "Human Activity Recognition (HAR) is a growing field of research in biomedical engineering and it has many potential applications in the treatment and prevention of several diseases. Due to the recent advancement in technology, devices that collect position and orientation measurements (e.g. accelerometers and gyroscopes) are becoming ubiquitous. These measurements can then be used to train machine learning models for HAR. In this research, we propose one recurrent neural network architecture and a data augmentation approach for building robust and accurate models for HAR. We compared models with Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers. The proposed data augmentation approach was used to make the models robust to the cases where one or more sensors are missing. In this empirical study, we could also understand some relations between the ideal locations of the sensors in the participants and the types of activities performed. The proposed approaches were tested in the GOTOv dataset from a study which involved 35 participants performing 16 sedentary, ambulatory and lifestyle activities in a semi-structured environment. The results presented, clearly show that the models are able to detect these activities in a robust way.",
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Okai, J, Paraschiakos, S, Beekman, M, Knobbe, A & Pinho Rebelo de Sá, CF 2019, Building robust models for Human Activity Recognition from raw accelerometers data using Gated Recurrent Units and Long Short Term Memory Neural Networks. in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), no. 41, vol. 2019, IEEE, Piscataway, NJ, 41st International Engineering in Medicine and Biology Conference, EMBC 2019, Berlin, Germany, 23/07/19. https://doi.org/10.1109/EMBC.2019.8857288

Building robust models for Human Activity Recognition from raw accelerometers data using Gated Recurrent Units and Long Short Term Memory Neural Networks. / Okai, Jeremiah ; Paraschiakos, Stylianos; Beekman, Marian; Knobbe, Arno; Pinho Rebelo de Sá, Claudio Frederico.

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Piscataway, NJ : IEEE, 2019. (Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Vol. 2019, No. 41).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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AU - Pinho Rebelo de Sá, Claudio Frederico

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N2 - Human Activity Recognition (HAR) is a growing field of research in biomedical engineering and it has many potential applications in the treatment and prevention of several diseases. Due to the recent advancement in technology, devices that collect position and orientation measurements (e.g. accelerometers and gyroscopes) are becoming ubiquitous. These measurements can then be used to train machine learning models for HAR. In this research, we propose one recurrent neural network architecture and a data augmentation approach for building robust and accurate models for HAR. We compared models with Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers. The proposed data augmentation approach was used to make the models robust to the cases where one or more sensors are missing. In this empirical study, we could also understand some relations between the ideal locations of the sensors in the participants and the types of activities performed. The proposed approaches were tested in the GOTOv dataset from a study which involved 35 participants performing 16 sedentary, ambulatory and lifestyle activities in a semi-structured environment. The results presented, clearly show that the models are able to detect these activities in a robust way.

AB - Human Activity Recognition (HAR) is a growing field of research in biomedical engineering and it has many potential applications in the treatment and prevention of several diseases. Due to the recent advancement in technology, devices that collect position and orientation measurements (e.g. accelerometers and gyroscopes) are becoming ubiquitous. These measurements can then be used to train machine learning models for HAR. In this research, we propose one recurrent neural network architecture and a data augmentation approach for building robust and accurate models for HAR. We compared models with Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers. The proposed data augmentation approach was used to make the models robust to the cases where one or more sensors are missing. In this empirical study, we could also understand some relations between the ideal locations of the sensors in the participants and the types of activities performed. The proposed approaches were tested in the GOTOv dataset from a study which involved 35 participants performing 16 sedentary, ambulatory and lifestyle activities in a semi-structured environment. The results presented, clearly show that the models are able to detect these activities in a robust way.

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M3 - Conference contribution

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BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

PB - IEEE

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ER -

Okai J, Paraschiakos S, Beekman M, Knobbe A, Pinho Rebelo de Sá CF. Building robust models for Human Activity Recognition from raw accelerometers data using Gated Recurrent Units and Long Short Term Memory Neural Networks. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Piscataway, NJ: IEEE. 2019. (Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 41). https://doi.org/10.1109/EMBC.2019.8857288