Abstract
| Original language | English |
|---|---|
| Title of host publication | 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
| Place of Publication | Piscataway, NJ |
| Publisher | IEEE |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-5386-1311-5 |
| ISBN (Print) | 978-1-5386-1312-2 |
| DOIs | |
| Publication status | Published - 23 Jul 2019 |
| Event | 41st 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 2019 → 27 Jul 2019 Conference number: 41 https://embc.embs.org/2019/ |
Publication series
| Name | Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
|---|---|
| Publisher | IEEE |
| Number | 41 |
| Volume | 2019 |
| ISSN (Print) | 1557-170X |
| ISSN (Electronic) | 1558-4615 |
Conference
| Conference | 41st International Engineering in Medicine and Biology Conference, EMBC 2019 |
|---|---|
| Abbreviated title | EMBC |
| Country | Germany |
| City | Berlin |
| Period | 23/07/19 → 27/07/19 |
| Internet address |
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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 proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Building robust models for Human Activity Recognition from raw accelerometers data using Gated Recurrent Units and Long Short Term Memory Neural Networks
AU - Okai, Jeremiah
AU - Paraschiakos, Stylianos
AU - Beekman, Marian
AU - Knobbe, Arno
AU - Pinho Rebelo de Sá, Claudio Frederico
PY - 2019/7/23
Y1 - 2019/7/23
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.
U2 - 10.1109/EMBC.2019.8857288
DO - 10.1109/EMBC.2019.8857288
M3 - Conference contribution
SN - 978-1-5386-1312-2
T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PB - IEEE
CY - Piscataway, NJ
ER -