Evaluating Movement and Device-Specific DeepConvLSTM Performance in Wearable-Based Human Activity Recognition

Gabriela Ciortuz, Hawzhin Hozhabr Pour, Sebastian Fudickar

2024

Abstract

This article provides a comprehensive look at human activity recognition via three consumer devices with different body placements and a deep hybrid model containing CNN and LSTM layers. The used dataset consists of 53 activities recorded from the motion sensors (IMUs) of the three devices. Compared to the available human activity recognition datasets, this dataset holds the biggest number of classes, enabling us to provide an in-depth analysis of activity recognition for health-related assessments, as well as a comparison with other benchmark models such as a CNN and LSTM model. In addition, we categorize the activities into six movement groups and discuss their relevance for health-related assessments. Our results show that the hybrid model outperforms the benchmark models for all devices individually and all together. Furthermore, we show that the smartwatch could as a standalone consumer device classify activities in the six movement groups very well and for most of the use cases using a smartwatch would be practical.

Paper Citation

in Harvard Style

Ciortuz G., Hozhabr Pour H. and Fudickar S. (2024). Evaluating Movement and Device-Specific DeepConvLSTM Performance in Wearable-Based Human Activity Recognition. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-688-0, SciTePress, pages 746-753. DOI: 10.5220/0012471300003657

in Bibtex Style

@conference{healthinf24,
author={Gabriela Ciortuz and Hawzhin Hozhabr Pour and Sebastian Fudickar},
title={Evaluating Movement and Device-Specific DeepConvLSTM Performance in Wearable-Based Human Activity Recognition},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2024},
pages={746-753},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012471300003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Evaluating Movement and Device-Specific DeepConvLSTM Performance in Wearable-Based Human Activity Recognition
SN - 978-989-758-688-0
AU - Ciortuz G.
AU - Hozhabr Pour H.
AU - Fudickar S.
PY - 2024
SP - 746
EP - 753
DO - 10.5220/0012471300003657
PB - SciTePress