An LSTM-based Descriptor for Human Activities Recognition using IMU Sensors

Sara Ashry, Reda Elbasiony, Walid Gomaa

Abstract

In this article, we present a public human activity dataset called ‘HAD-AW’. It consists of four types of 3D sensory signals: acceleration, angular velocity, rotation displacement, and gravity for 31 activities of daily living ADL measured by a wearable smart watch. It is created as a benchmark for algorithms comparison. We succinctly survey some existing datasets and compare them to ‘HAD-AW’. The goal is to make the dataset usable and extendible by others. We introduce a framework of ADL recognition by making various pre-processing steps based on statistical and physical features which we call AMED. These features are then classified using an LSTM recurrent network. The proposed approach is compared to a random-forest algorithm. Finally, our experiments show that the joint use of all four sensors has achieved the best prediction accuracy reaching 95.3% for all activities. It also achieves savings from 88% to 98% in the training and testing time; compared to the random forest classifier. To show the effectiveness of the proposed method, it is evaluated on other four public datasets: CMU-MMAC, USC-HAD, REALDISP, and Gomaa datasets.

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Paper Citation


in Harvard Style

Ashry S., Elbasiony R. and Gomaa W. (2018). An LSTM-based Descriptor for Human Activities Recognition using IMU Sensors.In Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-321-6, pages 494-501. DOI: 10.5220/0006902404940501


in Bibtex Style

@conference{icinco18,
author={Sara Ashry and Reda Elbasiony and Walid Gomaa},
title={An LSTM-based Descriptor for Human Activities Recognition using IMU Sensors},
booktitle={Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2018},
pages={494-501},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006902404940501},
isbn={978-989-758-321-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - An LSTM-based Descriptor for Human Activities Recognition using IMU Sensors
SN - 978-989-758-321-6
AU - Ashry S.
AU - Elbasiony R.
AU - Gomaa W.
PY - 2018
SP - 494
EP - 501
DO - 10.5220/0006902404940501