UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Deep human activity recognition using wearable sensors

Lawal, IA; Bano, S; (2019) Deep human activity recognition using wearable sensors. In: Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments. (pp. pp. 45-48). ACM: New York (NY), USA. Green open access

[thumbnail of Bano_Deep human activity recognition using wearable sensors_AAM.pdf]
Preview
Text
Bano_Deep human activity recognition using wearable sensors_AAM.pdf - Accepted Version

Download (634kB) | Preview

Abstract

This paper addresses the problem of classifying motion signals acquired via wearable sensors for the recognition of human activity. Automatic and accurate classification of motion signals is important in facilitating the development of an effective automated health monitoring system for the elderlies. Thus, we gathered hip motion signals from two different waist mounted sensors and for each individual sensor, we converted the motion signal into spectral image sequence. We use these images as inputs to independently train two Convolutional Neural Networks (CNN), one for each of the generated image sequences from the two sensors. The outputs of the trained CNNs are then fused together to predict the final class of the human activity. We evaluate the performance of the proposed method using the cross-subjects testing approach. Our method achieves recognition accuracy (F1 score) of 0.87 on a publicly available real-world human activity dataset. This performance is superior to that reported by another state-of-the-art method on the same dataset.

Type: Proceedings paper
Title: Deep human activity recognition using wearable sensors
Event: The 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA '19 )
Location: Rhodes, Greece
Dates: 5th-7th June 2019
ISBN-13: 978-1-4503-6232-0
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3316782.3321538
Publisher version: https://doi.org/10.1145/3316782.3321538
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Human Activity Recognition, Convolutional neural network, Ensemble method
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10078795
Downloads since deposit
283Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item