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2021 | OriginalPaper | Chapter

HTAD: A Home-Tasks Activities Dataset with Wrist-Accelerometer and Audio Features

Authors : Enrique Garcia-Ceja, Vajira Thambawita, Steven A. Hicks, Debesh Jha, Petter Jakobsen, Hugo L. Hammer, Pål Halvorsen, Michael A. Riegler

Published in: MultiMedia Modeling

Publisher: Springer International Publishing

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Abstract

In this paper, we present HTAD: A Home Tasks Activities Dataset. The dataset contains wrist-accelerometer and audio data from people performing at-home tasks such as sweeping, brushing teeth, washing hands, or watching TV. These activities represent a subset of activities that are needed to be able to live independently. Being able to detect activities with wearable devices in real-time is important for the realization of assistive technologies with applications in different domains such as elderly care and mental health monitoring. Preliminary results show that using machine learning with the presented dataset leads to promising results, but also there is still improvement potential. By making this dataset public, researchers can test different machine learning algorithms for activity recognition, especially, sensor data fusion methods.

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Title: HTAD: A Home-Tasks Activities Dataset with Wrist-Accelerometer and Audio Features
Authors: Enrique Garcia-Ceja
Vajira Thambawita
Steven A. Hicks
Debesh Jha
Petter Jakobsen
Hugo L. Hammer
Pål Halvorsen
Michael A. Riegler
Publisher: Springer International Publishing
Book: MultiMedia Modeling
Print ISBN: 978-3-030-67834-0

Electronic ISBN: 978-3-030-67835-7

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-67835-7_17

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