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

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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Category

Academic article

Language

English

Author(s)

  • Enrique Garcia-Ceja
  • Vajira L B Thambawita
  • Steven Hicks
  • Debesh Jha
  • Petter Jakobsen
  • Hugo Lewi Hammer
  • Pål Halvorsen
  • Michael Riegler

Affiliation

  • SINTEF Digital / Sustainable Communication Technologies
  • UiT The Arctic University of Norway
  • Bergen Hospital Trust - Haukeland University Hospital
  • Simula Metropolitan Center for Digital Engineering
  • OsloMet - Oslo Metropolitan University

Year

2021

Published in

Lecture Notes in Computer Science (LNCS)

ISSN

0302-9743

Volume

12573

Page(s)

196 - 205

View this publication at Norwegian Research Information Repository