research-article

Human Activity Recognition using Time Series Feature Extraction and Active Learning

Authors:

Vangjel Kazllarof,

Sotiris KotsiantisAuthors Info & Claims

SETN '22: Proceedings of the 12th Hellenic Conference on Artificial Intelligence

Article No.: 46, Pages 1 - 4

https://doi.org/10.1145/3549737.3549787

Published: 09 September 2022 Publication History

Abstract

Today, portable devices like smartwatches and smartphones have made a great impact in human's wellbeing. From sleep monitoring to exercise scheduling, Human Activity Recognition had played a major role in the habits of the people. In this work, we exploit a Time Series dataset that describes a Human Activity Recognition signal. In the beginning, we extract the features oriented on Spectral, Statistical and Temporal domains. Then, we construct a dataset for each domain and we calculate the classification results using a number of different classifiers. In the sequel, we apply Active Learning techniques and calculate their classification accuracy performance using a small portion of the original datasets as initial labeled set. Finally, we compare the original results with the ones produced with Active Learning methods.

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Cited By

Gómez-Carmona OCasado-Mansilla DLópez-de-Ipiña DGarcía-Zubia J(2024)Human-in-the-loop machine learning: Reconceptualizing the role of the user in interactive approachesInternet of Things10.1016/j.iot.2023.10104825(101048)Online publication date: Apr-2024
https://doi.org/10.1016/j.iot.2023.101048

Index Terms

Human Activity Recognition using Time Series Feature Extraction and Active Learning

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cover image ACM Other conferences

SETN '22: Proceedings of the 12th Hellenic Conference on Artificial Intelligence

September 2022

450 pages

ISBN:9781450395977

DOI:10.1145/3549737

Copyright © 2022 ACM.

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Published: 09 September 2022

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SETN 2022

SETN 2022: 12th Hellenic Conference on Artificial Intelligence

September 7 - 9, 2022

Corfu, Greece

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Gómez-Carmona OCasado-Mansilla DLópez-de-Ipiña DGarcía-Zubia J(2024)Human-in-the-loop machine learning: Reconceptualizing the role of the user in interactive approachesInternet of Things10.1016/j.iot.2023.10104825(101048)Online publication date: Apr-2024
https://doi.org/10.1016/j.iot.2023.101048

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