A Study on Hyperparameter Configuration for Human Activity Recognition

Kemilly D. Garcia*, Tiago Carvalho, João Mendes-Moreira, João M.P. Cardoso, André C.P.L.F. de Carvalho

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Human Activity Recognition is a machine learning task for the classification of human physical activities. Applications for that task have been extensively researched in recent literature, specially due to the benefits of improving quality of life. Since wearable technologies and smartphones have become more ubiquitous, a large amount of information about a person’s life has become available. However, since each person has a unique way of performing physical activities, a Human Activity Recognition system needs to be adapted to the characteristics of a person in order to maintain or improve accuracy. Additionally, when smartphones devices are used to collect data, it is necessary to manage its limited resources, so the system can efficiently work for long periods of time. In this paper, we present a semi-supervised ensemble algorithm and an extensive study of the influence of hyperparameter configuration in classification accuracy. We also investigate how the classification accuracy is affected by the person and the activities performed. Experimental results show that it is possible to maintain classification accuracy by adjusting hyperparameters, like window size and window overlap, depending on the person and activity performed. These results motivate the development of a system able to automatically adapt hyperparameter settings for the activity performed by each person.

Original languageEnglish
Title of host publication14th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2019, Proceedings
EditorsHéctor Quintián, José António Sáez Muñoz, Emilio Corchado, Francisco Martínez Álvarez, Alicia Troncoso Lora
PublisherSpringer Verlag
Pages47-56
Number of pages10
ISBN (Print)9783030200541
DOIs
Publication statusPublished - 2020
Event14th International Conference on Soft Computing Models in Industrial and Environmental Applications 2019 - Seville, Spain
Duration: 13 May 201915 May 2019
Conference number: 14
http://2019.sococonference.eu/

Publication series

NameAdvances in Intelligent Systems and Computing
Volume950
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference14th International Conference on Soft Computing Models in Industrial and Environmental Applications 2019
Abbreviated titleSOCO 2019
CountrySpain
CitySeville
Period13/05/1915/05/19
Internet address

Keywords

  • Ensemble of classifiers
  • Human Activity Recognition
  • Mobile computing
  • Semi-supervised learning

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