University of Oulu

Siirtola, P., Koskimäki, H., Röning, J., Importance of user inputs while using incremental learning to personalize human activity recognition models, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2019, ES2019-63, p. 449-454

Importance of user inputs while using incremental learning to personalize human activity recognition models

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Author: Siirtola, Pekka1; Koskimäki, Heli1; Röning, Juha1
Organizations: 1Biomimetics and Intelligent Systems Group, P.O. BOX 4500, FI-90014, University of Oulu, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.1 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020040810812
Language: English
Published: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2019
Publish Date: 2020-04-08
Description:

Abstract

In this study, importance of user inputs is studied in the context of personalizing human activity recognition models using incremental learning. Inertial sensor data from three body positions are used, and the classification is based on Learn++ ensemble method. Three different approaches to update models are compared: non-supervised, semi-supervised and supervised. Non-supervised approach relies fully on predicted labels, supervised fully on user labeled data, and the proposed method for semisupervised learning, is a combination of these two. In fact, our experiments show that by relying on predicted labels with high confidence, and asking the user to label only uncertain observations (from 12% to 26% of the observations depending on the used base classifier), almost as low error rates can be achieved as by using supervised approach. In fact, the difference was less than 2%-units. Moreover, unlike non-supervised approach, semisupervised approach does not suffer from drastic concept drift, and thus, the error rate of the non-supervised approach is over 5%-units higher than using semi-supervised approach.

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ISBN Print: 978-2-87587-065-0
Pages: 449 - 454
Article number: ES2019-63
Host publication: 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2019
Conference: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Subjects:
Copyright information: © The Authors 2019.