Siirtola, Pekka; Koskimäki, Heli; Röning, Juha (2018) Personalizing human activity recognition models using incremental learning. In: Proceedings of the26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) 2018, Bruges, Belgium, April 25-27. pp. 627-632.
Personalizing human activity recognition models using incremental learning
|Author:||Siirtola, Pekka1; Koskimäki, Heli1; Röning, Juha1|
1Biomimetics and Intelligent Systems Group, P.O. BOX 4500, FI-90014, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe201901021023
|Publish Date:|| 2019-01-02
In this study, the aim is to personalize inertial sensor databased human activity recognition models using incremental learning. At first, the recognition is based on user-independent model. However, when personal streaming data becomes available, the incremental learning-based recognition model can be updated, and therefore personalized, based on the data without user-interruption. The used incremental learning algorithm is Learn++ which is an ensemble method that can use any classifier as a base classifier. In fact, study compares three different base classifiers: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and classification and regression tree (CART). Experiments are based on publicly open data set and they show that already a small personal training data set can improve the classification accuracy. Improvement using LDA as base classifier is 4.6 percentage units, using QDA 2.0 percentage units, and 2.3 percentage units using CART. However, if the user-independent model used in the first phase of the recognition process is not accurate enough, personalization cannot improve recognition accuracy.
|Pages:||627 - 632|
Proceedings of the26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) 2018, Bruges, Belgium, April 25-27
European symposium on artificial neural networks, computational intelligence and machine learning
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
© 2018 ESANN and the authors. Published in this repository with the kind permission of the publisher.