kNN Prototyping Schemes for Embedded Human Activity Recognition with Online Learning

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Ferreira, PJS (Author) Other; João M. P. Cardoso (Author) FEUP; João Mendes-Moreira (Author) FEUP

Journal

Title: Computers

Vol. 9

Pages: 1-96

Publisher: MDPI

Indexing

Scopus - 24 Citations

Other information

Authenticus ID: P-00T-32E

DOI: 10.3390/computers9040096

Abstract (EN): The kNN machine learning method is widely used as a classifier in Human Activity Recognition (HAR) systems. Although the kNN algorithm works similarly both online and in offline mode, the use of all training instances is much more critical online than offline due to time and memory restrictions in the online mode. Some methods propose decreasing the high computational costs of kNN by focusing, e.g., on approximate kNN solutions such as the ones relying on Locality-Sensitive Hashing (LSH). However, embedded kNN implementations also need to address the target device¿s memory constraints, especially as the use of online classification needs to cope with those constraints to be practical. This paper discusses online approaches to reduce the number of training instances stored in the kNN search space. To address practical implementations of HAR systems using kNN, this paper presents simple, energy/computationally efficient, and real-time feasible schemes to maintain at runtime a maximum number of training instances stored by kNN. The proposed schemes include policies for substituting the training instances, maintaining the search space to a maximum size. Experiments in the context of HAR datasets show the efficiency of our best schemes. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Language: English

Type (Professor's evaluation): Scientific