An Efficient Scheme for Prototyping kNN in the Context of Real-Time Human Activity Recognition

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2019

Autores

Paulo J. S. Ferreira (Autor) Outra; Ricardo M. C. Magalhães (Autor) Outra; Kemilly Dearo Garcia (Autor) Outra; João M. P. Cardoso (Autor) FEUP; João Mendes Moreira (Autor) FEUP

Ata de Conferência Internacional

Título: INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2019, PT I

Páginas: 486-493

20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

Univ Manchester, Manchester, ENGLAND, NOV 14-16, 2019

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00R-FNS

DOI: 10.1007/978-3-030-33607-3_52

Abstract (EN): The Classifier kNN is largely used in Human Activity Recognition systems. Research efforts have proposed methods to decrease the high computational costs of the original kNN by focusing, e.g., on approximate kNN solutions such as the ones relying on Locality-sensitive Hashing (LSH). However, embedded kNN implementations need to address the target device memory constraints and power/energy consumption savings. One of the important aspects is the constraint regarding the maximum number of instances stored in the kNN learning process (being it offline or online and incremental). This paper presents simple, energy/computationally efficient and real-time feasible schemes to maintain a maximum number of learning instances stored by kNN. Experiments in the context of HAR show the efficiency of our best approaches, and their capability to avoid the kNN storage runs out of training instances for a given activity, a situation not prevented by typical default schemes.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8