Physical Activity Recognition from Smartphone Embedded Sensors

• Type: Article in International Conference Proceedings Book
• Date: 2013

Authors

João Prudêncio (Author) Other; Ana Aguiar (Author) FEUP; Daniel Lucani (Author) Other

Conference proceedings International

Title: PATTERN RECOGNITION AND IMAGE ANALYSIS, IBPRIA 2013

Pages: 863-872

6th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)

Funchal, PORTUGAL, JUN 05-07, 2013

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 8 Citations

Other information

Authenticus ID: P-008-DES

DOI: 10.1007/978-3-642-38628-2_102

Abstract (EN): The ubiquity of smartphones has motivated efforts to use the embedded sensors to detect various aspects of user context to transparently provide personalized and contextualized services to the user. One relevant piece of context is the physical activity of the smartphone user. In this paper, we propose a novel set of features for distinguishing five physical activities using only sensors embedded in the smartphone. Specifically, we introduce features that are normalized using the orientation sensor such that horizontal and vertical movements are explicitly computed. We evaluate a neural network classifier in experiments in the wild with multiple users and hardware, we achieve accuracies above 90% for a single user and phone, and above 65% for multiple users, which is higher that similar works on the same set of activities, demonstrating the potential of our approach.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10