Features selection for human activity recognition with iPhone inertial sensors

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

Authors

Nuno Cruz Silva (Author) FEUP; João Mendes Moreira (Author) FEUP; Paulo Menezes (Author) Other

Conference proceedings International

Title: Portuguese Conference on Artificial Intelligence

Pages: 560-570

EPIA 2013

Angra do Heroísmo, Açores, Portugal, 9 a 12 de Setembro de 2013

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

Other information

Abstract (EN): The recognition of human activities through sensors embedded in smart-phone devices, such as iPhone, is attracting researchers due to its relevance. The advances of this kind of technology are making possible the widespread and pervasiveness of sensing technology to take advantage of multiple sources of sensing to enrich users experience or to achieve proactive, context-aware applications and services. Human activity recognition and monitoring involves a continuing analysis of large amounts of data so, any increase or decrease in accuracy results in a wide variation in the number of activities correctly classied and incorrectly classied, so it is very important to increase the rate of correct classication. We have researched on a vector with 159 different features and on the vector subsets in order to improve the human activities recognition. We extracted features from the Magnitude of the Signal, the raw signal data, the vertical acceleration, the Horizontal acceleration, and the ltered Raw data. In the evaluation process we used the classiers: Naive Bayes, K-Nearest Neighbor and Random Forest. The features were extracted using the java programming language and the evaluation was done with WEKA. The maximum accuracy was obtained, as expected, with Random Forest using all the 159 features. The best subset found has twelve features: the Pearson correlation between vertical acceleration and horizontal acceleration, the Pearson correlation between x and y, the Pearson correlation between x and z, the STD of acceleration z, the STD of digital compass y, the STD of digital compass z, the STD of digital compass x, the mean between axis, the energy of digital compass x, the mean of acceleration x, the mean of acceleration z, the median of acceleration z.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11