Smart phone based data mining for human activity recognition

Girija Chetty, Matthew White, Farnaz Akther

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

81 Citations (Scopus)


Automatic activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors, and permit continuous monitoring of numerous physiological signals, where these sensors are attached to the subject's body. This can be immensely useful in healthcare applications, for automatic and intelligent daily activity monitoring for elderly people. In this paper, we present novel data analytic scheme for intelligent Human Activity Recognition (AR) using smartphone inertial sensors based on information theory based feature ranking algorithm and classifiers based on random forests, ensemble learning and lazy learning. Extensive experiments with a publicly available database1 of human activity with smart phone inertial sensors show that the proposed approach can indeed lead to development of intelligent and automatic real time human activity monitoring for eHealth application scenarios for elderly, disabled and people with special needs.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Information and Communication Technologies, ICICT 2014
EditorsPhilip Samuel
Number of pages7
Publication statusPublished - 2015
EventInternational Conference on Information and Communication Technologies: ICICT 2014 - Kochi, Kochi, India
Duration: 3 Dec 20145 Dec 2014

Publication series

NameProcedia Computer Science
PublisherElsevier BV
ISSN (Print)1877-0509


ConferenceInternational Conference on Information and Communication Technologies


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