Document details

Human activity recognition with accelerometry: novel time and frequency features

Author(s): Gomes, Ana Luísa Gonçalves Neves

Date: 2014

Persistent ID:

Origin: Repositório Institucional da UNL

Subject(s): Human activity recognition; Forward feature selection; Log Scale power bandwidth; Wavelets; Hidden Markov Models; Clustering algorithms


Human Activity Recognition systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according with the performed activities. These systems are under development and offer objective and personalized support for several applications such as the healthcare area. This thesis aims to create a framework for human activities recognition based on accelerometry signals. Some new features and techniques inspired in the audio recognition methodology are introduced in this work, namely Log Scale Power Bandwidth and the Markov Models application. The Forward Feature Selection was adopted as the feature selection algorithm in order to improve the clustering performances and limit the computational demands. This method selects the most suitable set of features for activities recognition in accelerometry from a 423th dimensional feature vector. Several Machine Learning algorithms were applied to the used accelerometry databases – FCHA and PAMAP databases - and these showed promising results in activities recognition. The developed algorithm set constitutes a mighty contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications.

Document Type Master thesis
Language English
Advisor(s) Gamboa, Hugo; Paixão, Vítor
Contributor(s) Gomes, Ana Luísa Gonçalves Neves
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