Bayesian recognition of motion related activities with inertial sensors

Authors:
Korbinian Frank;Maria Josefa Vera Nadales;Patrick Robertson;Tom Pfeifer
Affiliations:
German Aerospace Center (DLR), Oberpfaffenhofen, Germany;Universidad de Málaga, Malaga, Spain;German Aerospace Center (DLR), Oberpfaffenhofen, Germany;Waterford Institute of Technology (WIT), Waterford, Ireland
Venue:
Proceedings of the 12th ACM international conference adjunct papers on Ubiquitous computing - Adjunct
Year:
2010

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Abstract

This work presents the design and evaluation of an activity recognition system for seven important motion related activities. The only sensor used is an Inertial Measurement Unit (IMU) worn on the belt. For classification, we applied Bayesian techniques, based on relevant features of the IMU raw data which are calculated in real time. Based on a complete labelled data set, i.e. supervised by an observing human judge, a K2 learning algorithm by Cooper and Herskovits was used to construct the Bayesian Network (BN) of the features. Our comparison of dynamic and static inference algorithms, based on the evaluation of the labelled data sets recorded from 16 male and female subjects show that a Hidden Markov Model (HMM) based on a learnt BN provides the best results.