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Recognition of Human Motion Related Activities from Sensors

Vera Nadales, Maria Josefa (2010) Recognition of Human Motion Related Activities from Sensors. Master's, University of Malaga.

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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 Bayesian techniques, based on relevant features of the IMU raw data which are calculated in real time are applied. 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 and compared to a Naive Bayes approach. The 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.

Document Type:Thesis (Master's)
Title:Recognition of Human Motion Related Activities from Sensors
Authors:
AuthorsInstitution or Email of Authors
Vera Nadales, Maria JosefaUniversity of Malaga
Date:2010
Number of Pages:185
Status:Published
Keywords:context inference, activity recognition, human movement, accelerometers, gyroscopes, magnetometers, inertial measurement unit, digital signal processing, Bayesian networks, Bayesian filters, grid based filter, classification, probabilistic inference
Institution:University of Malaga
Department:Escuela Técnica Superior de Ingeniería de Telecomunicación
HGF - Research field:Aeronautics, Space and Transport (old)
HGF - Program:Space (old)
HGF - Program Themes:W KN - Kommunikation/Navigation
DLR - Research area:Space
DLR - Program:W KN - Kommunikation/Navigation
DLR - Research theme (Project):W - Vorhaben GNSS2/Neue Dienste und Produkte (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Communication and Navigation > Communications Systems
Deposited By: Korbinian Frank
Deposited On:05 Oct 2010 08:42
Last Modified:12 Dec 2013 21:02

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