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Human Activity Pattern Recognition from Accelerometry Data

Jos, Dennis (2013) Human Activity Pattern Recognition from Accelerometry Data. Master's, RUPRECHT-KARLS-UNIVERSITÄT HEIDELBERG.

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Abstract

Ambulant studies are dependent on the behavior and compliance of subjects in their home environment. Especially during interventions on the musculoskeletal system, monitoring physical activity is essential, even for research on nutritional, metabolic, or neuromuscular issues. To support an ambulant study at the German Aerospace Center (DLR), a pattern recognition system for human activity was developed. Everyday activi-ties of static (standing, sitting, lying) and dynamic nature (walking, ascending stairs, descending stairs, jogging) were under consideration. Two tri-axial accelerometers were attached to the hip and parallel to the tibia. Pattern characterizing features from the time domain (mean, standard deviation, absolute maximum) and the frequency domain (main frequencies, spectral entropy, autoregressive coefficients, signal magni-tude area) were extracted. Artificial neural networks (ANN) with a feedforward topology were trained with backpropagation as supervised learning algorithm. An evaluation of the resulting classifier was conducted with 14 subjects completing an activity protocol and a free chosen course of activities. An individual ANN was trained for each subject. Accuracies of 87,99 % and 71,23 % were approached in classifying the activity protocol and the free run, respectively. Reliabilities of 96,49 % and 76,77 % were measured. These performance parameters represent a working ambulant physical activity monitoring system.

Item URL in elib:https://elib.dlr.de/95630/
Document Type:Thesis (Master's)
Title:Human Activity Pattern Recognition from Accelerometry Data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Jos, DennisUNSPECIFIEDUNSPECIFIED
Date:November 2013
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:106
Status:Unpublished
Keywords:Activity recognition, accelerometer, artificial neural networks, ambulatory monitoring, supervised learning
Institution:RUPRECHT-KARLS-UNIVERSITÄT HEIDELBERG
Department:Medizinische Informatik
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Research under Space Conditions
DLR - Research area:Raumfahrt
DLR - Program:R FR - Forschung unter Weltraumbedingungen
DLR - Research theme (Project):R - Vorhaben Integrative Studien (old)
Location: Köln-Porz
Institutes and Institutions:Institute of Aerospace Medicine > Space Physiology
Deposited By: Becker, Christine
Deposited On:17 Mar 2015 09:11
Last Modified:31 Jul 2019 19:52

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