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ABSTRACT
Abstract English
Title of master’s thesis
Human activity pattern recognition from accelerometry data
Author
Dipl.-Ing. (FH) Dennis Jos
First examiner
Prof. Dr. Oliver Kalthoff
Second examiner
Dr. rer. nat. Uwe Mittag
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 activities 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 magnitude 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.
Key words
Activity recognition, accelerometer, artificial neural networks, ambulatory monitoring,
supervised learning
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