Recognizing Human Behaviour from Temporal Sequential Data with Activity Assignment

Sarah Fallmann; Johannes Kropf

eHealth 360°. International Summit on eHealth, Budapest, Hungary, June 14-16, 2016, Revised Selected Papers

Research Article

Recognizing Human Behaviour from Temporal Sequential Data with Activity Assignment

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@INPROCEEDINGS{10.1007/978-3-319-49655-9_50,
    author={Sarah Fallmann and Johannes Kropf},
    title={Recognizing Human Behaviour from Temporal Sequential Data with Activity Assignment},
    proceedings={eHealth 360°. International Summit on eHealth, Budapest, Hungary, June 14-16, 2016, Revised Selected Papers},
    proceedings_a={EHEALTH360},
    year={2017},
    month={1},
    keywords={Behaviour recognition Fuzzy Logic T-Pattern algorithm},
    doi={10.1007/978-3-319-49655-9_50}
}

Sarah Fallmann
Johannes Kropf
Year: 2017
Recognizing Human Behaviour from Temporal Sequential Data with Activity Assignment
EHEALTH360
Springer
DOI: 10.1007/978-3-319-49655-9_50

Sarah Fallmann¹^,*, Johannes Kropf¹^,*

1: AIT - Austrian Institute of Technology

*Contact email: sarah.fallman.fl@ait.ac.at, johannes.kropf@ait.ac.at

Abstract

A probabilistic system in an ambient assisted living environment is automatically built to detect human behaviour. The focus lies on the early prediction of human activities based on domotic sensor data and on general activity assignment. First recurrent patterns are detected using the Temporal-Pattern (T-Pattern) algorithm and further a probabilistic finite-state automaton is generated out of the patterns. Afterwards the patterns are assigned to specific defined human activities with the help of Fuzzy Logic. The needed rules are learned automatic from an annotated dataset.

Keywords: Behaviour recognition Fuzzy Logic T-Pattern algorithm

Published: 2017-01-05
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-319-49655-9_50