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Authors: Constantin Patsch ; Marsil Zakour and Rahul Chaudhari

Affiliation: Human Activity Understanding Group, Chair of Media Technology (LMT), Technical University Munich (TUM), Arcisstr. 21, Munich, Germany

Keyword(s): Activity and Plan Recognition, Knowledge Representation and Reasoning, Machine Learning.

Abstract: Developing intelligent assistants for activities of daily living (ADL) is an important topic in eldercare due to the aging society in industrialized countries. Recognizing activities and understanding the human’s intended goal are the major challenges associated with such a system. We propose a hybrid model for composite activity recognition in a household environment by combining Machine Learning and knowledge-based models. The Machine Learning part, based on structural Recurrent Neural Networks (S-RNN), performs low-level activity recognition based on video data. The knowledge-based part, based on our extended Activation Spreading Network architecture, models and recognizes the contextual meaning of an activity within a plan structure. This model is able to recognize activities, underlying goals and sub-goals, and is able to predict subsequent activities. Evaluating our action S-RNN on data from the 3D activity simulator HOIsim yields a macro average F1 score of 0.97 and an accurac y of 0.99. The hybrid model is evaluated with activation value graphs. (More)

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Paper citation in several formats:
Patsch, C.; Zakour, M. and Chaudhari, R. (2022). Automatic Recognition of Human Activities Combining Model-based AI and Machine Learning. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 15-22. DOI: 10.5220/0010747000003116

@conference{icaart22,
author={Constantin Patsch. and Marsil Zakour. and Rahul Chaudhari.},
title={Automatic Recognition of Human Activities Combining Model-based AI and Machine Learning},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2022},
pages={15-22},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010747000003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Automatic Recognition of Human Activities Combining Model-based AI and Machine Learning
SN - 978-989-758-547-0
IS - 2184-433X
AU - Patsch, C.
AU - Zakour, M.
AU - Chaudhari, R.
PY - 2022
SP - 15
EP - 22
DO - 10.5220/0010747000003116
PB - SciTePress

- Science and Technology Publications, Lda.
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