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Authors: Sebastian Baumbach 1 ; Arun Bhatt 2 ; Sheraz Ahmed 3 and Andreas Dengel 1

Affiliations: 1 German Research Center for Artificial Intelligence (DFKI), University of Kaiserslautern and Germany, Germany ; 2 University of Kaiserslautern and Germany, Germany ; 3 German Research Center for Artificial Intelligence (DFKI), Germany

ISBN: 978-989-758-275-2

Keyword(s): Human Activity Recognition, Sport Activities, Machine Learning, Deep Learning, LSTM.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods

Abstract: Human activity recognition has emerged as an active research area in recent years. With the advancement in mobile and wearable devices, various sensors are ubiquitous and widely available gathering data a broad spectrum of peoples’ daily life activities. Research studies thoroughly assessed lifestyle activities and are increasingly concentrated on a variety of sport exercises. In this paper, we examine nine sport and fitness exercises commonly conducted with sport equipments in gym, such as abdominal exercise and lat pull. We collected sensor data of 23 participants for these activities, for which smartphones and smartwatches were used. Traditional machine learning and deep learning algorithms were applied in these experiments in order to assess their performance on our dataset. Linear SVM and Naive Bayes with Gaussian kernel performs best with an accuracy of 80 %, whereas deep learning models outperform these machine learning techniques with an accuracy of 92 %.

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Paper citation in several formats:
Baumbach S., Bhatt A., Ahmed S. and Dengel A. (2018). Towards a Digital Personal Trainer for Health Clubs - Sport Exercise Recognition Using Personalized Models and Deep Learning.In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-275-2, pages 438-445. DOI: 10.5220/0006590504380445

@conference{icaart18,
author={Sebastian Baumbach and Arun Bhatt and Sheraz Ahmed and Andreas Dengel},
title={Towards a Digital Personal Trainer for Health Clubs - Sport Exercise Recognition Using Personalized Models and Deep Learning},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2018},
pages={438-445},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006590504380445},
isbn={978-989-758-275-2},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Towards a Digital Personal Trainer for Health Clubs - Sport Exercise Recognition Using Personalized Models and Deep Learning
SN - 978-989-758-275-2
AU - Baumbach S.
AU - Bhatt A.
AU - Ahmed S.
AU - Dengel A.
PY - 2018
SP - 438
EP - 445
DO - 10.5220/0006590504380445

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