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Authors: Andrea Mannini and Angelo Maria Sabatini

Affiliation: Arts Lab and Scuola Superiore Sant’Anna, Italy

Keyword(s): Human activity classification, Statistical pattern recognition, Accelerometers, Hidden Markov Models, Human robot interaction, Machine learning.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Detection and Identification ; Devices ; Fuzzy Systems and Signals ; Health Engineering and Technology Applications ; Health Information Systems ; Human-Computer Interaction ; Informatics in Control, Automation and Robotics ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing, Sensors, Systems Modeling and Control ; Soft Computing ; Time and Frequency Response ; Time-Frequency Analysis ; Wearable Sensors and Systems

Abstract: Several applications demanding the development of small networks of on-body sensors, such as motion sensors, are currently investigated. Accelerometers are a popular choice as motion sensors: the reason is partly in their capability of extracting information that can be used to automatically infer the physical activity the human subject is involved, beside their role in feeding estimators of biomechanical parameters. Automatic classification of human physical activities is highly attractive for pervasive computing systems, whereas contextual awareness may ease the human-machine interaction, and in biomedicine, whereas wearable sensor systems are proposed for long-term monitoring of physiological and biomechanical parameters. This paper is concerned with the machine learning algorithms needed to perform the classification task. Hidden Markov Model (HMM) classifiers are studied by contrasting them with Gaussian Mixture Model (GMM) classifiers. HMMs incorporate the statistical informati on available on movement dynamics into the classification process, without discarding the time history of previous outcomes, as GMMs do. In this work, rather than considering them as models for single motor activities, we apply HMMs as models suitable for sequences of chained activities. An example of the benefits of the statistical leverage by HMMs is illustrated and discussed by analyzing a dataset of accelerometer time series. (More)

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Paper citation in several formats:
Mannini, A. and Sabatini, A. (2011). CLASSIFICATION OF HUMAN PHYSICAL ACTIVITIES FROM ON-BODY ACCELEROMETERS - A Markov Modeling Approach. In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSTEC 2011) - BIOSIGNALS; ISBN 978-989-8425-35-5; ISSN 2184-4305, SciTePress, pages 201-208. DOI: 10.5220/0003151102010208

@conference{biosignals11,
author={Andrea Mannini. and Angelo Maria Sabatini.},
title={CLASSIFICATION OF HUMAN PHYSICAL ACTIVITIES FROM ON-BODY ACCELEROMETERS - A Markov Modeling Approach},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSTEC 2011) - BIOSIGNALS},
year={2011},
pages={201-208},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003151102010208},
isbn={978-989-8425-35-5},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSTEC 2011) - BIOSIGNALS
TI - CLASSIFICATION OF HUMAN PHYSICAL ACTIVITIES FROM ON-BODY ACCELEROMETERS - A Markov Modeling Approach
SN - 978-989-8425-35-5
IS - 2184-4305
AU - Mannini, A.
AU - Sabatini, A.
PY - 2011
SP - 201
EP - 208
DO - 10.5220/0003151102010208
PB - SciTePress