Codebook-based electrooculography data analysis towards cognitive activity recognition

P. Lagodzinski; K. Shirahama; M. Grzegorzek

doi:10.1016/j.compbiomed.2017.10.026

Codebook-based electrooculography data analysis towards cognitive activity recognition

P. Lagodzinski^*, K. Shirahama, M. Grzegorzek

^*Corresponding author for this work

Institute of Medical Informatics

14 Citations (Scopus)

Abstract

With the advancement in mobile/wearable technology, people started to use a variety of sensing devices to track their daily activities as well as health and fitness conditions in order to improve the quality of life. This work addresses an idea of eye movement analysis, which due to the strong correlation with cognitive tasks can be successfully utilized in activity recognition. Eye movements are recorded using an electrooculographic (EOG) system built into the frames of glasses, which can be worn more unobtrusively and comfortably than other devices. Since the obtained information is low-level sensor data expressed as a sequence representing values in constant intervals (100 Hz), the cognitive activity recognition problem is formulated as sequence classification. However, it is unclear what kind of features are useful for accurate cognitive activity recognition. Thus, a machine learning algorithm like a codebook approach is applied, which instead of focusing on feature engineering is using a distribution of characteristic subsequences (codewords) to describe sequences of recorded EOG data, where the codewords are obtained by clustering a large number of subsequences. Further, statistical analysis of the codeword distribution results in discovering features which are characteristic to a certain activity class. Experimental results demonstrate good accuracy of the codebook-based cognitive activity recognition reflecting the effective usage of the codewords.

Original language	English
Journal	Computers in Biology and Medicine
Volume	95
Pages (from-to)	277-287
Number of pages	11
ISSN	0010-4825
DOIs	https://doi.org/10.1016/j.compbiomed.2017.10.026
Publication status	Published - 01.04.2018

Funding

Research and development activities leading to this article have been supported by the German Federal Ministry of Education and Research within the project “Cognitive Village: Adaptively Learning Technical Support System for Elderly” (Grant Number: 16SV7223K ). Dr. Przemyslaw Lagodzinski is an Assistant Professor at the Department of Knowledge Engineering at the University of Economics in Katowice, Poland. He received the Diploma degree in Computer Science from the Silesian University of Technology, Gliwice, Poland in 2003 and the PhD degree in Computer Science from the Silesian University of Technology, Gliwice, Poland, in 2010. From 2002 to 2015, he was professionally related to the Silesian University of Technology, Gliwice, Poland, working as a Chief IT Specialist in the University Computer Center. He published several papers in image processing. His current research interests focus on pattern recognition, image processing, machine learning and sensor-based human activity recognition. He is a member of ACM SIGGRAPH. Dr. Kimiaki Shirahama received his B.E., M.E. and D.E. degrees in Engineering from Kobe University, Japan in 2003, 2005 and 2011, respectively. After working as an assistant professor in Muroran Institute of Technology, Japan, since 2013, he is working as a postdoctoral researcher at Pattern Recognition Group in University of Siegen, Germany. From 2013 to 2015, his research activity was supported by the Postdoctoral Fellowship of Japan Society for the Promotion of Science (JSPS), and is now supported within a project of German Federal Ministry of Education and Research (BMBF). His research interests include multimedia data processing, machine learning, data mining and sensor-based human activity recognition. He is a member of ACM SIGKDD, ACM SIGMM, Institute of Image Information and Television Engineers in Japan (ITE), Information Processing Society of Japan (IPSJ) and Institute of Electronics, Information and Communication Engineering in Japan (IEICE). Prof. Dr. Marcin Grzegorzek is Head of the Research Group for Pattern Recognition at the University of Siegen, Professor at the Department of Knowledge Engineering at the University of Economics in Katowice. He studied Computer Science at the Silesian University of Technology, did his PhD at the Pattern Recognition Lab at the University of Erlangen-Nuremberg, worked scientifically as Postdoc in the Multimedia and Vision Research Group at the Queen Mary University of London and at the Institute for Web Science and Technologies at the University of Koblenz-Landau, did his habilitation at the AGH University of Science and Technology in Kraków. He published more than 100 papers in pattern recognition, image processing, machine learning, and multimedia analysis. For the time being, he runs eight externally funded research projects. For instance, the project CogAge ( www.cognitive-village.de ) aiming at developing a user-friendly support system for elderly that applies machine learning algorithms for sensor-based health assessment.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 9 Industry, Innovation, and Infrastructure

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10.1016/j.compbiomed.2017.10.026

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title = "Codebook-based electrooculography data analysis towards cognitive activity recognition",

abstract = "With the advancement in mobile/wearable technology, people started to use a variety of sensing devices to track their daily activities as well as health and fitness conditions in order to improve the quality of life. This work addresses an idea of eye movement analysis, which due to the strong correlation with cognitive tasks can be successfully utilized in activity recognition. Eye movements are recorded using an electrooculographic (EOG) system built into the frames of glasses, which can be worn more unobtrusively and comfortably than other devices. Since the obtained information is low-level sensor data expressed as a sequence representing values in constant intervals (100 Hz), the cognitive activity recognition problem is formulated as sequence classification. However, it is unclear what kind of features are useful for accurate cognitive activity recognition. Thus, a machine learning algorithm like a codebook approach is applied, which instead of focusing on feature engineering is using a distribution of characteristic subsequences (codewords) to describe sequences of recorded EOG data, where the codewords are obtained by clustering a large number of subsequences. Further, statistical analysis of the codeword distribution results in discovering features which are characteristic to a certain activity class. Experimental results demonstrate good accuracy of the codebook-based cognitive activity recognition reflecting the effective usage of the codewords.",

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Codebook-based electrooculography data analysis towards cognitive activity recognition

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Funding

UN SDGs

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