Authors:
Ana Luísa Gomes
1
;
Vítor Paixão
2
and
Hugo Gamboa
3
Affiliations:
1
Universidade Nova de Lisboa, Portugal
;
2
Champalimaud Centre for the Unknown, Portugal
;
3
Universidade Nova de Lisboa and PLUX - Wireless Biosignals, Portugal
Keyword(s):
Human Activity Recognition, Forward Feature Selection, Hidden Markov Models, Clustering.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Biometrics
;
Biometrics and Pattern Recognition
;
Data Manipulation
;
Devices
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Multimedia
;
Multimedia Signal Processing
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
;
Telecommunications
;
Time and Frequency Response
;
Time-Frequency Analysis
;
Wearable Sensors and Systems
Abstract:
The Human Activity Recognition (HAR) systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according to the performed activities. In this work, a framework for human activity recognition in accelerometry (ACC) based on our previous work and with new features and techniques was developed. The new features set covered wavelets,
the CUIDADO features implementation and the Log Scale Power Bandwidth creation. The Hidden Markov Models were also applied to the clustering output. The Forward Feature Selection chose the most suitable set from a 423th dimensional feature vector in order to improve the clustering performances and limit the computational demands. K-means, Affinity Propagation, DBSCAN and Ward were applied
to ACC databases and showed promising results in activity recognition: from 73.20% 7.98% to 89.05% +/- 7.43% and from 70.75% +/- 10.09% to 83.89% +/- 13.65% with the Hungarian accuracy (HA) for the FCHA and
PAMAP databases, respectively. The Adjust Rand Index (ARI) was also applied as clustering evaluation method. The developed algorithm constitutes a contribution for the
development of reliable evaluation methods of movement disorders for diagnosis and treatment applications.
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