TY - JOUR
T1 - Feature extraction and feature selection in smartphone-based activity recognition
AU - Dehkordi, Maryam Banitalebi
AU - Zaraki, Abolfazl
AU - Setchi, Rossitza
N1 - Funding Information:
The authors would like to acknowledge the support of the Centre for Artificial Intelligence, Robotics and Human-Machine Systems (IROHMS), operation C82092, part-funded by the European Regional Development Fund (ERDF) through the Welsh Government. In addition, the authors would like to thank Prof. Eckehard Steinbach from Technical University of Munich and Dr. Georg Schroth from NavVis Gmb. for their support during the collaborative research, and the participants of the data logging experiments.
Publisher Copyright:
© 2020 The Authors. Published by Elsevier B.V.
PY - 2020
Y1 - 2020
N2 - Nowadays, smartphones are gradually being integrated in our daily lives, and they can be considered powerful tools for monitoring human activities. However, due to the limitations of processing capability and energy consumption of smartphones compared to standard machines, a trade-off between performance and computational complexity must be considered when developing smartphone-based systems. In this paper, we shed light on the importance of feature selection and its impact on simplifying the activity classification process which enhances the computational complexity of the system. Through an in-depth survey on the features that are widely used in state-of-the-art studies, we selected the most common features for sensor-based activity classification, namely conventional features. Then, in an experimental study with 10 participants and using 2 different smartphones, we investigated how to reduce system complexity while maintaining classification performance by replacing the conventional feature set with an optimal set. For this reason, in the considered scenario, the users were instructed to perform different static and dynamic activities, while freely holding a smartphone in their hands. In our comparison to the state-of-the-art approaches, we implemented and evaluated major classification algorithms, including the decision tree and Bayesian network. We demonstrated that replacing the conventional feature set with an optimal set can significantly reduce the complexity of the activity recognition system with only a negligible impact on the overall system performance.
AB - Nowadays, smartphones are gradually being integrated in our daily lives, and they can be considered powerful tools for monitoring human activities. However, due to the limitations of processing capability and energy consumption of smartphones compared to standard machines, a trade-off between performance and computational complexity must be considered when developing smartphone-based systems. In this paper, we shed light on the importance of feature selection and its impact on simplifying the activity classification process which enhances the computational complexity of the system. Through an in-depth survey on the features that are widely used in state-of-the-art studies, we selected the most common features for sensor-based activity classification, namely conventional features. Then, in an experimental study with 10 participants and using 2 different smartphones, we investigated how to reduce system complexity while maintaining classification performance by replacing the conventional feature set with an optimal set. For this reason, in the considered scenario, the users were instructed to perform different static and dynamic activities, while freely holding a smartphone in their hands. In our comparison to the state-of-the-art approaches, we implemented and evaluated major classification algorithms, including the decision tree and Bayesian network. We demonstrated that replacing the conventional feature set with an optimal set can significantly reduce the complexity of the activity recognition system with only a negligible impact on the overall system performance.
KW - Activity recognition
KW - Feature extraction
KW - Feature selection
KW - Inertial sensor
UR - http://www.scopus.com/inward/record.url?scp=85093360679&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2020.09.301
DO - 10.1016/j.procs.2020.09.301
M3 - Conference article
AN - SCOPUS:85093360679
SN - 1877-0509
VL - 176
SP - 2655
EP - 2664
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020
Y2 - 16 September 2020 through 18 September 2020
ER -