Inertial Sensor Based Modelling of Human Activity Classes: Feature Extraction and Multi-sensor Data Fusion Using Machine Learning Algorithms : WestminsterResearch

Publication dates
Chapter title	Inertial Sensor Based Modelling of Human Activity Classes: Feature Extraction and Multi-sensor Data Fusion Using Machine Learning Algorithms
Authors	Zebin, T., Scully, PJ and Ozanyan, K B.
Abstract	Wearable inertial sensors are currently receiving pronounced interest due to applications in unconstrained daily life settings, ambulatory monitoring and pervasive computing systems. This research focuses on human activity recognition problem, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are automatically classified human activities. A general-purpose framework has been presented for designing and evaluating activity recognition system with six different activities using machine learning algorithms such as support vector machine (SVM) and artificial neural networks (ANN). Several feature selection methods were explored to make the recognition process faster by experimenting on the features extracted from the accelerometer and gyroscope time series data collected from a number of volunteers. In addition, a detailed discussion is presented to explore how different design parameters, for example, the number of features and data fusion from multiple sensor locations - impact on overall recognition performance.
Book title	eHealth 360°. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Page range	306-314
Year	2017
Publisher	Springer
Published	22 Dec 2017
Published online	01 Dec 2016
Series	Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
ISBN	9783319496542
	9783319496559
ISSN	1867-8211
Digital Object Identifier (DOI)	https://doi.org/10.1007/978-3-319-49655-9_38
File	AS4405975940218891482058060256_content_1.pdf
Web address (URL)	https://link.springer.com/chapter/10.1007/978-3-319-49655-9_38

Related outputs

Design and implementation of a deep recurrent model for prediction of readmission in urgent care using electronic health records
Zebin, T. and Chaussalet, T.J. 2019. Design and implementation of a deep recurrent model for prediction of readmission in urgent care using electronic health records. 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology. Certosa di Pontignano, Siena - Tuscany, Italy 09 - 11 Jul 2019 IEEE . https://doi.org/10.1109/CIBCB.2019.8791466

A deep learning approach for length of stay prediction in clinical settings from medical records
Zebin, T., Rezvy, S. and Chaussalet, T.J. 2019. A deep learning approach for length of stay prediction in clinical settings from medical records. 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology. Certosa di Pontignano, Siena - Tuscany, Italy 09 - 11 Jul 2019 IEEE . https://doi.org/10.1109/CIBCB.2019.8791477

Intrusion Detection and Classification with Autoencoded Deep Neural Network
Rezvy, S, Petridis, M, Lasebae, A and Zebin, T. 2019. Intrusion Detection and Classification with Autoencoded Deep Neural Network. in: Innovative Security Solutions for Information Technology and Communications Springer. pp. 142-156

Human activity recognition from inertial sensor time-series using batch normalized deep LSTM recurrent networks
Zebin, T., Sperrin, M., Peek, N. and Casson, A.J. 2018. Human activity recognition from inertial sensor time-series using batch normalized deep LSTM recurrent networks. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Honolulu, HI, USA 18 - 21 Jul 2018 IEEE . https://doi.org/10.1109/EMBC.2018.8513115

Human activity recognition with inertial sensors using a deep learning approach
Zebin, T., Scully, P.J. and Ozanyan, K.B. 2016. Human activity recognition with inertial sensors using a deep learning approach. 2016 IEEE SENSORS. Orlando, FL, USA 30 Oct - 03 Nov 2016 IEEE . https://doi.org/10.1109/ICSENS.2016.7808590

Permalink - https://westminsterresearch.westminster.ac.uk/item/q9x25/inertial-sensor-based-modelling-of-human-activity-classes-feature-extraction-and-multi-sensor-data-fusion-using-machine-learning-algorithms

Inertial Sensor Based Modelling of Human Activity Classes: Feature Extraction and Multi-sensor Data Fusion Using Machine Learning Algorithms

Related outputs

Share this

Usage statistics

Export as