Ordonez Morales, Francisco Javier and Roggen, Daniel (2016) Deep convolutional feature transfer across mobile activity recognition domains, sensor modalities and locations. In: 20th International Symposiumon Wearable Computers (ISWC) 2016, 12-16 September 2016, Heidelberg, Germany.

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Abstract

Deep convolutional neural networks are powerful image and signal classifiers. One hypothesis is that kernels in the convolutional layers act as feature extractors, progressively highlighting more domain-specific features in upper layers of the network. Thus lower-level features might be suitable for transfer. We analyse this in wearable activity recognition by reusing kernels learned on a source domain on another target domain. We consider transfer between users, application domains, sensor modalities and sensor locations. We characterise the trade-offs of transferring various convolutional layers along model size, learning speed, recognition performance and training data. Through a novel kernel visualisation technique and comparative evaluations we identify what learned kernels are predominantly sensitive to, amongst sensor characteristics, motion dynamics and on-body placement. We demonstrate a ~17% decrease in training time at equal performance thanks to kernel transfer and we derive recommendations on when transfer is most suitable.

Item Type:	Conference or Workshop Item (Paper)
Schools and Departments:	School of Engineering and Informatics > Engineering and Design
Subjects:	Q Science > QA Mathematics > QA0276 Mathematical statistics Q Science > QA Mathematics > QA0299 Analysis. Including analytical methods connected with physical problems Q Science > QA Mathematics > QA0075 Electronic computers. Computer science T Technology > T Technology (General)
Related URLs:	Organisation
Depositing User:	Daniel Roggen
Date Deposited:	29 Jun 2016 11:57
Last Modified:	17 Nov 2016 11:51
URI:	http://sro.sussex.ac.uk/id/eprint/61768

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