University of Sussex

File(s) under permanent embargo

Deep convolutional feature transfer across mobile activity recognition domains, sensor modalities and locations

posted on 2023-06-09, 01:56 authored by Francisco Javier Ordonez Morales, Daniel RoggenDaniel Roggen
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.


Is deep learning useful for wearable activity recognition?; G1460; GOOGLE


Publication status

  • Published

File Version

  • Accepted version



Page range


Presentation Type

  • paper

Event name

20th International Symposiumon Wearable Computers (ISWC) 2016

Event location

Heidelberg, Germany

Event type


Event date

12-16 September 2016

Book title

Proceedings of International Symposium on Wearable Computers

Department affiliated with

  • Engineering and Design Publications

Full text available

  • No

Peer reviewed?

  • Yes


Kai Kunze, Ulf Blanke

Legacy Posted Date


First Compliant Deposit (FCD) Date


Usage metrics

    University of Sussex (Publications)


    No categories selected