Next Article in Journal
Re-Visiting Acoustic Sounding to Advance the Measurement of Optical Turbulence
Next Article in Special Issue
Human Activity Recognition Using CSI Information with Nexmon
Previous Article in Journal
A Hybrid DenseNet-LSTM Model for Epileptic Seizure Prediction
Previous Article in Special Issue
TickPhone App: A Smartphone Application for Rapid Tick Identification Using Deep Learning
Article

The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous Sensing

School of Computing, Ulster University, Jordanstown BT37 0QB, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Sławomir K. Zieliński
Appl. Sci. 2021, 11(16), 7660; https://doi.org/10.3390/app11167660
Received: 27 June 2021 / Revised: 23 July 2021 / Accepted: 25 July 2021 / Published: 20 August 2021
(This article belongs to the Special Issue Sensor-Based Human Activity Recognition in Real-World Scenarios)
Transfer learning is a growing field that can address the variability of activity recognition problems by reusing the knowledge from previous experiences to recognise activities from different conditions, resulting in the leveraging of resources such as training and labelling efforts. Although integrating ubiquitous sensing technology and transfer learning seem promising, there are some research opportunities that, if addressed, could accelerate the development of activity recognition. This paper presents TL-FmRADLs; a framework that converges the feature fusion strategy with a teacher/learner approach over the active learning technique to automatise the self-training process of the learner models. Evaluation TL-FmRADLs is conducted over InSync; an open access dataset introduced for the first time in this paper. Results show promising effects towards mitigating the insufficiency of labelled data available by enabling the learner model to outperform the teacher’s performance. View Full-Text
Keywords: transfer learning; teacher/learner; activity recognition; machine-learning transfer learning; teacher/learner; activity recognition; machine-learning
Show Figures

Figure 1

MDPI and ACS Style

Hernandez-Cruz, N.; Nugent, C.; Zhang, S.; McChesney, I. The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous Sensing. Appl. Sci. 2021, 11, 7660. https://doi.org/10.3390/app11167660

AMA Style

Hernandez-Cruz N, Nugent C, Zhang S, McChesney I. The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous Sensing. Applied Sciences. 2021; 11(16):7660. https://doi.org/10.3390/app11167660

Chicago/Turabian Style

Hernandez-Cruz, Netzahualcoyotl, Chris Nugent, Shuai Zhang, and Ian McChesney. 2021. "The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous Sensing" Applied Sciences 11, no. 16: 7660. https://doi.org/10.3390/app11167660

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop