Personalizing Activity Recognition With a Clustering Based Semi-Population Approach

Federico Cruciani, CD Nugent, Javier Medina Quero, I Cleland, P McCullagh, Kåre Synnes, Josef Hallberg

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
102 Downloads (Pure)


Smartphone-based approaches for Human Activity Recognition have become prevalent in recent years. Despite the amount of research undertaken in the field, issues such as cross-subject variability are still posing an obstacle to the deployment of solutions in large scale, free-living settings. Personalized methods (i.e. aiming to adapt a generic classifier to a specific target user) attempt to solve this problem. The lack of labeled data for training purposes, however, represents a major barrier. This is especially the case when taking into consideration that personalization generally requires labeled data to be user specific. This paper presents a novel personalization method combining a semi-population based approach with user adaptation. Personalization is achieved through the following. Firstly, the proposed method identifies a subset of users from the available population as best candidates for initializing the classifier to the target user. Subsequently, a semi-population Neural Network classifier is trained using data from this subset of users. The classifier’s network weights are then updated using a small amount of labeled data from the target user subsequently implementing personalization. This approach was validated on a large publicly available dataset collected in a free-living scenario. The personalized approach using the proposed method has shown to improve the overall F-score to 74.4% compared to 70.9% when using a generic non personalized approach. Results obtained, with statistical significance being confirmed on a set of 57 users, indicate that model initialization using the semi-population approach can reduce the amount of labeled data required for personalization. As such, the proposed method for model initialization could facilitate the real world deployment of systems implementing personalization by reducing the amount of data needed for personalization.
Original languageEnglish
Article number9258905
Pages (from-to) 207794-207804
Number of pages11
JournalIEEE Access
Early online date16 Nov 2020
Publication statusPublished (in print/issue) - 30 Nov 2020

Bibliographical note

Funding Information:
This research has received funding under the ACROSSING project Marie Sklodowska-Curie EU Framework for Research and Innovation Horizon 2020, under Grant Agreement No. 676157.

Publisher Copyright:
© 2013 IEEE.

Copyright 2020 Elsevier B.V., All rights reserved.


  • Free-living
  • human activity recognition
  • neural networks
  • personalized machine learning
  • smartphones


Dive into the research topics of 'Personalizing Activity Recognition With a Clustering Based Semi-Population Approach'. Together they form a unique fingerprint.

Cite this