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Comparing Person-Specific and Independent Models on Subject-Dependent and Independent Human Activity Recognition Performance

Authors: 
Publication Type: 
Refereed Original Article
Abstract: 
The distinction between subject-dependent and subject-independent performance is ubiquitous in the human activity recognition (HAR) literature. We assess whether HAR models really do achieve better subject-dependent performance than subject-independent performance, whether a model trained with data from many users achieves better subject-independent performance than one trained with data from a single person, and whether one trained with data from a single specific target user performs better for that user than one trained with data from many. To those ends, we compare four popular machine learning algorithms’ subject-dependent and subject-independent performances across eight datasets using three different personalisation–generalisation approaches, which we term person-independent models (PIMs), person-specific models (PSMs), and ensembles of PSMs (EPSMs). We further consider three different ways to construct such an ensemble: unweighted, κ -weighted, and baseline-feature-weighted. Our analysis shows that PSMs outperform PIMs by 43.5% in terms of their subject-dependent performances, whereas PIMs outperform PSMs by 55.9% and κ -weighted EPSMs—the best-performing EPSM type—by 16.4% in terms of the subject-independent performance.
Digital Object Identifer (DOI): 
10.3390/s20133647
ISSN: 
1424-8220
Publication Status: 
Published
Date Accepted for Publication: 
Wednesday, 24 June, 2020
Publication Date: 
29/06/2020
Journal: 
Sensors
Volume: 
20
Issue: 
13
Pages: 
3647
Institution: 
National University of Ireland, Cork (UCC)
Tyndall National Institute
Open access repository: 
Yes
Publication document: