Comparing person-specific and independent models on subject-dependent and independent human activity recognition performance

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Scheurer, Sebastian
Tedesco, Salvatore
O'Flynn, Brendan
Brown, Kenneth N.
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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.
Human activity recognition , Machine learning , Ensemble methods , Boosting; bagging , Inertial sensors
Scheurer, S., Tedesco, S., O’Flynn, B. and Brown, K. N. (2020) 'Comparing Person-Specific and Independent Models on Subject-Dependent and Independent Human Activity Recognition Performance', Sensors, 20(13), 3647 (27 pp). doi: 10.3390/s20133647
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