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

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...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 20; H. 13; S. 3647
Hauptverfasser: Scheurer, Sebastian, Tedesco, Salvatore, O’Flynn, Brendan, Brown, Kenneth N.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI 29.06.2020
MDPI AG
Schlagworte:
Algorithms
bagging
boosting
ensemble methods
Human Activities
human activity recognition
Humans
inertial sensors
Machine Learning
inertial sensors
boosting
machine learning
human activity recognition
bagging
ensemble methods
ISSN:1424-8220, 1424-8220
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Zusammenfassung: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.
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This paper is an extended version of our paper published in Scheurer, S.; Tedesco, S.; Brown, K.N.; O’Flynn, B. Subject-dependent and-independent human activity recognition with person-specific and-independent models. In Proceedings of the 6th international Workshop on Sensor-based Activity Recognition and Interaction, Rostock, Germany, 16–17 September 2019; pp. 1–7.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20133647