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

• Main Authors: Sebastian Scheurer, Salvatore Tedesco, Brendan O’Flynn, Kenneth N. Brown
• Format: Article
• Language: English
• Published: MDPI AG 2020-06-01
• Series: Sensors
• Subjects: human activity recognition; machine learning; ensemble methods; boosting; bagging; inertial sensors
• Online Access: https://www.mdpi.com/1424-8220/20/13/3647

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, <inline-formula> <math display="inline"> <semantics> <mi>κ</mi> </semantics> </math> </inline-formula>-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 <inline-formula> <math display="inline"> <semantics> <mi>κ</mi> </semantics> </math> </inline-formula>-weighted EPSMs—the best-performing EPSM type—by 16.4% in terms of the subject-independent performance.