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, $\kappa$ -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 $\kappa$ -weighted EPSMs—the best-performing EPSM type—by 16.4% in terms of the subject-independent performance.

中文翻译：

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比较特定于主体的模型和基于主体的和独立的人类活动识别性能的独立模型。

在人类活动识别（HAR）文献中普遍存在主体依赖型和个体依赖型之间的区别。我们评估了HAR模型是否确实比独立于对象的性能更好地实现了与主题无关的性能；是否使用多位用户的数据训练过的模型是否比使用单人数据进行训练的模型更好地实现了独立于对象的性能，以及是否使用数据进行了训练来自一个特定目标用户的数据对于该用户的效果要好于受过许多数据训练的用户。为此，我们使用三种不同的个性化—通用化方法，将四种流行的机器学习算法在八个数据集中的与主题相关和与主题无关的性能进行了比较，我们将其称为个人独立模型（PIM），个人特定模型（PSM），和PSM（EPSM）的合奏。我们进一步考虑了构建这种合奏的三种不同方法：未加权， $\kappa$ 权重和基线特征权重。我们的分析表明，就主题依赖表现而言，PSM的表现优于PIM，而PIM的表现则优于PSM的55.9％和 $\kappa$ 按主题无关的表现，加权加权EPSM（表现最佳的EPSM类型）增加了16.4％。