Subject-dependent and -independent human activity recognition with person-specific and -independent models

Self Cite

Human activity recognition (HAR) has become an increasingly popular application of machine learning across a range of domains. Typically the HAR task that a machine learning algorithm is trained for requires separating multiple activities such as walking, running, sitting, and falling from each other. Despite a large body of work on multi-class HAR, and the well-known fact that the performance on a multi-class problem can be significantly affected by how it is decomposed into a set of binary problems, there has been little research into how the choice of multi-class decomposition method affects the performance of HAR systems. This paper presents the first empirical comparison of multi-class decomposition methods in a HAR context by estimating the performance of five machine learning algorithms when used in their multi-class formulation, with four popular multi-class decomposition methods, five expert hierarchies—nested dichotomies constructed from domain knowledge—or an ensemble of expert hierarchies on a 17-class HAR data-set which consists of features extracted from tri-axial accelerometer and gyroscope signals. We further compare performance on two binary classification problems, each based on the topmost dichotomy of an expert hierarchy. The results show that expert hierarchies can indeed compete with one-vs-all, both on the original multi-class problem and on a more general binary classification problem, such as that induced by an expert hierarchy’s topmost dichotomy. Finally, we show that an ensemble of expert hierarchies performs better than one-vs-all and comparably to one-vs-one, despite being of lower time and space complexity, on the multi-class problem, and outperforms all other multi-class decomposition methods on the two dichotomous problems.

Section: Related Workmentioning

confidence: 99%

Using Domain Knowledge for Interpretable and Competitive Multi-Class Human Activity Recognition

Scheurer

Tedesco

Brown

et al. 2020

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“…Although DL and ML have found extensive application in BP assessment, the considerable inter-subject variability has posed challenges in formulating a sufficiently generalized model whose performance could also be maintained outside of the initial dataset. Therefore, drawing inspiration from established practices in the field of human activity recognition [ 34 , 35 ], numerous studies have suggested the formulation of person-specific models for the examination of this clinical parameter [ 36 , 37 , 38 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload

Valerio,

Demarchi,

O’Flynn

et al. 2024

Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure. We evaluated the behavior of this latter classifier according to the different sizes of the training set and degrees of personalization used. Aggregated accuracy, precision, recall, and F1-score were equal to 95.1%, 95.2%, 95%, and 95.4% when 30% of a target subject’s pulse waveforms were combined with five randomly selected source subjects available in the dataset. Experimental findings illustrated that incorporating a pre-training stage with data from different subjects made it viable to discern morphological distinctions in beat-to-beat pulse waveforms under conditions of cognitive or physical workload.

“…This is an extended version of a paper we presented at the 6th international Workshop on Sensor-based Activity Recognition and Interaction (iWOAR’19) [ 1 ]. In addition to the experiments, results, and analyses presented in that paper, this paper covers more of the literature in more depth, considers additional ways to combine person-specific models into ensembles, sharpens and deepens the statistical analysis, and expands the discussion by relating our findings to the pertinent literature.…”

Section: Introductionmentioning

confidence: 99%

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

Scheurer

Tedesco

O’Flynn

et al. 2020

Self Cite

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.