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

Abstract

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.

Adaptive Hierarchical Classification for Human Activity Recognition Using Inertial Measurement Unit (IMU) Time-Series Data

“…In [9] used the normalized confusion matrix of LSTM in applying a hierarchical agglomerative clustering algorithm for automatic tree taxonomy building without providing enough details. The authors in [52] showed that using any HC produces better performance than FC. It also showed by examining different hierarchy taxonomies that changing the taxonomy will not yield significant improvement.…”

“…When flat or LCL classification methods are used, such types always suffer from high misclassification rates. Although LCPN could propagate the errors from one level to the next, its overall accuracy would still be higher than the flat classification [55]. This approach is preferable for researchers in the HAR domain who consider hierarchical classification for its lower complexity [13].…”

A lightweight deep learning with feature weighting for activity recognition

“…Recent research on activity recognition has employed wearable sensors for activity data collection, with the most common sensor device being an accelerometer. 24,25,26 An accelerometer is a device that measures the acceleration of an entity. Other sensor devices include gyroscopes, magnetometers, electrocardiography monitors, and many others.…”

Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition

“…As an advantage, considering these classes paired together as opposed to the flat classification setting leads to significant degradation of recognition performances as demonstrated in some works around the SHL dataset [23]. In contrast, organizing the various concepts into a tree-like structure, inspired by domain expertise, demonstrated significant gains in terms of recognition performances in the context of the SHL challenge [12] and activity recognition in general [15,16].…”