Next Article in Journal
Development of a Low-Cost Narrow Band Multispectral Imaging System Coupled with Chemometric Analysis for Rapid Detection of Rice False Smut in Rice Seed
Next Article in Special Issue
Wearable Sensor-Based Gait Analysis for Age and Gender Estimation
Previous Article in Journal
Reliable Task Management Based on a Smart Contract for Runtime Verification of Sensing and Actuating Tasks in IoT Environments
Previous Article in Special Issue
Zero-Shot Human Activity Recognition Using Non-Visual Sensors
Article

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

1
Insight Centre for Data Analytics, School of Computer Science and Information Technology, University College Cork, T12 XF62 Cork, Ireland
2
Tyndall National Institute, University College Cork, T12 R5CP Cork, Ireland
3
CONNECT Centre for Future Networks and Communications, Tyndall National Institute, University College Cork, T12 R5CP Cork, Ireland
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(4), 1208; https://doi.org/10.3390/s20041208
Received: 31 December 2019 / Revised: 17 February 2020 / Accepted: 19 February 2020 / Published: 22 February 2020
(This article belongs to the Special Issue Inertial Sensors for Activity Recognition and Classification)
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. View Full-Text
Keywords: human activity recognition; machine learning; wearable sensors; inertial sensors; multi-class classification; hierarchical classification; error-correcting output codes; ensembles of nested dichotomies human activity recognition; machine learning; wearable sensors; inertial sensors; multi-class classification; hierarchical classification; error-correcting output codes; ensembles of nested dichotomies
Show Figures

Figure 1

MDPI and ACS Style

Scheurer, S.; Tedesco, S.; Brown, K.N.; O’Flynn, B. Using Domain Knowledge for Interpretable and Competitive Multi-Class Human Activity Recognition. Sensors 2020, 20, 1208. https://doi.org/10.3390/s20041208

AMA Style

Scheurer S, Tedesco S, Brown KN, O’Flynn B. Using Domain Knowledge for Interpretable and Competitive Multi-Class Human Activity Recognition. Sensors. 2020; 20(4):1208. https://doi.org/10.3390/s20041208

Chicago/Turabian Style

Scheurer, Sebastian, Salvatore Tedesco, Kenneth N. Brown, and Brendan O’Flynn. 2020. "Using Domain Knowledge for Interpretable and Competitive Multi-Class Human Activity Recognition" Sensors 20, no. 4: 1208. https://doi.org/10.3390/s20041208

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop