Conference Proceedings


Tomas E. Ward
Alan F. Smeaton
Jose Juan Dominguez Veiga
Zhengwei Wang
Eoin Brophy


Computer Science

artificial intelligence visualization support vector machine human activity recognition deep learning explainable artificial intelligence machine learning activity recognition

An interpretable machine vision approach to human activity recognition using photoplethysmograph sensor data (2018)

Abstract The current gold standard for human activity recognition (HAR) is based on the use of cameras. However, the poor scalability of camera systems renders them impractical in pursuit of the goal of wider adoption of HAR in mobile computing contexts. Consequently, researchers instead rely on wearable sensors and in particular inertial sensors. A particularly prevalent wearable is the smart watch which due to its integrated inertial and optical sensing capabilities holds great potential for realising better HAR in a non-obtrusive way. This paper seeks to simplify the wearable approach to HAR through determining if the wrist-mounted optical sensor alone typically found in a smartwatch or similar device can be used as a useful source of data for activity recognition. The approach has the potential to eliminate the need for the inertial sensing element which would in turn reduce the cost of and complexity of smartwatches and fitness trackers. This could potentially commoditise the hardware requirements for HAR while retaining the functionality of both heart rate monitoring and activity capture all from a single optical sensor. Our approach relies on the adoption of machine vision for activity recognition based on suitably scaled plots of the optical signals. We take this approach so as to produce classifications that are easily explainable and interpretable by non-technical users. More specifically, images of photoplethysmography signal time series are used to retrain the penultimate layer of a convolutional neural network which has initially been trained on the ImageNet database. We then use the 2048 dimensional features from the penultimate layer as input to a support vector machine. Results from the experiment yielded an average classification accuracy of 92.3\%. This result outperforms that of an optical and inertial sensor combined (78\%) and illustrates the capability of HAR systems using standalone optical sensing elements which also allows for both HAR and heart rate monitoring. Finally, we demonstrate through the use of tools from research in explainable AI how this machine vision approach lends itself to more interpretable machine learning output.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> Subject = Computer Science: Artificial intelligence
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Computer Science: Visualization
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: INSIGHT Centre for Data Analytics
Ireland -> Dublin City University -> Subject = Computer Science: Machine learning

Full list of authors on original publication

Tomas E. Ward, Alan F. Smeaton, Jose Juan Dominguez Veiga, Zhengwei Wang, Eoin Brophy

Experts in our system

Tomas Ward
Maynooth University
Total Publications: 189
Alan F. Smeaton
Dublin City University
Total Publications: 492
Jose Juan Dominguez Veiga
Dublin City University
Total Publications: 3
Zhengwei Wang
Dublin City University
Total Publications: 11