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A machine vision approach to human activity recognition using photoplethysmograph sensor data

Brophy, Eoin orcid logoORCID: 0000-0002-6486-5746, Wang, Zhengwei orcid logoORCID: 0000-0001-7706-553X, Dominguez Veiga, José Juan orcid logoORCID: 0000-0002-6634-9606 and Ward, Tomás E. orcid logoORCID: 0000-0002-6173-6607 (2018) A machine vision approach to human activity recognition using photoplethysmograph sensor data. In: 29th Irish Signals and Systems Conference, 21 - 22 June 2018, Belfast, Northern Ireland. ISBN 987-1-5386-6046-1

Human activity recognition (HAR) is an active area of research concerned with the classification of human motion. Cameras are the gold standard used in this area, but they are proven to have scalability and privacy issues. HAR studies have also been conducted with wearable devices consisting of inertial sensors. Perhaps the most common wearable, smart watches, comprising of inertial and optical sensors, allow for scalable, non-obtrusive studies. We are seeking to simplify this wearable approach further by determining if wrist-mounted optical sensing, usually used for heart rate determination, can also provide useful data for relevant activity recognition. If successful, this could eliminate the need for the inertial sensor, and so simplify the technological requirements in wearable HAR. We adopt a machine vision approach for activity recognition based on plots of the optical signals so as to produce classifications that are easily explainable and interpretable by non-technical users. Specifically, time-series images of photoplethysmography signals are used to retrain the penultimate layer of a pretrained convolutional neural network leveraging the concept of transfer learning. Our results demonstrate an average accuracy of 75.8%. This illustrates the feasibility of implementing an optical sensor-only solution for a coarse activity and heart rate monitoring system. Implementing an optical sensor only in the design of these wearables leads to a trade off in classification performance, but in turn, grants the potential to simplify the overall design of activity monitoring and classification systems in the future.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:activity recognition; deep learning; activity recognition; biomedical; photoplethysmography
Subjects:Computer Science > Machine learning
Engineering > Signal processing
Computer Science > Artificial intelligence
Engineering > Biomedical engineering
DCU Faculties and Centres:Research Institutes and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: 29th Irish Signals and Systems Conference Proccedings. . IEEE. ISBN 987-1-5386-6046-1
Official URL:https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&ar...
Copyright Information:© 2018 IEEE
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:John Hume Studentship - Maynooth University, Science Foundation Ireland - SFI/12/RC/2289
ID Code:22433
Deposited On:03 Jul 2018 09:57 by Eoin Brophy . Last Modified 11 Feb 2019 11:15

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