Abstract
Continuous and automatic monitoring of an individual’s physical activity using wearable devices provides valuable insights into their daily habits and patterns. This information can be used to promote healthier lifestyles, prevent chronic diseases, and improve overall well-being. Smart glasses are an emerging technology that can be worn comfortably and continuously. Their wearable nature and hands-free operation make them well suited for long-term monitoring of physical activity and other real-world applications. To this end, we investigated the ability of the multi-sensor OCOsense™ smart glasses to recognize everyday activities. We evaluated three end-to-end deep learning architectures that showed promising results when working with IMU (accelerometer, gyroscope, and magnetometer) data in the past. The data used in the experiments was collected from 18 participants who performed pre-defined activities while wearing the glasses. The best architecture achieved an F1 score of 0.81, demonstrating its ability to effectively recognize activities, with the most problematic categories being standing vs. sitting.
Competing Interest Statement
The study was funded by emteqLabs.
Funding Statement
The study was funded by emteqLabs.
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
The study was reviewed and approved by the NHS research ethics committee, (ref: 18/WM/0205)
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Yes
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Yes
I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.
Yes
Footnotes
simon.stankoski{at}emteqlabs.com
