What is it about?
More and more, people are using "wearables" - from Bluetooth headphones or hands-free headsets, to fitness trackers, to smart watches: rapid growth in this area has been seen and is set to continue. Such devices communicate with a smart phone uses wireless communications, usually Bluetooth operating at 2.45 GHz. The conditions experience by the radio link are strongly affected by the activity of the person. In this paper, we investigate the radio channel during three different types of sports activity: jogging, cycling and rowing. We further examine the possibility of extracting meaningful data from the variation in the radio channel, and show that activity classification is feasible. In addition, we see if physiological data, such as breathing rate and heart rate, can also be identified from the variation in the radio channel, concluding that breathing rate can be seen, but not heart rate.
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Why is it important?
Wearable devices are becoming increasingly popular and are an important part of the quantified self and digital health movements. They typically reply on accelerometers to detect movement, and other sensors for physiological signals (e.g., temperature, blood pressure, heart rate, breathing rate). Detection of breathing rate typically requires a restrictive band worn around the chest. We have shown that it seems possible, in principle, to detect breathing rates during periods of exertion, though more work is required to refine the process and achieve accurate data. Furthermore, the smart home and internet of things will require context to understand user requirements at any given time. The radio channel may add to the sources providing such context.
Perspectives
The interesting thing for me with this work is that there are clear differences between activities and the different radio channels. Fitness trackers currently require users to tell the app what type of activity was performed, for it to estimate calories used. It seems to me there is a possibility this could be automated to some extent, using radio links between devices the user may already be wearing for fitness or health tracking. I also would like to see how robust the breathing detection could be, as removing the need to wear a special band would be good, whilst breathing rate could feed into an algorithm determining the calories used.
Dr Robert N Foster
University of Birmingham
Read the Original
This page is a summary of: Exploring Physiological Parameters in Dynamic WBAN Channels, IEEE Transactions on Antennas and Propagation, October 2014, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/tap.2014.2342751.
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