Gyro’s the next thing in wearable technology, see our interview in Electronics Letters

Gyroscope classifierAccelerometers are now the mainstay in wearable technology, why, because they are cheap to buy and cheap to run – now only consuming microamps of power. Rate gyroscopes are a close cousin that yield important but different metrics of human movement. Rate gyros traditionally have been a bit thirsty, but thanks to recent trends in development they are about to become the next big thing in wearables.

Recently Mitch applied gyros to human activity with impressive results, so much so that Electronics Letters interviewed lead supervisor  and SABEL’s deputy director David Rowlands. Well done Mitch!

See David’s interview here http://digital-library.theiet.org/content/journals/10.1049/el.2015.1243?

, with a link to the full paper was well http://digital-library.theiet.org/content/journals/10.1049/el.2015.0436

Here is the Abstract

Gyroscope classifier resultsDecision-tree-based human activity classification algorithm using single-channel foot-mounted gyroscope

M.W. McCarthy✉, D.A. James, J.B. Lee and D.D. Rowlands

Wearable devices that measure and recognise human activity in real time require classification algorithms that are both fast and accurate when implemented on limited hardware. A decision-tree-based method for differentiating between individual walking, running, stair climbing and stair descent strides using a single channel of a foot- mounted gyroscope suitable for implementation on embedded hard- ware is presented. Temporal features unique to each activity were extracted using an initial subject group (n = 13) and a decision-tree- based classification algorithm was developed using the timing in- formation of these features. A second subject group (n = 10) completed the same activities to provide data for verification of the system. Results indicate that the classifier was able to correctly match each stride to its activity with >90% accuracy. Running and walking strides in particular matched with >99% accuracy. The outcomes demonstrate that a lightweight yet robust classification system is feas- ible for implementation on embedded hardware for real-time daily monitoring.

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