Decision-tree-based human activity classification algorithm using single-channel foot-mounted gyroscope

M McCarthy, Daniel James, James Bruce Lee, David Rowlands

    Research output: Contribution to journalArticlepeer-review


    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 footmounted gyroscope suitable for implementation on embedded hardware is presented. Temporal features unique to each activity were extracted using an initial subject group (n = 13) and a decision-treebased classification algorithm was developed using the timing information 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 feasible for implementation on embedded hardware for real-time daily monitoring.
    Original languageEnglish
    Pages (from-to)675-676
    Number of pages2
    JournalElectronics Letters
    Issue number9
    Publication statusPublished - 30 Apr 2015


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