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

M.W. McCarthy, J.B. Lee, D.D. Rowlands, D.A. James
2015 Electronics Letters  
Wearable devices that measure and recognise human activity in realtime require classification algorithms that are both fast and accurate when implemented on limited hardware. This paper presents 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 hardware. Temporal features unique to each activity were extracted using an initial
more » ... ject group (n=13) and a decision-tree based 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 of this study demonstrate that a lightweight yet robust classification system is feasible for implementation on embedded hardware for realtime daily monitoring.
doi:10.1049/el.2015.0436 fatcat:wjnoeoy3c5hzzo6kn6clpb7umy