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

• Published in: Electronics letters Vol. 51; no. 9; pp. 675 - 676
• Main Authors: McCarthy, M.W, James, D.A, Lee, J.B, Rowlands, D.D
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 30.04.2015
• Subjects: Biomedical technology; decision trees; decision‐tree‐based human activity classification algorithm; gyroscopes; human activity measurement; human activity recognition; running; sensors; single‐channel foot‐mounted gyroscope; stair climbing; stair descent stride; walking; wearable device; running; wearable device; sensors; stair descent stride; stair climbing; decision-tree-based human activity classification algorithm; human activity recognition; walking; decision trees; gyroscopes; human activity measurement; single-channel foot-mounted gyroscope
• ISSN: 0013-5194; 1350-911X; 1350-911X
• DOI: 10.1049/el.2015.0436

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 hardware 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 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.