A framework for energy-efficient equine activity recognition with leg accelerometers

Automated behavioral detection and classification through sensors can enhance the horses’ health and welfare. Since monitoring needs to be carried out continuously, an energy-efficient method is needed. The number of logging axes, sampling rate, and selected features of accelerometer data not only have a significant impact on classification accuracy in activity recognition but also on the sensors’ energy needs. Three models are designed for...