Wearable technology

Embedded system

Resampling

Operating system

Computer science

Filter (signal processing)

Accelerometer

Pattern recognition (psychology)

Activity recognition

Sampling (signal processing)

Wearable computer

Artificial intelligence

Deep learning

Artificial neural network

Feature extraction

Convolutional neural network

Speech recognition

Computer vision

Monitoring horses' behaviors through sensors can yield important information about their health and welfare. Sampling frequency majorly affects the classification accuracy in activity recognition and energy needs for the sensor. The aim of this study was to evaluate the effect of sampling rate reduction of a tri-axial accelerometer on the recognition accuracy by resampling a 50 Hz experimental dataset to four lower sampling rates (5 Hz, 10 Hz, 12.5 Hz and 25 Hz). Also, in this work we investigate the `reality gap' that incorporates changes in the data that are primarily characterized as sensor rotations or measurement noise through various data augmentation techniques such as rotation and jittering. Finally, another factor influencing activity recognition are the subjects themselves and therefore the model is evaluated on different horse types. A deep learning-based approach for activity detection of equines is proposed to automatically classify 2238 manually annotated 2 s samples tri-axial accelerometer leg data data of seven different activities performed by six different subjects. The raw data are preprocessed and fed into a convolutional neural network (CNN) from which features are extracted automatically by using strong computing capabilities. Furthermore, the neural network was intentionally designed to minimize running time, enabling us to imagine the future use of the built model in embedded constrained devices. The complexity of these automatic learning techniques can be decreased while achieving high accuracies using ten-fold-cross validation using a computationally less intensive received signal length data (99.32% at 5 Hz vs 99.74% at 25 Hz). This indicates that sampling at 5 Hz with a 2 s window will offer advantages for activity surveillance thanks to decreased energy requirements, since validation time decreases 16-fold (784 microseconds at 50 Hz to 48 microseconds at 5 Hz). Moreover, in this work we show that rotating the training or validation signal with 10 degrees over the X, Y and Z-axis increases the generalization capabilities of our model (99.61 % vs 99.93%) while adding small amounts of noise (smaller than 0.3 standard deviation (STD)) does not decrease the classification accuracy under 99%. Finally, the performance and ability of the model to generalize is validated on data from unseen horses at the cost of only 4.1% and 2.45% reduction in accuracy when validated on a pony and a lame horse, respectively.

Resampling and Data Augmentation For Equines’ Behaviour Classification Based on Wearable Sensor Accelerometer Data Using a Convolutional Neural Network