We used a 50Hz sampling frequency to collect tri-axle acceleration from the cows. For the traditional Machine learning approach, we segmented the data to calculate features, selected the important features, and applied machine learning algorithms for classification. We compared the performance of various models and found a robust model with relatively low computation and high accuracy. For the deep learning approach, we designed an end-to-end trainable Convolutional Neural Networks (CNN) to predict activities for given segments, applied distillation, and quantization to reduce model size. In addition to the fixed window size approach, we used CNN to predict dense labels that each data point has an individual label, inspired by semantic segmentation. In this way, we could have a more precise measurement for the composition of activities. Summarily, physically monitoring the well-being of crowded animals is labor-intensive, so we proposed a solution for timely and efficient measuring of cattle’s daily activities using wearable sensors and machine learning models.