VTechWorks staff will be away for the Thanksgiving holiday beginning at noon on Wednesday, November 27, through Friday, November 29. We will resume normal operations on Monday, December 2. Thank you for your patience.

Browsing by Author "Zhao, Zhuqing"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Apply Machine Learning on Cattle Behavior Classification Using Accelerometer Data
    Zhao, Zhuqing (Virginia Tech, 2022-04-15)
    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.
  • Thumbnail Image
    Improved pig behavior analysis by optimizing window sizes for individual behaviors on acceleration and angular velocity data
    Alghamdi, Saleh; Zhao, Zhuqing; Ha, Dong S.; Morota, Gota; Ha, Sook S. (Oxford University Press, 2022-11-01)
    This paper presents the application of machine learning algorithms to identify pigs' behaviors from data collected using the wireless sensor nodes mounted on pigs. The sensor node attached to a pig's back senses the acceleration and angular velocity in three axes, and the sensed data are transmitted to a host computer wirelessly. Two video cameras, one attached to the ceiling of the pigpen and the other one to a fence, provided ground truth for data annotations. The data were collected from pigs for 131 h over 2 mo. As the typical behavior period depends on the behavior type, we segmented the acceleration data with different window sizes (WS) and step sizes (SS), and tested how the classification performance of different activities varied with different WS and SS. After exploring the possible combinations, we selected the optimum WS and SS. To compare performance, we used five machine learning algorithms, specifically support vector machine, k-nearest neighbors, decision trees, naive Bayes, and random forest (RF). Among the five algorithms, RF achieved the highest F1 score for four major behaviors consisting of 92.36% in total. The F1 scores of the algorithm were 0.98 for "eating,"0.99 for "lying,"0.93 for "walking,"and 0.91 for "standing"behaviors. The optimal WS was 7 s for "eating"and "lying,"and 3 s for "walking"and "standing."The proposed work demonstrates that, based on the length of behavior, the adaptive window and step sizes increase the classification performance.