Time Series Classification to Improve Poultry Welfare

-- Poultry farms are an important contributor to the human food chain. Worldwide, humankind keeps an enormous number of domesticated birds (e.g. However, around the world, there have been growing concerns about the quality of life for the livestock in poultry farms; and increasingly vocal demands for improved standards of animal welfare. Recent advances in sensing technologies and machine learning allow the possibility of automatically assessing the health of some individual birds, and employing the lessons learned to improve the welfare for all birds. This task superficially appears to be easy, given the dramatic progress in recent years in classifying human behaviors, and given that human behaviors are presumably more complex. However, as we shall demonstrate, classifying chicken behaviors poses several unique challenges, chief among which is creating a generalizable "dictionary" of behaviors from sparse and noisy data. In this work we introduce a novel time series dictionary learning algorithm that can robustly learn from weakly labeled data sources. Poultry farms are a major source of high-protein and low-fat food. Given the ever-increasing population of the world, the demand for such food sources has been steadily growing. According to Food and Agriculture Organization of the United Nations (FAO), poultry meat consumption around the world has climbed from 11 kg/person in 2000 to 14.1 kg/person in 2011 [2]; and it is predicted to continue for the foreseeable future [3].