ABSTRACT With the increasing scale of farms and the correspondingly higher number of laying hens, it is increasingly difficult for farmers to monitor their animals in a traditional way. Early warning of abnormal animal activities is helpful for farmers’ fast response to the negative impact on animal health, animal welfare and daily management. This study introduces an automatic and non-invasive method for detecting abnormal poultry activities using a 3D depth camera. A typical region including eighteen Hy-line brown laying hens was continuously monitored by a top-view Kinect during 49 continuous days. A mean prediction model (MPM), based on the frame difference algorithm, was built to monitor animal activities and occupation zones. As a result, this method reported abnormal activities with an average accuracy of 84.2% and a rate of misclassifying abnormal events of 15.8% (PFPR). Additionally, it was found that the flock showed a diurnal change pattern in the activity and occupation quantified index. They also presented a similar changing pattern each week.

La satisfacción es una dimensión de la usabilidad que adolece de métricas cuantitativas, calculadas durante la interacción, y se limita a una valoración subjetiva dependiente de la habilidad de interpretación de cuestionarios y recordación del usuario. Por esta razón, en este trabajo se busca establecer una métrica cuantitativa y automática para la satisfacción. Para ello, se tomaron como base los estados mentales de atención/meditación del usuario, haciendo uso de una Interfaz cerebro-computador. Los resultados obtenidos muestran la correlación entre el estado de atención y el nivel de satisfacción del usuario

Satisfaction is a dimension of usability for which quantitative metrics cannot be calculated during user interactions. Measurement is subjective and depends on the ability to interpret questionnaires and on the memory of the user. This paper represents an attempt to develop an automatic quantitative metric of satisfaction, developed using a Brain Computer Interface to monitor the mental states (Attention/Meditation) of users. Based on these results, we are able to establish a correlation between the state of Attention and the users' level of satisfaction