Cardiac pulse detection in BCG signals implemented on a regular classroom chair integrated to an emotional and learning model for personalization of learning resources

Emotions are related with learning processes, and physiological signals can be used to detect them. In this work, it is presented a model for the dynamic personalization of a learning environment. In the model, a specific combination of emotions and cognition processes are connected and integrated with the concept of `flow´, since it may provide a way to improve learning abilities. Physiological signals can be used to relate temporal emotions and subject´s learning processes; the cardiac pulse is a reliable signal that carries useful information about the subject´s emotional condition, which is detected using a classroom chair adapted with non invasive Electro-Mechanical Film (EMFi) sensors and an acquisition system that generates a ballistocardiogram (BCG), which is analyzed by a developed algorithm to obtain cardiac pulse statistics. A study including different data obtained from the chair´s sensors was carried out and interpreted in terms of emotions, which together with a cognitive model is used for the personalization of content in a learning scenario. Such model establishes a relation between the learner´s knowledge, the content of a course or a learning activity and the emotional condition of each student.