Discriminative restricted Boltzmann machine for emergency detection on healthcare robot

In this work, we propose a concept of emergency detection algorithm for healthcare robot which adopts discriminative restricted Boltzmann machine for anomaly detection. We will adopt anomaly detection rather than simple emergency case classification as it is hard to collect real emergency data to train the effective classifier. The conventional anomaly detection method uses decision tree to analyze the signals obtained from the sensors attached on the bodies of the patients to find out the emergency situations. We propose anomaly detection using video and audio signals as they are easy to be obtained by the healthcare robot, with equipping a camera and a microphone, and it is much more convenient for patients. The discriminative restricted Boltzmann machine which is specialized in learning probability distribution in an unsupervised manner will be applied for anomaly detection. This paper only provides the novel idea for emergency detection. The implementation and the experiments will be conducted in the future work.