Detection of human mistakes and misperception for human perceptive augmentation: behavior monitoring using hybrid hidden Markov models

A method of detecting human mistakes and misperception for assisting humans in operating complex systems is presented. The method is developed in the context of operating iPASS (Integrative Physical Assists and Seamless Services) system which provides a patient diverse physical aids without changing equipment. The system can serve as a bed, a walker, a stand-up and seating assistance, as well as a wheelchair. iPASS needs special care for its operations because human mistakes and misperception might lead to serious consequences such as injury and costly repair. In order to detect human mistakes and misperception in a human motion, it is important to monitor a human motion and to understand human intention. In this paper, processes of human perception and motion are treated as stochastic processes, and they are modeled by using hybrid hidden Markov models. Finally, an application of this method to stand-up assistance for iPASS is described