Authors:
Hiroyuki Okuda
;
Fumio Kometani
;
Shinkichi Inagaki
and
Tatsuya Suzuki
Affiliation:
Nagoya University, Japan
Keyword(s):
Human fatigue, Hybrid system, Electro Myo-Gram, Recognition.
Related
Ontology
Subjects/Areas/Topics:
Cybernetics
;
Health Engineering and Technology Applications
;
Human Augmentation and Shared Control
;
Human-Robots Interfaces
;
Hybrid Dynamical Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Machine Learning in Control Applications
;
Mechatronic Systems
;
NeuroSensing and Diagnosis
;
Neurotechnology, Electronics and Informatics
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
The man-machine cooperative system is attracting great attention in many fields, such as industry, welfare and so on. The assisting system must be designed so as to accommodate the operator’s skill, which might be strongly affected by the fatigue. This paper presents a new fatigue recognizer based on the Electro Myo-Gram (EMG) signals and the Stochastic Switched ARX (SS-ARX) model which is one of the extended model of the standard Hidden Markov Model (HMM). Since the SS-ARX model can represent complex dynamical relationship which involves switching and stochastic variance, it is expected to show higher performance as the fatigue recognizer than using simple statistical characteristics of the EMG signal and/or standard HMM. The usefulness of the proposed strategy is demonstrated by applying to a peg-in-hole task.