By use of hand gesture recognition technology, traditional remote manufacturing networks can be operated by disabled people who lose the grasp ability. The surface electromyography (sEMG) network inference is critically important for revealing fundamental hand gesture processes, investigating sEMG functions, and understanding their relations. However, current hand gesture recognition methods using sEMG do not sufficiently consider the temporal behavior of this type of data and lack the capability to capture the complex nonlinear system dynamics. It is therefore imperative to have good methods to explore a more suitable choice, which can avoid the problems mentioned above. This paper presents a rapid prototyping manufacturing network with sEMG using recurrent hidden Markov models (RHMMs) and a particle swarm optimization (PSO) approach. It also provides high-level modeling, programming methods, and running results for this remote manufacturing network in terms of software and hardware. PSO is used to train the RHMM and determine the model parameters. Remote manufacturing is accomplished between the network monitor center and a number of sensor nodes by use of a sEMG sensor circuit and Winsock monitor sensors. Furthermore, it has such advantages as network wireless, low cost, and strong interaction and expansibility, and is easy to maintain and promote.
- Particle swarm optimization
- pattern recognition
- rapid prototyping manufacturing
- recurrent hidden Markov model
- surface electromyography