Abstract: A stochastic Bayesian nonlinear filtering system and method that improves the filtering of noisy signals by providing efficiency, power, speed, and flexibility. The filter only requires the likelihood function p(observation|state) to determine the system state and works in various measurement models. This allows for the processing of noisy signals to be used in real time, such as in a biofeedback device that senses noisy surface electromyography muscle electrical activity, filters the sensed signal using the nonlinear filtering method, and provides vibrations based on the muscular activity.

Abstract: A stochastic Bayesian non-linear filtering system and method that improves the filtering of noisy signals by providing efficiency, power, speed, and flexibility. The filter only requires the likelihood function p(observation|state) to determine the system state and works in various measurement models. This allows for the processing of noisy signals to be used in real time, such as in a biofeedback device that senses noisy surface electromyography muscle electrical activity, filters the sensed signal using the nonlinear filtering method, and provides vibrations based on the muscular activity.