Hybrid fusion of linear, non-linear and spectral models for the dynamic modeling of sEMG and skeletal muscle force: an application to upper extremity amputation.

Estimating skeletal muscle (finger) forces using surface Electromyography (sEMG) signals poses many challenges. In general, the sEMG measurements are based on single sensor data. In this paper, two novel hybrid fusion techniques for estimating the skeletal muscle force from the sEMG array sensors are proposed.

Fusion of spectral models for dynamic modeling of sEMG and skeletal muscle force.

In this paper, we present a method of combining spectral models using a Kullback Information Criterion (KIC) data fusion algorithm. Surface Electromyographic (sEMG) signals and their corresponding skeletal muscle force signals are acquired from three sensors and pre-processed using a Half-Gaussian filter and a Chebyshev Type- II filter, respectively. Spectral models - Spectral Analysis (SPA), Empirical Transfer Function Estimate (ETFE), Spectral Analysis with Frequency Dependent Resolution (SPFRD) - are extracted from sEMG signals as input and skeletal muscle force as output signal.

Optimal tracking of a sEMG based force model for a prosthetic hand.

This paper presents a surface electromyographic (sEMG)-based, optimal control strategy for a prosthetic hand. System Identification (SI) is used to obtain the dynamic relation between the sEMG and the corresponding skeletal muscle force. The input sEMG signal is preprocessed using a Half-Gaussian filter and fed to a fusion-based Multiple Input Single Output (MISO) skeletal muscle force model.

Towards smart prosthetic hand: Adaptive probability based skeletan muscle fatigue model.

Skeletal muscle force can be estimated using surface electromyographic (sEMG) signals. Usually, the surface location for the sensors is near the respective muscle motor unit points. Skeletal muscles generate a spatial EMG signal, which causes cross talk between different sEMG signal sensors.

A hybrid adaptive control strategy for a smart prosthetic hand.

This paper presents a hybrid of a soft computing technique of adaptive neuro-fuzzy inference system (ANFIS) and a hard computing technique of adaptive control for a two-dimensional movement of a prosthetic hand with a thumb and index finger. In particular, ANFIS is used for inverse kinematics, and the adaptive control is used for linearized dynamics to minimize tracking error. The simulations of this hybrid controller, when compared with the proportional-integral-derivative (PID) controller showed enhanced performance.

Control strategies for smart prosthetic hand technology: an overview.

A chronological overview of the applications of control theory to prosthetic hand is presented. The overview focuses on hard computing or control techniques such as multivariable feedback, optimal, nonlinear, adaptive and robust and soft computing or control techniques such as artificial intelligence, neural networks, fuzzy logic, genetic algorithms and on the fusion of hard and soft control techniques. This overview is not intended to be an exhaustive survey on this topic and any omissions of other works is purely unintentional.