A New Algorithm to Estimate Glenohumeral Joint Location Based on Scapula Rhythm.

This work analyzes the human shoulder complex workspace by introducing a new method to estimate the intraarticulation location of the glenohumeral (GH) joint. The proposed algorithm is based on the hypothesis of the GH joint remaining fixed during the first 30 degrees of shoulder elevation. This part of any vertical movement is considered to estimate the center of spherical motions CoS) where the humeral head is located.

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.

Frequency domain surface EMG sensor fusion for estimating finger forces.

Extracting or estimating skeletal hand/finger forces using surface electro myographic (sEMG) signals poses many challenges due to cross-talk, noise, and a temporal and spatially modulated signal characteristics. Normal sEMG measurements are based on single sensor data. In this paper, array sensors are used along with a proposed sensor fusion scheme that result in a simple Multi-Input-Single-Output (MISO) transfer function.

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.