Parmod Kumar

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. Experimental data is used along with system identification to find this MISO system. A Genetic Algorithm (GA) approach is employed to optimize the characteristics of the MISO system. The proposed fusion-based approach is tested experimentally and indicates improvement in finger/hand force estimation.

Surface EMG Array Sensor Based Model Fusion Using Bayesian Approaches for Prosthetic Hands

Traditional electromyopgrahic (EMG) measurements are based on single sensor information. Due to the arrangement of skeletal muscle fibers for hand motions, cross talk is an inherent problem when inferring motion/force potentials from EMG data. This paper studies means of using sensor arrays to infer better motion/force potential for prosthetic hands. In particular, a surface electromyographic (sEMG) sensor array is used to investigate multiple model fusion techniques. This paper provides a comparison between three statistical model selection criteria. The sEMG signals are pre-processed using four filters, Butterworth, Chebyshev type-II, as well as Bayesian filters such as the Exponential and Half-Gaussian filter. Output Error (OE) models were extracted from sEMG data and hand force data and compared using a Bayesian based fusion model. The four different filters effect were quantified based on the OE models performance in matching the actual measured data. The comparison indicates a preference for using the sensor fusion technique with preprocessed EMG data using the Half-Gaussian Bayesian filter and the Kullback Information Criterion (KIC).Copyright

Implementation of sEMG-based real-time embedded adaptive finger force control for a prosthetic hand

This paper presents surface electromyographic (sEMG)-based, real-time Model Reference Adaptive Control (MRAC) strategy for a prosthetic hand prototype. The proposed design is capable of decoding the prerecorded sEMG signal as well as the sensory force feedback from the sensors to control the force of the prosthetic hand prototype using a PIC 32MX360F512L microcontroller. 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. This MISO system provides the estimated finger forces to be produced as input to the prosthetic hand prototype. A simple MRAC method along with a two stage embedded design is used for the force control of the prosthetic hand. The sensed force at the fingertip is fed back to the controller for real-time operation. The data is transmitted to the computer through an universal asynchronous receiver/transmitter (UART) interface of the proposed embedded design. Results show good performance in controlling the finger force as well as shortcomings of the mechanical design of the prosthetic hand prototype to be addressed in future.

A hybrid adaptive data fusion with linear and nonlinear models for skeletal muscle force estimation

Position and force control are two critical aspects of prosthetic control. Surface electromyographic (sEMG) signals can be used for skeletal muscle force estimation. In this paper, skeletal muscle is considered as a system and System Identification (SI) is used to model sEMG and skeletal muscle force. The recorded sEMG signal is filtered utilizing optimized nonlinear Half-Gaussian Bayesian filter, and a Chebyshev type-II filter prepares the muscle force signal. The filter optimization is accomplished using Genetic Algorithm (GA). Multi- linear and nonlinear models are obtained with sEMG as input and skeletal muscle force of a human hand as an output. The outputs of these models are fused with a probabilistic Kullback Information Criterion (KIC) for model selection and an adaptive probability of KIC. This approach gives good estimate of the skeletal muscle force.

An adaptive hybrid data fusion based identification of skeletal muscle force with ANFIS and smoothing spline curve fitting

Precise and effective prosthetic control is important for its applicability. Two desired objectives of the prosthetic control are finger position and force control. Variation in skeletal muscle force results in corresponding change of surface electromyographic (sEMG) signals. sEMG signals generated by skeletal muscles are temporal and spatially distributed that result in cross talk between adjacent sEMG signal sensors. To address this issue, an array of nine sEMG sensors is used with a force sensing resistor to capture muscle dynamics in terms of sEMG and skeletal muscle force. sEMG and skeletal muscle force are filtered with a nonlinear Teager-Kaiser Energy (TKE) operator based nonlinear spatial filter and Chebyshev type-II filter respectively. Multiple Takagi-Sugeno-Kang Adaptive Neuro Fuzzy Inference Systems (ANFIS) are obtained using sEMG as input and skeletal muscle force as output. Outputs of these ANFIS systems are fitted with smoothing spline curve fitting. To achieve better estimate of the skeletal muscle force, an adaptive probabilistic Kullback Information Criterion (KIC) for model selection based data fusion algorithm is applied to the smoothing spline curve fitting outputs. Final fusion based output of this approach results in improved skeletal muscle force estimates.

sEMG based fuzzy control strategy with ANFIS path planning for prosthetic hand

This paper presents an intelligent adaptive neurofuzzy inference system (ANFIS) based fuzzy Mamdani controller for a multifingered prosthetic hand. The objective of the controller is to move the finger joint angles along predetermined paths representing a grasping motion. The initiation of the grasping task is evaluated via EMG-entropy data, measured at the forearm of the prosthetic user. In addition to the motion control, the finger force is regulated with a Fuzzy logic controller. Simulation results indicate good performance of the proposed controller. Results show that the outputs follow the hand/finger force and given reference trajectory closely.

Optimization of Bayesian Filters and Hammerstein-Wiener Models for EMG-Force Signals Using Genetic Algorithm

Processing electromyographic (EMG) signals for force estimation has many unknown variables that can influence the outcome or interpretation of the recorded EMG signal significantly. An array of filtering methods have been proposed over the past few years with the objective to classify motion for use in prosthetic hands. In this paper, we explore the optimal parameter settings of a set of Bayesian based EMG filters with the objective to use the filtered EMG data for system identification. System identification is utilized to establish a relationship between the measured EMG data and the generated force developed by fingers in a human hand. The proposed system identification is based on nonlinear Hammerstein-Wiener models. Optimization is also applied to find the optimal parameter settings for these nonlinear models. Genetic Algorithm (GA) is used to conduct the optimization for both, the optimal parameter settings for the Bayesian filters as well as the Hammerstein-Wiener model. The experimental results and optimization analysis indicate that the optimization can yield significant improvement in data accuracy and interpretation.Copyright

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. In this study, an array of three sEMG sensors is used to capture the information of muscle dynamics in terms of sEMG signals. The recorded sEMG signals are filtered utilizing optimized nonlinear Half-Gaussian Bayesian filters parameters, and the muscle force signal using a Chebyshev type-II filter. The filter optimization is accomplished using Genetic Algorithms. Three discrete time state-space muscle fatigue models are obtained using system identification and modal transformation for three sets of sensors for single motor unit. The outputs of these three muscle fatigue models are fused with a probabilistic Kullback Information Criterion (KIC) for model selection. The final fused output is estimated with an adaptive probability of KIC, which provides improved force estimates

Analysis of EMG-Force Relation Using System Identification and Hammerstein-Wiener Models

Surface Electromyographic (sEMG) signals have been exploited for almost a century, for various clinical and engineering applications. One of the most compelling and altruistic applications being, control of prosthetic devices. The study conducted here looks at the modeling of the force and sEMG signals, using nonlinear Hammerstein-Weiner System Identification techniques. This study involved modeling of sEMG and corresponding force data to establish a relation which can mimic the actual force characteristics for a few particular hand motions. Analysis of the sEMG signals, obtained from specific Motor Unit locations corresponding to the index, middle and ring finger, and the force data led to the following deductions; a) Each motor unit location has to be treated as a separate system, (i.e. extrapolation of models for different fingers cannot be done) b) Fatigue influences the Hammerstein-Wiener model parameters and any control algorithm for implementing the force regimen will have to be adaptive in nature to compensate for the changes in the sEMG signal and c) The results also manifest the importance of the design of the experiments that need to be adopted to comprehensively model sEMG and force.Copyright

Spectral analysis of sEMG signals to investigate skeletal muscle fatigue

Our recent investigations are focused to develop dynamic models for skeletal muscle force and finger angles for prosthetic hand control using surface electromyographic sEMG as input. Since sEMG is temporal and spatially distributed and is influenced by various factors, muscle fatigue and its related sEMG becomes of importance. This study is an effort to spectrally analyze the sEMG signal during progression of muscle fatigue. The sEMG is captured from the arms of healthy subjects during muscle fatiguing experiments for dynamic and static force levels. Filtered sEMG signal is segmented in five parts with 75% overlap between adjacent segments. The analysis is done using different classical (fast Fourier transform, Welchs averaged modified periodogram), model-based (Yule-Walker, Burg, Covariance and Modified Covariance autoregressive (AR) method), and eigenvector methods (Multiple Signal Classification (MUSIC) and eigenvector spectral estimation method) in frequency domain. Results show that the classical and eigenvector based methods are more sensitive than the model-based methods to fatigue related changes in sEMG signals.