This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal (MES). The scheme described within uses pattern recognition to process four channels of MES, with the task of discriminating multiple classes of limb movement. The method does not require segmentation of the MES data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. It is shown in this paper that, by exploiting the processing power inherent in current computing systems, substantial gains in classifier accuracy and response time are possible. Other important characteristics for prosthetic control systems are met as well. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. Finally, minimal storage capacity is required, which is an important factor in embedded control systems.

A robust, real-time control scheme for multifunction myoelectric control

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(12)61816-9.pdf

High-performance neuroprosthetic control by an individual with tetraplegia

Feature extraction is a significant method to extract the useful information which is hidden in surface electromyography (EMG) signal and to remove the unwanted part and interferences. To be successful in classification of the EMG signal, selection of a feature vector ought to be carefully considered. However, numerous studies of the EMG signal classification have used a feature set that have contained a number of redundant features. In this study, most complete and up-to-date thirty-seven time domain and frequency domain features have been proposed to be studied their properties. The results, which were verified by scatter plot of features, statistical analysis and classifier, indicated that most time domain features are superfluity and redundancy. They can be grouped according to mathematical property and information into four main types: energy and complexity, frequency, prediction model, and time-dependence. On the other hand, all frequency domain features are calculated based on statistical parameters of EMG power spectral density. Its performance in class separability viewpoint is not suitable for EMG recognition system. Recommendation of features to avoid the usage of redundant features for classifier in EMG signal classification applications is also proposed in this study.

Feature reduction and selection for EMG signal classification

Myoelectric control systems—A survey

A surgical instrument is provided, including: at least one articulatable arm having a distal end, a proximal end, and at least one joint region disposed between the distal and proximal ends; an optical fiber bend sensor provided in the at least one joint region of the at least one articulatable arm; a detection system coupled to the optical fiber bend sensor, said detection system comprising a light source and a light detector for detecting light reflected by or transmitted through the optical fiber bend sensor to determine a position of at least one joint region of the at least one articulatable arm based on the detected light reflected by or transmitted through the optical fiber bend sensor; and a control system comprising a servo controller for effectuating movement of the arm.

Robotic surgery system including position sensors using fiber bragg gratings

A novel signal processing approach for surface electromyogram (EMG) is proposed to estimate the forces produced at multiple degree of freedom (DOF) simultaneously during dynamic voluntary contractions. The method is based on a generative model of surface EMG, and the estimation algorithm is a modified non-negative matrix factorization (NFM). The proposed method has potential to provide simultaneous and proportional control information for multi-function powered prosthesis.

Estimating forces at multiple degrees of freedom from surface EMG using non-negative matrix factorization for myoelectric control

A simulation model for the motor unit innervation process (MUIP) is proposed. The model is based on the Hodgkin-Huxley (HH) model, which enables it to simulate the complex dynamics of MUIPs, such as the interactions between motor unit common drive and MUIP correlation (motor unit synchrony). Some simulation results are presented and possible extensions of the model are discussed

A Novel Simulation Model for the Motor Unit Innervation Process

Muscle Activation Patterns of the Forearm: High Density Emg Data of Normally Limbed and Transradial Amputee Subjects

https://proceedings.cmbes.ca/index.php/proceedings/article/download/754/748

A Study of Kalman Filtering Applied to Myoelectric Signal State Tracking

Providing an externally powered prosthetic solution for high-level upper extremity amputation cases requires several design factors to be considered by the clinical fitting team. The sensors used must often be finely adjusted during the prosthesis fabrication in order to provide consistent repeatable control input signals. The work presented in this paper introduces a mathematical framework capable of implementing robust residual shoulder motion driven control using only a short preliminary training protocol in an attempt to remove issues such as sensor type, alignment, and range of motion. The algorithm determines the output amplitude values based on the real time residual shoulder position to reliably drive the prosthetic limb’s actuators. This algorithm provides a new control input option for designing an externally powered prosthetic solution for high-level amputation cases.

https://proceedings.cmbes.ca/index.php/proceedings/article/download/340/334

Kevin Englehart

Papers

Estimating forces at multiple degrees of freedom from surface EMG using non-negative matrix factorization for myoelectric control

A Novel Simulation Model for the Motor Unit Innervation Process

Muscle Activation Patterns of the Forearm: High Density Emg Data of Normally Limbed and Transradial Amputee Subjects

A Study of Kalman Filtering Applied to Myoelectric Signal State Tracking

Residual Shoulder Motion Vector Projection