Yves Losier

Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control—A Review

The recent introduction of novel multifunction hands as well as new control paradigms increase the demand for advanced prosthetic control systems. In this context, an unambiguous terminology and a good understanding of the nature of the control problem is important for efficient research and communication concerning the subject. Thus, one purpose of this paper is to suggest an unambiguous taxonomy, applicable to control systems for upper limb prostheses and also to prostheses in general. A functionally partitioned model of the prosthesis control problem is also presented along with the taxonomy. In the second half of the paper, the suggested taxonomy has been exploited in a comprehensive literature review on proportional myoelectric control of upper limb prostheses. The review revealed that the methods for system training have not matured at the same pace as the novel multifunction prostheses and more advanced intent interpretation methods. Few publications exist regarding the choice of training method and the composition of the training data set. In this context, the notion of outcome measures is essential. By definition, system training involves optimization, and the quality of the results depends heavily on the choice of appropriate optimization criteria. In order to further promote the development of proportional myoelectric control, these topics need to be addressed.

A Real-Time Pattern Recognition Based Myoelectric Control Usability Study Implemented in a Virtual Environment

Pattern recognition based myoelectric control systems have been well researched; however very few systems have been implemented in a clinical environment. Although classification accuracy or classification error is the metric most often reported to describe how well these control systems perform, very little work research has been conducted to relate this measure to the usability of the system. This work presents a virtual clothespin usability test to assess the performance of pattern recognition based myoelectric control systems. The results suggest that users can complete the virtual task in reasonable time frames when using systems with high classification accuracies. Additionally, results indicate that a clinically-supported classifier training approach (inclusion of the transient potion of contraction signals) may reduce classification accuracy but increase real-time performance.

A novel approach to surface electromyography: an exploratory study of electrode-pair selection based on signal characteristics

A 3 × 4 electrode array was placed over each of seven muscles and surface electromyography (sEMG) data were collected during isometric contractions. For each array, nine bipolar electrode pairs were formed off-line and sEMG parameters were calculated and evaluated based on repeatability across trials and comparison to an anatomically placed electrode pair. The use of time-domain parameters for the selection of an electrode pair from within a grid-like array may improve upon existing electrode placement methodologies.

Evaluation of shoulder complex motion-based input strategies for endpoint prosthetic-limb control using dual-task paradigm

This article describes the design and evaluation of two comprehensive strategies for endpoint-based control of multiarticulated powered upper-limb prostheses. One method uses residual shoulder motion position; the other solely uses myoelectric signal pattern classification. Both approaches are calibrated for individual users through a short training protocol. The control systems were assessed both quantitatively and qualitatively with use of a functional usability protocol based on a dual-task paradigm. The results revealed that the residual motion-based strategy outperformed the myoelectric signal-based scheme, while neither strategy appeared to significantly increase the mental burden demanded of the users.

A Bus-Based Smart Myoelectric Electrode/Amplifier—System Requirements

The evaluation of a bus-based smart myoelectric electrode/amplifier is described that is to be used in conjunction with a multi-function prosthetic hand controller. The smart electrode/amplifier was designed to meet power consumption and size specifications of commercially available myoelectric amplifiers used for prosthetic control applications while providing a number of additional features. This paper investigates the electrode/amplifier requirements for a pattern classifier system and compares them to those currently accepted within a clinical setting. System testing and evaluation was performed with both normally limbed subjects and traumatic amputees.

A Control System for a Powered Prosthesis Using Positional and Myoelectric Inputs from the Shoulder Complex

The integration of multiple input sources within a control strategy for powered upper limb prostheses could provide smoother, more intuitive multi-joint reaching movements based on the users intended motion. The work presented in this paper presents the results of using myoelectric signals (MES) of the shoulder area in combination with the position of the shoulder as input sources to multiple linear discriminant analysis classifiers. Such an approach may provide users with control signals capable of controlling three degrees of freedom (DOF). This work is another important step in the development of hybrid systems that will enable simultaneous control of multiple degrees of freedom used for reaching tasks in a prosthetic limb.

A bus-based smart myoelectric electrode/amplifier

The design of a bus-based smart myoelectric electrode/amplifier is described that is be used in conjunction with a multi-function prosthetic hand controller. This system incorporates several different control strategies including pattern classification of EMG to control a three axis hand (The UNB Hand) that can perform six basic grip patterns. The smart electrode/amplifier was designed to meet power consumption and size specifications of commercially available myoelectric amplifiers used for prosthetic control applications while providing a host of additional features. These features include a CAN bus communication protocol to alleviate wiring complexity, software programmable gain, distributed signal processing and electrode lift detection. In addition, this paper also compares the differences in requirements between a myoelectric electrode/amplifier used in a pattern classifier system to that used in a clinical setting. System testing and evaluation with normally limbed subjects and traumatic amputees are presented.

Clinical Investigation of High-Density Electromyography Data and Pattern Classification Accuracy for Prosthetic Control

ABSTRACT Introduction: Myoelectric prosthetic limbs have been well accepted by upper-limb amputees for many years, and advances in myoelectric control systems have increased the popularity of these devices. Pattern recognition–based control of powered upper-limb myoelectric prostheses offers a means of extracting more information from the available muscles than do conventional methods and therefore can be used to increase the number of functions in an artificial limb. Traditionally, surface electromyography (EMG) has been used to investigate muscle activation patterns to determine which areas of a residual limb would be appropriate for electrode placement and control. High-density EMG (HDEMG) systems have allowed for noninvasive collection of myoelectric signals (MES) from many closely spaced electrodes. The data obtained can be examined through the use of “color maps,” which provide a visual indication of the distribution and intensity of muscle activation. This technology is particularly suited for those with limited muscle physiology due to injury or loss. Materials and Methods: To further understand the activation patterns of amputees, this work focused on examining two types of contractions to determine the relationship, if any, between the color maps produced from HDEMG data and classification accuracy used for pattern recognition control. Understanding this relationship may help to develop better clinical training protocols for prosthesis users and also identify those individuals who would be the most suitable candidates for myoelectric prostheses. An HDEMG system (REFA, TMS International) was used to evaluate two common hand movements (“hand open” and “hand closed”) at a self-selected medium contraction level. Four transradial amputees (two with traumatic [TR] amputations and two with congenital [CG] amputations) participated in this study. Up to 32 surface electrodes were placed in a grid formation over the forearm region to collect data from the residual limb. The areas on the forearm that experienced muscle activity during given movements were illustrated in topographical (color) maps for each trial. The color maps were visually inspected to determine any changes in intensity (amplitude) and pattern repeatability between trials. Pattern classification accuracies were computed for both movements and compared with their corresponding color maps to observe any trends. Results: Both color map pattern and intensity changes were noted in relation to classification accuracy; however, a quantitative relationship between the two was not determined. Although the sample size is limited (n = 4), these observations were similar for those with CG and TR amputations. The results suggest that classification accuracy differs according to both pattern and intensity changes; however, the exact relationship remains elusive. Conclusions: Understanding this connection may help to determine which candidates are better suited for prostheses using pattern recognition versus those that may remain successful with traditional systems.

Analyzing Bus Load Data Using an FPGA and a Microcontroller

In this paper we present the design, implementation, and testing of an evaluation tool for the ongoing development of the Prosthetic Device Communication Protocol (PDCP) which is an open protocol and is featured in the University of New Brunswicks most recent prosthetic limb research project, the UNB Hand System. This prosthetic device utilizes the CAN bus hardware with the PDCP for passing command and data messages between modules within the prosthetic limb system. The PDCP allows abstraction of the underlying bus system and allows different network topologies depending on particular needs. To be able to analyze communication in the CAN layers as well as in the PDCP layer we present our own solutions utilizing an FPGA for CAN bus bandwidth load monitoring and a microcontroller for PDCP monitoring and analysis.