Classification of hand and wrist tasks of unknown force levels using muscle synergies.

Muscle synergies have been proposed as a way for the central nervous system (CNS) to simplify the generation of motor commands and they have been shown to explain a large portion of the variation in the muscle patterns across a variety of conditions. However, whether human subjects are able to control prostheses proportionally with a small set of synergies has not been tested directly. Here we investigated if muscle synergies can be used to identify different wrist and hand motions. We recorded electromyographic (EMG) activity from eight arm muscles while the subjects exerted seven different intensity levels during the motions when performing seven classes of hand and wrist motion. From these data we extracted the muscle synergies and classified the tasks associated to each contraction intensity profile by linear discriminant analysis (LDA). We compared the performance obtained using muscle synergies with the performance of using the mean absolute values (MAV) as a feature. Also, the consistency of extracted muscle synergies was studied across intensity variations. While the synergies showed relative consistency particularly across closer intensity levels, average classification results generated with the synergies were less accurate than MAVs. These results indicate that although the performance of muscle synergies was very close to MAVs, they do not provide additional information for task identification across different exerted intensity levels.

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 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 user's 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.

Principal components analysis preprocessing to reduce controller delays in pattern recognition based myoelectric control.

Information extracted from signals recorded from multi-channel surface myoelectric signal (MES) recording sites can be used as inputs to control systems for powered prostheses. For small, closely spaced muscles, such as the muscles in the forearm, the detected MES often contains contributions from more than one muscle; the contribution from each specific muscle being modified by a tissue filter between the muscle and the detection points. In this work, the measured raw MES signals are rotated by class specific rotation matrices to spatially decorrelate the measured data prior to feature extraction. This tunes the pattern recognition classifier to better discriminate the test motions. Using this preprocessing step, MES analysis windows may be cut from 256 ms to 128 ms without affecting the classification accuracy.

Myoelectric signal processing for control of powered limb prostheses.

Progress in myoelectric control technology has over the years been incremental, due in part to the alternating focus of the R&D between control methodology and device hardware. The technology has over the past 50 years or so moved from single muscle control of a single prosthesis function to muscle group activity control of multifunction prostheses. Central to these changes have been developments in the means of extracting information from the myoelectric signal. This paper gives an overview of the myoelectric signal processing challenge, a brief look at the challenge from an historical perspective, the state-of-the-art in myoelectric signal processing for prosthesis control, and an indication of where this field is heading. The paper demonstrates that considerable progress has been made in providing clients with useful and reliable myoelectric communication channels, and that exciting work and developments are on the horizon.

The effect of electrode displacements on pattern recognition based myoelectric control.

Pattern recognition based myoelectric controllers rely on a fundamental assumption that the patterns detected under a given electrode are repeatable for a given state of muscle activation. Consequently, electrode displacements on the skins surface affect the classification accuracy of the pattern based myoelectric controller. The effects of electrode displacement can be mitigated by using a training set of data which consists of patterns detected over a range of plausible displacement locations to train the control system.

Pattern recognition of single and combined motions from the shoulder complex.

Much research has been done towards developing control systems for artificial hands, elbows, and wrists based on the myoelectric signal (MES). While great effort has gone into developing pattern recognition based control systems for these devices, very little attention has been devoted to the shoulder. This is in part because the majority of amputees are either below elbow or mid-humeral, and true shoulder disarticulations are rare. However, as the level of limb loss increases so does the need for functional replacement. This study investigates pattern recognition concepts for independent control of an artificial shoulder.

Using telehealth technology to improve the delivery of health services to people who are deaf.

The use of technology to access sign language interpreters from a remote location can have a significant impact on the timely access of such services for people who are deaf. The potential integration of such services is contingent on factors such as the availability of suitable equipment and the acceptance of the technological solution by people who are deaf, sign language interpreters and the health professionals. A system was assembled to address the needs of the users while maintaining focused on the requirement of the system being feasible such that it remains an option for small clinics and even medical offices. The technological solution was tested using simulated sessions involving people who are deaf, health professionals and sign language interpreters. The sessions simulated typical health conditions seen in hospital emergency rooms, medical clinics and doctor offices. Data collected from all participants indicate the technology proved to be acceptable in most simulated situations.

Optimized Gaussian mixture models for upper limb motion classification.

This paper introduces the use of Gaussian mixture models (GMM) for discriminating multiple classes of limb motions using continuous myoelectric signals (MES). The purpose of this work is to investigate an optimum configuration of a GMM-based limb motion classification scheme. For this effort, a complete experimental evaluation of the Gaussian mixture motion model is conducted on a 12-subject database. The experiments examine algorithmic issues of the GMM including the model order selection and variance limiting. The final classification performance of this GMM system has been compared with that of three other classifiers (a linear discriminant analysis (LDA), a linear perceptron neural network (LP) and a multilayer perceptron (MLP) neural network) . The Gaussian mixture motion model attains 96.3% classification accuracy using four channel MES for distinguishing six limb motions and is shown to outperform the other motion modeling techniques on an identical six limb motion task.

User satisfaction with rehabilitation services delivered using Internet video.

Rehabilitation services to four remote sites in New Brunswick were delivered via PC-based videoconferencing equipment, using ADSL connections to the Internet. Approximately 40 people used the equipment over 18 months. There were 32 videoconference sessions. A total of 60 questionnaires were returned (a 94% response rate). In 31 of the 32 videoconferences, a connection was successfully established between the computers. The videoconferences lasted on average 20 min. The most frequent applications were viewing of rehabilitative equipment and video communication. The technology was found to be useful and provided an enhanced form of communication from the video component. There were some problems with the stability and reliability of the equipment.

Intelligent multifunction myoelectric control of hand prostheses.

Intuitive myoelectric prosthesis control is difficult to achieve due to the absence of proprioceptive feedback, which forces the user to monitor grip pressure by visual information. Existing myoelectric hand prostheses form a single degree of freedom pincer motion that inhibits the stable prehension of a range of objects. Multi-axis hands may address this lack of functionality, but as with multifunction devices in general, serve to increase the cognitive burden on the user. Intelligent hierarchical control of multiple degree-of-freedom hand prostheses has been used to reduce the need for visual feedback by automating the grasping process. This paper presents a hybrid controller that has been developed to enable different prehensile functions to be initiated directly from the user's myoelectric signal. A digital signal processor (DSP) regulates the grip pressure of a new six-degree-of-freedom hand prosthesis thereby ensuring secure prehension without continuous visual feedback.

Myo-electric signals to augment speech recognition.

It is proposed that myo-electric signals can be used to augment conventional speech-recognition systems to improve their performance under acoustically noisy conditions (e.g. in an aircraft cockpit). A preliminary study is performed to ascertain the presence of speech information within myo-electric signals from facial muscles. Five surface myo-electric signals are recorded during speech, using Ag-AgCl button electrodes embedded in a pilot oxygen mask. An acoustic channel is also recorded to enable segmentation of the recorded myo-electric signal. These segments are processed off-line, using a wavelet transform feature set, and classified with linear discriminant analysis. Two experiments are performed, using a ten-word vocabulary consisting of the numbers 'zero' to 'nine'. Five subjects are tested in the first experiment, where the vocabulary is not randomised. Subjects repeat each word continuously for 1 min; classification errors range from 0.0% to 6.1%. Two of the subjects perform the second experiment, saying words from the vocabulary randomly; classification errors are 2.7% and 10.4%. The results demonstrate that there is excellent potential for using surface myo-electric signals to enhance the performance of a conventional speech-recognition system.

A wavelet-based continuous classification scheme for multifunction myoelectric control.

This work represents an ongoing investigation of dexterous and natural control of powered upper limbs using the myoelectric signal. When approached as a pattern recognition problem, the success of a myoelectric control scheme depends largely on the classification accuracy. A novel approach is described that demonstrates greater accuracy than in previous work. Fundamental to the success of this method is the use of a wavelet-based feature set, reduced in dimension by principal components analysis. Further, it is shown that four channels of myoelectric data greatly improve the classification accuracy, as compared to one or two channels. It is demonstrated that exceptionally accurate performance is possible using the steady-state myoelectric signal. Exploiting these successes, a robust online classifier is constructed, which produces class decisions on a continuous stream of data. Although in its preliminary stages of development, this scheme promises a more natural and efficient means of myoelectric control than one based on discrete, transient bursts of activity.

Nonlinear adaptive filtering of stimulus artifact.

Noninvasive measurements of somatosensory evoked potentials have both clinical and research applications. The electrical artifact which results from the stimulus is an interference which can distort the evoked signal, and introduce errors in response onset timing estimation. Given that this interference is synchronous with the evoked signal, it cannot be reduced by the conventional technique of ensemble averaging. The technique of adaptive noise cancelling has potential in this regard however, and has been used effectively in other similar problems. An adaptive noise cancelling filter which uses a neural network as the adaptive element is investigated in this application. The filter is implemented and performance determined in the cancelling of artifact for in vivo measurements on the median nerve. A technique of segmented neural network training is proposed in which the network is trained on that segment of the record time window which does not contain the evoked signal. The neural network is found to generalize well from this training to include the segment of the window containing the evoked signal. Both quantitative and qualitative measures show that significant stimulus artifact reduction is achieved.

Classification of the myoelectric signal using time-frequency based representations.

An accurate and computationally efficient means of classifying surface myoelectric signal patterns has been the subject of considerable research effort in recent years. Effective feature extraction is crucial to reliable classification and, in the quest to improve the accuracy of transient myoelectric signal pattern classification, an ensemble of time-frequency based representations are proposed. It is shown that feature sets based upon the short-time Fourier transform, the wavelet transform, and the wavelet packet transform provide an effective representation for classification, provided that they are subject to an appropriate form of dimensionality reduction.

Errors associated with the use of adaptive differential pulse code modulation in the compression of isometric and dynamic myo-electric signals.

Muscle activity produces an electrical signal termed the myo-electric signal (MES). The MES is a useful clinical tool, used in diagnostics and rehabilitation. This signal is typically stored in 2 bytes as 12-bit data, sampled at 3 kHz, resulting in a 6 kbyte s-1 storage requirement. Processing MES data requires large bit manipulations and heavy memory storage requirements. Adaptive differential pulse code modulation (ADPCM) is a popular and successful compression technique for speech. Its application to MES would reduce 12-bit data to a 4-bit representation, providing a 3:1 compression. As, in most practical applications, memory is organised in bytes, the realisable compression is 4:1, as pairs of data can be stored in a single byte. The performance of the ADPCM compression technique, using a real-time system at 1 kHz, 2 kHz and 4 kHz sampling rates, is evaluated. The data used include MES from both isometric and dynamic contractions. The percent residual difference (PRD) between an unprocessed and processed MES is used as a performance measure. Errors in computed parameters, such as median frequency and variance, which are used in clinical diagnostics, and waveform features employed in prosthetic control are also used to evaluate the system. The results of the study demonstrate that the ADPCM compression technique is an excellent solution for relieving the data storage requirements of MES both in isometric and dynamic situations.

Two-channel enhancement of a multifunction control system.

The enhancement of an existing myoelectric control system has been investigated. The original one-channel system used an artificial neural network to classify myoelectric patterns. This research shows that a two-channel control system can improve the classification accuracy of the pattern classifier significantly, thus improving the reliability of the prosthesis.

A new strategy for multifunction myoelectric control.

This paper describes a novel approach to the control of a multifunction prosthesis based on the classification of myoelectric patterns. It is shown that the myoelectric signal exhibits a deterministic structure during the initial phase of a muscle contraction. Features are extracted from several time segments of the myoelectric signal to preserve pattern structure. These features are then classified using an artificial neural network. The control signals are derived from natural contraction patterns which can be produced reliably with little subject training. The new control scheme increases the number of functions which can be controlled by a single channel of myoelectric signal but does so in a way which does not increase the effort required by the amputee. Results are presented to support this approach.