A computational model and simulation study of the efferent activity in the brachial nerves during voluntary motor intent

Bidirectional Control of Myoelectric Prostheses in Upper Limb Amputees: Current Results and Expectations

The most common causes of upper-limb amputations include traumatic etiology and malignity, followed by peripheral vascular diseases. Prosthetic fitting along with conducting a rehabilitation program provide the greatest possible degree of independence to the upper-limb amputees in performing their daily tasks, occupational, recreational and work activities. Despite recent advance in strategies of design and control, the lack of sensory feedback is, according to patients, one of the most important characteristics lacked by commercial myoelectric prostheses. This reason has led to the need for the development of comprehensive prosthetic part which would provide intuitive control and realistic sensory feedback to the amputees enabling them thus to more easily accomplish the tasks which are essential for easier performance of activities of daily life. Electromyography, and recently, electroneurography signals have been used for the development of more efficacious upper-limb prosthetic control. Several recent studies have demonstrated the efficacy of homologous and somatotopic approach in upper-limb amputees, by applying implanted and surface electrodes. This work presents novel methods for effective bidirectional control of myoelectric prostheses in patients with upper-limb amputations using motor control and sensory feedback. The above-mentioned approaches are applicable and have good prospects in further clinical use. The intraneural, extraneural and surface approach can be more or less applicable depending on the etiology and the level of amputation. From a clinical point of view, various approaches should be combined for obtaining more efficient control of bidirectional prostheses and corresponding sensory feedback.

Identification and classification of upper limb motions using PCA

This paper describes the utility of principal component analysis (PCA) in classifying upper limb signals. PCA is a powerful tool for analyzing data of high dimension. Here, two different input strategies were explored. The first method uses upper arm dual-position-based myoelectric signal acquisition and the other solely uses PCA for classifying surface electromyogram (SEMG) signals. SEMG data from the biceps and the triceps brachii muscles and four independent muscle activities of the upper arm were measured in seven subjects (total dataset=56). The datasets used for the analysis are rotated by class-specific principal component matrices to decorrelate the measured data prior to feature extraction.

A functional model and simulation of spinal motor pools and intrafascicular recordings of motoneuron activity in peripheral nerve

Decoding motor intent from recorded neural signals is essential for the development of effective neural-controlled prostheses. To facilitate the development of online decoding algorithms we have developed a software platform to simulate neural motor signals recorded with peripheral nerve electrodes, such as longitudinal intrafascicular electrodes (LIFEs). The simulator uses stored motor intent signals to drive a pool of simulated motoneurons with various spike shapes, recruitment characteristics, and firing frequencies. Each electrode records a weighted sum of a subset of simulated motoneuron activity patterns. As designed, the simulator facilitates development of a suite of test scenarios that would not be possible with actual data sets because, unlike with actual recordings, in the simulator the individual contributions to the simulated composite recordings are known and can be methodically varied across a set of simulation runs. In this manner, the simulation tool is suitable for iterative development of real-time decoding algorithms prior to definitive evaluation in amputee subjects with implanted electrodes. The simulation tool was used to produce data sets that demonstrate its ability to capture some features of neural recordings that pose challenges for decoding algorithms.

Control of hand prostheses using peripheral information

Several efforts have been carried out to enhance dexterous hand prosthesis control by impaired individuals. Choosing which voluntary signal to use for control purposes is a critical element to achieve this goal. This review presents and discusses the recent results achieved by using electromyographic signals, recorded either with surface (sEMG) or intramuscular (iEMG) electrodes, and electroneurographic (ENG) signals. The potential benefits and shortcomings of the different approaches are described with a particular attention to the definition of all the steps required to achieve an effective hand prosthesis control in the different cases. Finally, a possible roadmap in the field is also presented.