Myoelectric signal processing for control of powered limb prostheses

Parker, P.A.; Englehart, Kevin; Hudgins, B.

doi:10.1016/j.jelekin.2006.08.006

Cited by 570 publications

(325 citation statements)

References 19 publications

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“…First, we applied methods for signal classification, which are dominant in the scientific literature on myocontrol 29 , to the decoded series of discharge timings. The classification results indicated an almost perfect discrimination (on average >97%) of up to 11 classes.…”

Section: Methodsmentioning

confidence: 99%

Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

et al. 2017

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The intuitive control of upper - limb prostheses requires a man/machine interface that directly exploits biological signals. Here, we define and experimentally test an offline man/machine interface that takes advantage of the discharge timings of spinal moto r neurons. The motor - neuron behaviour is identified by deconvolution of the electrical activity of muscles reinnervated by nerves of a missing limb in patients with amputation at the shoulder or humeral level. We mapped the series of motor - neuron discharge s into control commands across multiple degrees of freedom via the offline application of direct proportional control, pattern recognition and musculoskeletal modelling. A series of experiments performed on six patients reveal that the man/machine interfac e has superior offline performance than conventional direct electromyographic control applied after targeted muscle innervation. The combination of surgical procedures, decoding and mapping into effective commands constitutes an interface with the output l ayers of the spinal cord circuitry that allows for the intuitive control of multiple degrees of freedom

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Section: Methodsmentioning

confidence: 99%

Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

et al. 2017

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“…A central problem in robotic and prosthetic grasping is to obtain information on the object that has to be grasped: its shape, weight, and intended use are all factors determining the position and exerted force of the fingers grasping the object; see, e.g., Cutkosky and Howe (1990) for an overview of human grasping studies and challenges in grasping with artificial hands. Even though research on the control of active hand prosthesis controlled with sEMG signals made considerable progress recently (see, e.g., Parker et al (2006) for an overview), this research mainly concentrates on recognizing different finger movements.…”

Section: Introductionmentioning

confidence: 99%

Relation between object properties and EMG during reaching to grasp

Fligge

Urbanek

Smagt

2013

Journal of Electromyography and Kinesiology

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a b s t r a c tIn order to stably grasp an object with an artificial hand, a priori knowledge of the object's properties is a major advantage, especially to ensure subsequent manipulation of the object held by the hand. This is also true for hand prostheses: pre-shaping of the hand while approaching the object, similar to able-bodied, allows the wearer for a much faster and more intuitive way of handling and grasping an object. For hand prostheses, it would be advantageous to obtain this information about object properties from a surface electromyography (sEMG) signal, which is already present and used to control the active prosthetic hand.We describe experiments in which human subjects grasp different objects at different positions while their muscular activity is recorded through eight sEMG electrodes placed on the forearm. Results show that sEMG data, gathered before the hand is in contact with the object, can be used to obtain relevant information on object properties such as size and weight.

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“…A notch filter at 60Hz removes power line noise and a high pass filter with a cutoff frequency of 10Hz removes motion artifacts. The next step is to estimate the signal strength by extracting the mean absolute value (MAV) as described in [1]. In order to do this the signal is rectified and averaged using a moving average filter that uses 400 points.…”

Section: Emg Acquisition and Controlmentioning

confidence: 99%

“…For example an above elbow patient could use their biceps to control hand opening and their triceps to control hand closing. In the literature this type of controller is known as a two-state amplitude modulation controller [1]. Since the patient is limited by the number of muscle sites available and higher level amputees have less muscle sites only a limited number of degrees of freedom (DoF) on an actuator can be controlled at a time.…”

Section: Introductionmentioning

confidence: 99%

The Development of a Myoelectric Training Tool for Above-Elbow Amputees

Dawson¹,

Fahimi²,

Carey³

2012

TOBEJ

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The objective of above-elbow myoelectric prostheses is to reestablish the functionality of missing limbs and increase the quality of life of amputees. By using electromyography (EMG) electrodes attached to the surface of the skin, amputees are able to control motors in myoelectric prostheses by voluntarily contracting the muscles of their residual limb. This work describes the development of an inexpensive myoelectric training tool (MTT) designed to help upper limb amputees learn how to use myoelectric technology in advance of receiving their actual myoelectric prosthesis. The training tool consists of a physical and simulated robotic arm, signal acquisition hardware, controller software, and a graphical user interface. The MTT improves over earlier training systems by allowing a targeted muscle reinnervation (TMR) patient to control up to two degrees of freedom simultaneously. The training tool has also been designed to function as a research prototype for novel myoelectric controllers. A preliminary experiment was performed in order to evaluate the effectiveness of the MTT as a learning tool and to identify any issues with the system. Five able-bodied participants performed a motor-learning task using the EMG controlled robotic arm with the goal of moving five balls from one box to another as quickly as possible. The results indicate that the subjects improved their skill in myoelectric control over the course of the trials. A usability survey was administered to the subjects after their trials. Results from the survey showed that the shoulder degree of freedom was the most difficult to control.

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Myoelectric signal processing for control of powered limb prostheses

Cited by 570 publications

References 19 publications

Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

Relation between object properties and EMG during reaching to grasp

The Development of a Myoelectric Training Tool for Above-Elbow Amputees

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