Classification of motor commands using a modified self-organising feature map

Jin

Journal of Biomechanics

et al. 2009

Neuroprostheses, implantable or non-invasive ones, are promising techniques to enable paralyzed individuals with conditions, such as spinal cord injury or spina bifida (SB), to control their limbs voluntarily. Direct cortical control of invasive neuroprosthetic devices and robotic arms have recently become feasible for primates. However, little is known about designing non-invasive, closed-loop neuromuscular control strategies for neural prostheses. Our goal was to investigate if an Artificial Neural Network-based (ANN-based) model for closed-loop controlled neural prostheses could use neuromuscular activation recorded from individuals with impaired spinal cord to predict their endpoint gait parameters (such as stride length and step width). We recruited 12 persons with SB (5 females and 7 males) and collected their neuromuscular activation and end-point gait parameters during overground walking. Our results show that the proposed ANN-based technique can achieve a highly accurate prediction (e.g., R-values of 0.92 -0.97, ANN (tansig+tansig) for single composition of data sets) for altered end-point locomotion. Compared to traditional robust regression, this technique can provide up to 80% more accurate prediction. Our results suggest that more precise control of complex neural prostheses during locomotion can be achieved by engaging neuromuscular activity as intrinsic feedback to generate end-point leg movement. This ANN-based model allows a seamless incorporation of neuromuscular activity, detected from paralyzed individuals, to adaptively predict their altered gait patterns, which can be employed to provide closed-loop feedback information for neural prostheses.

Section: Methodsmentioning

confidence: 99%

From neuromuscular activation to end-point locomotion: An artificial neural network-based technique for neural prostheses

Jin

Journal of Biomechanics

et al. 2009

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

“…In particular, several EMG based systems were proposed for estimating the hand and wrist motions and were used consequently as an interface for controlling wrist exoskeletons [1], [2], or prosthetic devices used in the hand and wrist [3], [4], for teleoperating robotic arms [3], [5], or in a virtual environment to control computer-animated hands [6]. In order to estimate and control the wrist motion, people have primarily focused on classification-based methods.…”

Section: Introductionmentioning

confidence: 99%

“…In [3], [5], the authors use different machine learning techniques to discriminate the EMG patterns related to eight different gestures, adding to the previous six gestures the wrist abduction and adduction movements. Similarly, in [4], [6], [8], the authors propose methods for classifying ten different types of movements including flexion/extension of the wrist as well as flexion/extension of a different combination of fingers. All these approaches investigate a discrete classification of the wrist or hand/fingers opening and closing.…”

Section: Introductionmentioning

confidence: 99%

EMG-based learning approach for estimating wrist motion

El-Khoury

Batzianoulis

Antuvan

et al. 2015

Abstract-This paper proposes an EMG based learning approach for estimating the displacement along the 2-axes (abduction/adduction and flexion/extension) of the human wrist in real-time. The algorithm extracts features from the EMG electrodes on the upper and forearm and uses Support Vector Regression to estimate the intended displacement of the wrist. Using data recorded with the arm outstretched in various locations in space, we train the algorithm so as to allow robust prediction even when the subject moves his/her arm across several positions in space. The proposed approach was tested on five healthy subjects and showed that a R 2 index of 63.6% is obtained for generalization across different arm positions and wrist joint angles.

BioMed Research International

“…There are also problems with the attachments of the electrodes to the skin. Still, it has been demonstrated that EMG signals can be used to control a hand prosthesis with several degrees of freedom [9, 10]. …”

Section: Introductionmentioning

confidence: 99%

The Multiple Silicone Tube Device, “Tubes within a Tube,” for Multiplication in Nerve Reconstruction

Johansson

Dahlin

2014

Multiple nerve branches were created during the regeneration procedure after a nerve injury and such multiple branches are suggested to be used to control, for example, prosthesis with many degrees of freedom. Transected rat sciatic nerve stumps were inserted into a nine mm long silicone tube, which contained four, five mm long, smaller tubes, thus leaving a five mm gap for regenerating nerve fibers. Six weeks later, several new nerve structures were formed not only in the four smaller tubes, but also in the spaces in-between. The 7–9 new continuous nerve structures, which were isolated as individual free nerves after removal of the tubes, were delineated by a perineurium and contained both myelinated and unmyelinated nerve fibers as well as blood vessels. Stimulation of the proximal nerve elicited contractions in distal muscles. Thin metal electrodes, inserted initially into the smaller tubes in some experiments, became embedded in the new nerve structures and when stimulated contractions of the distal muscles were observed. The “tubes within a tube” technique, creating multiple new nerves from a single “mother” nerve, can be used to record multiple signals for prosthetic device control or as sources for supply of multiple denervated targets.