ABSTRACT
The recording and analysis of peripheral neural signal can provide insight for various prosthetic and bioelectronics medicine applications. However, there are few studies that investigate how informative features can be extracted from population activity electroneurographic (ENG) signals. In this study, five feature extraction frameworks were implemented on sensory ENG datasets and their classification performance was compared. The datasets were collected in acute rat experiments where multi-channel nerve cuffs recorded from the sciatic nerve in response to proprioceptive stimulation of the hindlimb. A novel feature extraction framework, which incorporates spatio-temporal focus and dynamic time warping, achieved classification accuracies above 90% while keeping a low computational cost. This framework outperformed the remaining frameworks tested in this study and has improved the discrimination accuracy of the sensory signals. Thus, this study has extended the tools available to extract features from sensory population activity ENG signals. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.
Subject(s)
Sciatic Nerve , Animals , Rats , Sciatic Nerve/physiologyABSTRACT
BACKGROUND: Existing trans-radial prosthetic socket designs are not optimised to facilitate reliable myoelectric control. Many socket designs pre-date the introduction of myoelectric devices. However, socket designs featuring improved biomechanical stability, notably longitudinal compression sockets, have emerged in more recent years. Neither the subsequent effects, if any, of stabilising the limb on myoelectric control nor in which arrangement to apply the compression have been reported. METHODOLOGY: Twelve able-bodied participants completed two tasks whilst wearing a longitudinal compression socket simulator in three different configurations: 1) compressed, where the compression strut was placed on top of the muscle of interest, 2) relief, where the compression struts were placed either side of the muscle being recorded and 3) uncompressed, with no external compression. The tasks were 1) a single-channel myoelectric target tracking exercise, followed by 2), a high-intensity grasping task. The wearers' accuracy during the tracking task, the pressure at opposing sides of the simulator during contractions and the rate at which the limb fatigued were observed. FINDINGS: No significant difference between the tracking-task accuracy scores or rate of fatigue was observed for the different compression configurations. Pressure recordings from the compressed configuration showed that pressure was maintained at opposing sides of the simulator during muscle contractions. CONCLUSION: Longitudinal compression does not inhibit single-channel EMG control, nor improve fatigue performance. Longitudinal compression sockets have the potential to improve the reliability of multi-channel EMG control due to the maintenance of pressure during muscle contractions.
ABSTRACT
We describe a novel application of Higher Order Statistics (HOS) for classifying Surface Electromyogram (sEMG) signals. We have followed seven approaches to identify discriminating signals representative of four primitive motions, i.e., elbow flexion/extension and forearm supination/pronation. The Sequential Forward Selection (SFS) method is utilized to reduce the number of HOS features to a sufficient minimum while retaining their discriminatory information. The SFS selected the kurtosis of sEMG as well as its second order statistics as discriminating features. Our method is robust, and does not require additional computations as compared to existing efficient methods for providing higher rates of correct classification of sEMG, which make it useful in practical sEMG' controlled prostheses.