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Contrast of multi-resolution analysis approach to transhumeral phantom motion decoding

CAAI Transactions on Intelligence Technology 2021年第3期6卷 360-375页

作者： Ejay Nsugbe Oluwarotimi William Samuel mojisola grace asogbon Guanglin Li Independent Scholar Key Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences(CAS)Shenzhen Institutes of Advanced TechnologyShenzhenChina

In signal processing,multiresolution decomposition techniques allow for the separation of an acquired signal into sub levels,where the optimal level within the signal minimises redundancy,uncertainties,and contains the information required for the characterisation of the sensed phenomena.In the area of physiological signal processing for prosthesis control,scenarios where a signal decomposition analysis are required:the wavelet decomposition(WD)has been seen to be the favoured time-frequency approach for the decomposition of non-stationary signals.From a research perspective,the WD in certain cases has allowed for a more accurate motion intent decoding process following feature extraction and classification.Despite this,there is yet to be a widespread adaptation of the WD in a practical setting due to perceived computational complexity.Here,for neuro-muscular(electromyography)and brainwave(electroencephalography)signals acquired from a transhumeral amputee,a computationally efficient time domain signal decom-position method based on a series of heuristics was applied to process the acquired signals before feature extraction.The results showed an improvement in motion intent decoding prowess for the proposed time-domain-based signal decomposition across four different classifiers for both the neuromuscular and brain wave signals when compared to the WD and the raw signal.

关键词： motion approach analysis

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Phantom motion intent decoding for transhumeral prosthesis control with fused neuromuscular and brain wave signals

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IET Cyber-Systems and Robotics 2021年第1期3卷 77-88页

作者： Ejay Nsugbe Oluwarotimi Williams Samuel mojisola grace asogbon Guanglin Li Independent Researcher Chinese Academy of Sciences(CAS)Key Laboratory of Human-Machine Intelligence-Synergy System Shenzhen Institutes of Advanced TechnologyShenzhenChina Shenzhen College of Advanced Technology University of Chinese Academy of SciencesShenzhenChina

In recent years,the electroencephalography(EEG)brain-computer interface(BCI)has been researched in the area of upper-limb prosthesis control due to the promise of being able to record neurological signals which follow activation patterns in the cortex directly from the brain with non-invasive electrodes.This is seen as a way of bypassing the limitation posed by acquiring neuromuscular signals predominantly with electromyography(EMG)directly from the stump,which possesses residual limb anatomy post-amputation.In this study,the sequential forward selection algorithm to form a 10-optimal-channel representation,alongside an extended signal feature vector was applied,to investigate the motion intent decoding performance of EMG-only,EEG-only,and a fused EMG-EEG sensing configuration for four transhumeral amputees with varying stump lengths.The results showed a considerable improvement for the EMG-only configuration with the advanced feature vector,but only a small increase for the EEG-only,and thus a marginal improvement when information from both signals was fused together.This is likely due to the EEG requiring a greater number of channels spread across the skull to provide a reliable intent decoding.Further work will now involve optimisation studies to find a greater representation of electrode representation and parsimony,to minimise the number of channels while boosting motion intent decoding accuracy.

关键词： anatomy fused motion

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A study on preterm birth predictions using physiological signals,medical health record information and low-dimensional embedding methods

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IET Cyber-Systems and Robotics 2021年第3期3卷 228-244页

作者： Ejay Nsugbe Oluwarotimi William Samuel Ibrahim Sanusi mojisola grace asogbon Guanglin Li Nsugbe Research Labs SwindonUK Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences ShenzhenChina Department of Automatic Control and Systems Engineering The University of SheffieldSheffieldUK

Preterm births have been seen to have psychological and financial implications;current surveys suggest that amongst the various methods of preterm prediction,there is yet to exist a reliable and standard means of predicting preterm births.This study investigates the application of electrohysterogram and tocogram signals acquired at various points during the third pregnancy trimester,alongside information from the patients'medical health record regarding the pregnancy,towards preterm prediction and an associated delivery imminency timeline.In addition to this,the impact of both linear and non-linear dimensional embedding methods towards the preterm prediction is explored.The classification exercises were carried out using a support vector machine and decision tree,both of which have a certain degree of model interpretability and have potential to be introduced into a clinical operating framework.

关键词： cybernetics decision-making decision-tree classifier machine intelligence machine learning sensor fusion

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