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| 1 | Contrast of multi-resolution analysis approach to transhumeral phantom motion decoding显示文摘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. | Ejay Nsugbe Oluwarotimi William Samuel Mojisola Grace Asogbon Guanglin Li | 2021 | CAAI Transactions on Intelligence Technology2021，6，3： | 0 |
| 2 | Shoulder girdle recognition using electrophysiological and low frequency anatomical contraction signals for prosthesis control显示文摘Shoulder disarticulation amputees account for a small portion of upper-limb amputees,thus little emphasis has been devoted to developing functional prosthesis for this cohort of amputees.In this study,shoulder girdle recognition was investigated with acquired data from electrophysiological(electromyography[EMG])and low frequency contraction(accelerometer[Acc])signals from both amputee and non-amputee participants.The contribution of this study is based around the contrast of the classification accuracy(CA)for different sensor configurations using a unique set of signal features.It was seen that the fusion of the EMG-Acc produced an enhancement in the CA in the range of 10%-20%,depending on which windowing parameters were considered.From this,it was seen that the best combination of a windowing scheme and classifier would likely be for the 350 ms and spectral regression discriminant analysis,with a fusion of the EMG-Acc information.The results have thus provided evidence that the two sensors can be combined and used in practice for prosthesis control.Taking a holistic view on the study,the authors conclude by providing a framework on how the shoulder motion recognition could be combined with neuromuscular reprogramming to contribute towards easing the cognitive burden of amputees during the prosthesis control process. | Ejay Nsugbe Ali H.Al-Timemy | 2022 | CAAI Transactions on Intelligence Technology2022，7，1： | 0 |
| 3 | A study on preterm birth predictions using physiological signals,medical health record information and low-dimensional embedding methods显示文摘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. | Ejay Nsugbe Oluwarotimi William Samuel Ibrahim Sanusi Mojisola Grace Asogbon Guanglin Li | 2021 | IET Cyber-Systems and Robotics2021，3，3： | 0 |
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