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| 1 | Gesture recognition for transhumeral prosthesis control using EMG and NIR显示文摘A key challenge associated with myoelectric prosthesis limbs is the acquisition of a good quality gesture intent signal from the residual anatomy of an amputee.In this study,the authors aim to overcome this limitation by observing the classification accuracy of the fusion of wearable electromyography(EMG)and near-infrared(NIR)to classify eight hand gesture motions across 12 able-bodied participants.As part of the study,they investigate the classification accuracy across a multi-layer perceptron neural network,linear discriminant analysis and quadratic discriminant analysis for different sensing configurations,i.e.EMG-only,NIR-only and EMG-NIR.A separate offline ultrasound scan was conducted as part of the study and served as a ground truth and contrastive basis for the results picked up from the wearable sensors,and allowed for a closer study of the anatomy along the humerus during gesture motion.Results and findings from the work suggest that it could be possible to further develop transhumeral prosthesis using affordable,ergonomic and wearable EMG and NIR sensing,without the need for invasive neuromuscular sensors or further hardware complexity. | Ejay Nsugbe Carol Phillips Mike Fraser Jess McIntosh | 2020 | IET Cyber-Systems and Robotics2020，2，3： | 3 |
| 2 | Phantom motion intent decoding for transhumeral prosthesis control with fused neuromuscular and brain wave signals显示文摘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. | Ejay Nsugbe Oluwarotimi Williams Samuel Mojisola Grace Asogbon Guanglin Li | 2021 | IET Cyber-Systems and Robotics2021，3，1： | 2 |
| 3 | 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 |
| 4 | 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 |
| 5 | 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 |
| 6 | Towards the use of cybernetics for an enhanced cervical cancer care strategy显示文摘Background Cervical cancer is a prominent disease in women,with a high mortality rate worldwide.This cancer continues to be a challenge to concisely diagnose,especially in its early stages.The aim of this study was to pro-pose a unique cybernetic system which showcased the human-machine collaboration forming a superintelligence framework that ultimately allowed for greater clinical care strategies.Methods In this work,we applied machine learning(ML)models on 650 patients’data collected from Hospital Universitario de Caracas in Caracas,Venezuela,where ethical approval and informed consent were granted.The data were hosted at the University of California at Irvine(UCI)database for cancer prediction by using data purely from a patient questionnaire that include key cervical cancer drivers such as questions on sexually transmitted diseases and time since first intercourse in order to design a clinical prediction machine that can predict various stages of cervical cancer.Two contrasting methods are explored in the design of a ML-driven prediction machine in this study,namely,a probabilistic method using Gaussian mixture models(GMM),and fuzziness-based reasoning using the fuzzy c-means(FCM)clustering on the data from 650 patients.Results The models were validated using a K-Fold validation method,and the results show that both meth-ods could be feasibly deployed in a clinical setting,with the probabilistic method(produced accuracies of 80+%/classifier dependent)allowing for more detail in the grading of a potential cervical cancer prediction,albeit at the cost of greater computation power;the FCM approach(produced accuracies around 90+%/classifier dependent)allows for a more parsimonious modelling with a slightly reduced prediction depth in comparison.As part of the novelty of this work,a clinical cybernetic system is also proposed to host the prediction machine,which allows for a human-machine collaborative interaction and an enhanced decision support platform to aug-ment overall care strategies.Conclusion The present study showcased how the use of prediction machines can contribute towards early de-tection and prioritised care of patients with cervical cancer,while also allowing for cost-saving benefits when compared with routine cervical cancer screening.Further work in this area would now involve additional vali-dation of the proposed clinical cybernetic loop and further improvement to the prediction machine by exploring non-linear dimensional embedding and clustering methods. | Ejay Nsugbe | 2022 | Intelligent Medicine2022，2，3： | 0 |
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