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Transhumeral amputees account for the largest cohort of upper-limb amputees missing a substantial amount of their upper-limb, as per combined statistics with the UK and Italy. In this work, we utilise the human motor control theory, and Penfield homunculus as a basis for providing a review and school of thought behind phantom limb sensations, pain and associated therapy. Clinical work was also conducted on five non-amputated individuals using ultrasound imaging along the humerus while participants were instructed to produce a number of hand movements. This set of results has thus suggested that mainly compound gesture motions, which involve a degree of bulk muscular recruitment, can be detected along the humerus. It is foreseen that this set of gestures can be used to explore mobility and sensation of phantom limbs by clinical rehabilitation prosthetists.

Polycystic ovary syndrome (PCOS) can affect a female’s reproductive system and comes with associated complications to the endocrine system of the affected individual. The diagnosis success of the condition varies depending on the stage of the disease. Thus, there is a need for investment in additional technologies that can help bolster the overall diagnosis success of the condition and to cue in prompt care strategies. A substantial amount of work has been done on this, where artificial intelligence technology has been investigated around the exploitation of patient medical health records towards predicting whether a patient is carrying the PCOS condition. The shortcomings associated with this related literature are based on the use of an unbalanced dataset towards the training of the candidate models, which can induce a form of model bias and approach the problem as a binary-based prediction exercise. This study aims at providing a solution to this apparent gap in knowledge by designing a prediction model using the Kaggle PCOS dataset, which is initially balanced using a synthetic sample generation algorithm. Next, a probability-based inference system is designed to estimate and stage the degree of the PCOS condition in the patient.

Preterm births are one of the main causes of death in children under the age of 5; they carry financial implications to the economy and cause exceptional psychological distress to mothers and their families. Prior work has been done in the application of classification methods towards predicting whether a pregnant patient is likely to deliver preterm, but the majority of these solutions have been done separately without full consideration of how their proposed solution can be integrated into a clinical system setup. In this work, we propose a multi order cybernetic framework to design a recommender system that can be used to form a closed loop clinical interaction poised towards steering a system from its current state into a more desirable outcome. Using fused estimates from both electrohysterogram (electrophysiology) and tocogram (mechanical) signals from uterine wall contractions, a classification machine was designed to classify between Preterm/Term states, and also predict an associated delivery imminency for the pregnant patient. The classification machine was implemented using a multilayer perceptron neural network (MLP) and a support vector machine (SVM), where it was seen that the SVM outperformed the MLP in the majority of the classification tasks. Further work in this area would involve the application of regression techniques towards the classification tasks, which is expected to also provide greater model interpretability and continuous state estimation.

This paper investigates the application of magnetomyography (MMG) signals from uterine contractions in pregnant patients towards the prediction of labour imminency within or above 48 h. The study utilised the MMG signals collected from a host of pregnant patients retrieved from a Physionet database, which also contained information regarding patients’ ethnicity and pregnancy. Utilising​ the information available in addition to the dataset, the study investigated the prospect of designing an ethnic specific labour imminency classifier to allow for an enhanced prediction, with an emphasis on Black and Caucasian ethnicities due to the nature of the data. Using an extended feature vector and a support vector machine (SVM) classifier, it was seen that the labour imminency was enhanced across the various classifier metrics considered in the ethnic specific classifier when compared with the generalised classifier. The results from the classification exercise, which considered the fusion of MMG signal information with the information on patients’ records, showed greater variability and a slightly lower classifier performance, thus suggesting that the MMG signals present a more reliable way of classifier training. Subsequent work in this area would now involve the application of optimisation algorithms to select an optimal number of electrodes that can be used for data acquisition, and thereby contributing towards the lowering of the cost associated with the implementation of the method using the MMG instrumentation.