The Prosthetic Mobility Questionnaire (PMQ 2.0) represents a reliable solution for evaluating amputees' self-perceived mobility. The study aimed to evaluate the perceived mobility of middle-aged users with a traumatic amputation using the PMQ 2.0 and to assess the influence of age, stump and phantom limb pain, amputation level, time since amputation, and prosthesis use on it. Fifty subjects were recruited. The median value of the score was higher than previously published reference values, reflecting the 'active' mobility status of the sample. The hours of prosthesis use per day explained about 21% of the variance of the questionnaire score and was a significant predictor of perceived mobility. Reference values for the recently developed PMQ 2.0 survey and relative to active, traumatic amputees were reported. As prosthesis use was a significant predictor of the amputees' perceived mobility, prolonged use of the artificial limb should be always encouraged in clinical practice.
Electrocorticogram (ECoG) well characterizes hand movement intentions and gestures. In the present work we aim to investigate the possibility to enhance hand pose classification, in a Rock-Paper-Scissor -and Rest -task, by introducing topological descriptors of time series data. We hypothesized that an innovative approach based on topological data analysis can extract hidden information that are not detectable with standard Brain Computer Interface (BCI) techniques. To investigate this hypothesis, we integrate topological features together with power band features and feed them to several standard classifiers, e.g. Random Forest, Gradient Boosting. Model selection is thus completed after a meticulous phase of bayesian hyperparameter optimization. With our method, we observed robust results in terms of accuracy for a four-labels classification problem, with limited available data. Through feature importance investigation, we conclude that topological descriptors are able to extract useful discriminative information and provide novel insights. Since our data are restricted to single-patient recordings, generalization might be limited. Nevertheless, our method can be extended and applied to a wide range of neurophysiological recordings and it might be an intriguing point of departure for future studies.
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