Publications by authors named "Stefano Doronzio"

Good data quality is vital for personalising plans in rehabilitation. Machine learning (ML) improves prognostics but integrating it with Multiple Imputation (MImp) for dealing missingness is an unexplored field. This work aims to provide post-stroke ambulation prognosis, integrating MImp with ML, and identify the prognostic influential factors.

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Background: Intensive treadmill training (TT) has been documented to improve gait parameters and functional independence in Parkinson's Disease (PD), but the optimal intervention protocol and the criteria for tailoring the intervention to patients' performances are lacking. TT may be integrated with augmented virtual reality (AVR), however, evidence of the effectiveness of this combined treatment is still limited. Moreover, prognostic biomarkers of rehabilitation, potentially useful to customize the treatment, are currently missing.

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Background: Transfemoral amputation is a serious intervention that alters the locomotion pattern, leading to secondary disorders and reduced quality of life. The outcomes of current gait rehabilitation for TFAs seem to be highly dependent on factors such as the duration and intensity of the treatment and the age or etiology of the patient. Although the use of robotic assistance for prosthetic gait rehabilitation has been limited, robotic technologies have demonstrated positive rehabilitative effects for other mobility disorders and may thus offer a promising solution for the restoration of healthy gait in TFAs.

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Gait asymmetry in lower-limb amputees can lead to several secondary conditions that can decrease general health and quality of life. Including augmented sensory feedback in rehabilitation programs can effectively mitigate spatiotemporal gait irregularities. Such benefits can be obtained with non-invasive haptic systems representing an advantageous choice for usability in overground training and every-day life.

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