Search

Background: Occupational therapy (OT) is an essential component of patient care, and it is especially beneficial if focused on meaningful activities. For ataxic patients, traditional procedures are currently the most efficient, although without specific guidelines and suggestions for virtual reality integration. In this context, this study proposes Hybrid Smart Rehabilitation (HSR) based on mixed reality (MR) as an aid in overcoming limitations of the traditional OT procedures. Methods: MR-HSR is designed specifically for ataxic patients and developed in Unity with the Holographic Remoting setting for run-time intervention on the scene. The subject reaches a book and grabs it with their hand inside a holographic guide with audio-visive feedback. Hand trajectories acquired from eight ataxic patients and eight healthy subjects were compared and new variables were analyzed to evaluate the performance. The Trust in Automation questionnaire was submitted to assess the opinion of the patients. Results: Patients confirmed their trust in the developer and in the improvement that this system can bring to their rehabilitation. The "total time" and "sway area" of the trajectory were statistically significant and, together with the deviation of the trajectory from the main axis of the guide, although not statistically significant, made it possible to build a classifier. Conclusions: The patient-specific MR-HSR can be considered as an integrative tool for assessing the subject's condition by analyzing new quantitative variables which, if matched to the Scale for the Assessment and Rating of Ataxia (SARA), could be the basis of a new index to assess the progressiveness of ataxia. [ABSTRACT FROM AUTHOR]

Background: Stroke-induced immunosuppression (SII) represents a negative rehabilitative prognostic factor associated with poor motor performance at discharge from a neurorehabilitation unit (NRB). This study aims to evaluate the association between SII and gait impairment at NRB admission. Methods: Forty-six stroke patients (65.4 ± 15.8 years, 28 males) and 42 healthy subjects (HS), matched for age, sex, and gait speed, underwent gait analysis using an inertial measurement unit at the lumbar level. Stroke patients were divided into two groups: (i) the SII group was defined using a neutrophil-to-lymphocyte ratio ≥ 5, and (ii) the immunocompetent (IC) group. Harmonic ratio (HR) and short-term largest Lyapunov's exponent (sLLE) were calculated as measures of gait symmetry and stability, respectively. Results: Out of 46 patients, 14 (30.4%) had SII. HR was higher in HS when compared to SII and IC groups (p < 0.01). HR values were lower in SII when compared to IC subjects (p < 0.01). sLLE was lower in HS when compared to SII and IC groups in the vertical and medio-lateral planes (p ≤ 0.01 for all comparisons). sLLE in the medio-lateral plane was higher in SII when compared to IC subjects (p = 0.04). Conclusions: SII individuals are characterized by a pronounced asymmetric gait and a more impaired dynamic gait stability. Our findings underline the importance of devising tailored rehabilitation programs in patients with SII. Further studies are needed to assess the long-term outcomes and the role of other clinical features on gait pattern. [ABSTRACT FROM AUTHOR]

The interpretability of gait analysis studies in people with rare diseases, such as those with primary hereditary cerebellar ataxia (pwCA), is frequently limited by the small sample sizes and unbalanced datasets. The purpose of this study was to assess the effectiveness of data balancing and generative artificial intelligence (AI) algorithms in generating synthetic data reflecting the actual gait abnormalities of pwCA. Gait data of 30 pwCA (age: 51.6 ± 12.2 years; 13 females, 17 males) and 100 healthy subjects (age: 57.1 ± 10.4; 60 females, 40 males) were collected at the lumbar level with an inertial measurement unit. Subsampling, oversampling, synthetic minority oversampling, generative adversarial networks, and conditional tabular generative adversarial networks (ctGAN) were applied to generate datasets to be input to a random forest classifier. Consistency and explainability metrics were also calculated to assess the coherence of the generated dataset with known gait abnormalities of pwCA. ctGAN significantly improved the classification performance compared with the original dataset and traditional data augmentation methods. ctGAN are effective methods for balancing tabular datasets from populations with rare diseases, owing to their ability to improve diagnostic models with consistent explainability. [ABSTRACT FROM AUTHOR]