Human-robot coupling dynamic modeling and analysis for upper limb rehabilitation robots

Human Factor Engineering Research for Rehabilitation Robots: A Systematic Review

The application of human factors engineering for rehabilitation robots is based on a "human-centered" design philosophy that strives to provide safe and efficient human-robot interaction training for patients rather than depending on rehabilitation therapists. Human factors engineering for rehabilitation robots is undergoing preliminary investigation. However, the depth and breadth of current research do not provide a complete human factor engineering solution for developing rehabilitation robots. This study aims to provide a systematic review of research at the intersection of rehabilitation robotics and ergonomics to understand the progress and state-of-the-art research on critical human factors, issues, and corresponding solutions for rehabilitation robots. A total of 496 relevant studies were obtained from six scientific database searches, reference searches, and citation-tracking strategies. After applying the selection criteria and reading the full text of each study, 21 studies were selected for review and classified into four categories based on their human factor objectives: implementation of high safety, implementation of lightweight and high comfort, implementation of high human-robot interaction, and performance evaluation index and system studies. Based on the results of the studies, recommendations for future research are presented and discussed.

Construction of a prediction model for risk of early neurological deterioration following intravenous thrombolysis in patients with acute ischemic stroke

BACKGROUND

The etiology of early neurological deterioration (END) occurring after intravenous thrombolysis is unclear.

OBJECTIVE

To investigate the factors associated with END following intravenous thrombolysis in patients with acute ischemic stroke, and to construct a prediction model.

METHODS

We selected a total of 321 patients with acute ischemic stroke, who were divided into two groups: the END group (n= 91) and the non-END group (n= 230). They were compared for their demographics, onset-to-needle time (ONT), door-to-needle time (DNT), related score results, and other data. The risk factors of the END group were identified using logistic regression analysis, and we constructed a nomogram model using the R software. A calibration curve was used to evaluate the calibration of the nomogram, and we assessed its clinical applicability using decision curve analysis (DCA).

RESULTS

In our multivariate logistic regression analysis, we found that four indexes, namely, complication with atrial fibrillation, post-thrombolysis National Institutes of Health Stroke Scale (NIHSS) score, pre-thrombolysis systolic blood pressure (SBP), and serum albumin level, were independent risk factors for END following intravenous thrombolysis in the patients (P< 0.05). We constructed an individualized nomogram prediction model using the above four predictors. The AUC value of the nomogram model was 0.785 (95% CI: 0.727-0.845) after internal validation, and the mean absolute error (MAE) in the calibration curve was 0.011, which indicated that the nomogram model had good prediction value. The decision curve analysis indicated that the nomogram model was clinically relevant.

CONCLUSION

The model was found to have excellent value in clinical application and prediction of END. This will be beneficial for healthcare providers to develop individualized prevention measures for END in advance, and thus reduce the incidence of END following intravenous thrombolysis.