Explore the vast range of titles available.

A popular and widely suggested measure for assessing unilateral hand motor skills in stroke patients is the box and block test (BBT). Our study aimed to create an augmented reality enhanced version of the BBT (AR-BBT) and evaluate its correlation to the original BBT for stroke patients. Following G-power analysis, clinical examination, and inclusion–exclusion criteria, 31 stroke patients were included in this study. AR-BBT was developed using the Open Source Computer Vision Library (OpenCV). The MediaPipe's hand tracking library uses a palm and a hand landmark machine learning model to detect and track hands. A computer and a depth camera were employed in the clinical evaluation of AR-BBT following the principles of traditional BBT. A strong correlation was achieved between the number of blocks moved in the BBT and the AR-BBT on the hemiplegic side (Pearson correlation = 0.918) and a positive statistically significant correlation ( p  = 0.000008). The conventional BBT is currently the preferred assessment method. However, our approach offers an advantage, as it suggests that an AR-BBT solution could remotely monitor the assessment of a home-based rehabilitation program and provide additional hand kinematic information for hand dexterities in AR environment conditions. Furthermore, it employs minimal hardware equipment.

Background/Objectives: Spasticity commonly occurs in individuals after experiencing a stroke, impairing their hand function and limiting activities of daily living (ADLs). In this paper, we introduce an exoskeletal aid, combined with a set of augmented reality (AR) games consisting of the Rehabotics rehabilitation solution, designed for individuals with upper limb spasticity following stroke. Methods: Our study, involving 60 post-stroke patients (mean ± SD age: 70.97  ±  4.89 years), demonstrates significant improvements in Ashworth Scale (AS) scores and Box and Block test (BBT) scores when the Rehabotics solution is employed. Results: The intervention group showed slightly greater improvement compared to the control group in terms of the AS (−0.23, with a confidence interval of −0.53 to 0.07) and BBT (1.67, with a confidence interval of 1.18 to 2.16). Additionally, the Rehabotics solution was particularly effective for patients with more severe deficits. Patients with an AS score of 3 showed more substantial improvements, with their AS scores increasing by −1.17 ± 0.39 and BBT scores increasing by −4.83 ± 0.72. Conclusions: These findings underscore the potential of wearable hand robotics in enhancing stroke survivors’ hand rehabilitation, emphasizing the need for further investigations into its broader applications.