Robot-assisted therapy has become increasingly popular and useful in post-stroke neurorehabilitation. This paper presents an overview of the design and control of the dual-arm Recupera exoskeleton to provide intense therapist-guided as well as self training for sensorimotor rehabilitation of the upper body. The exoskeleton features a lightweight design, high level of modularity, decentralized computing, and various levels of safety implementation. Due to its modularity, the system can be used as a wheel-chair mounted system or as a full-body system. Both systems enable a wide range of therapies while efficiently grounding the weight of the system and without compromising the patient’s mobility. Furthermore, two rehabilitation therapies implemented on the exoskeleton system, namely teach & replay therapy and mirror therapy, are presented along with experimental results.
Epicardial adipose tissue (EAT) has been shown to have important effects on the development of coronary artery disease (CAD) via local paracrine influences on the vascular bed. We compared a cohort of asymptomatic patients with Type II Diabetes (DM) without known CAD to an age and gender matched group of asymptomatic patients without DM from the CTRAD study in which patients underwent a cardiac computed tomography angiogram (CTA), for early detection of CAD. Mean EAT volumes of 118.6 ± 43.0 and 70.0 ± 44.0 cm3 were found in the DM and non-DM groups respectively. When stratified by presence and severity of CAD, it was found that in the DM (p=0.003) and non-DM groups (p<0.001) there was a statistically significant increase in EAT volume as the patients were found to have increasingly severe CAD. After adjusting for age, race, gender, DM, hypertension, insulin use, BMI, and coronary artery calcium (CAC) score, the presence of >120 cm3 of EAT was found to be highly correlated with the presence of significant CAD (Adjusted Odds Ratio 4.47, 95% CI (1.35–14.82)). We found that not only is EAT volume an independent predictor of CAD, but that an increasing volume of EAT predicted increasing severity of CAD even after adjustment for CAC score.
Aims
The association between epicardial adipose tissue (EAT) volume and coronary artery disease (CAD) severity was evaluated, independent of traditional risk factors and coronary artery calcium (CAC) scores, in patients with diabetes type 2 (DM-2) using cardiac computed tomography angiography (CTA).
Methods
A multivariate analysis was utilized to assess for an independent association after calculating EAT volume, CAD severity, and calcium scores in 92 patients with DM-II from the CTRAD study. We graded CAD severity as none (normal coronaries), mild-moderate (<70% stenosis), and severe (70% or greater stenosis).
Results
A total of 39 (42.3 %) asymptomatic patients with diabetes did not have CAD; 30.4% had mild/moderate CAD; and 27.1% had severe CAD. Mean EAT volume was highest in patients with severe CAD (143.14 cm3) as compared to mild/moderate CAD (112.7 cm3), and no CAD (107.5 cm3) (p= 0.003). After adjustment of clinical risk factors, notably, CAC score, multivariate regression analysis showed EAT volume was an independent predictor of CAD severity in this sample (odds ratio 11.2, 95% confidence interval 1.7 –73.8, p =0.01).
Conclusions
Increasing EAT volume in asymptomatic patients with DM-II is associated with presence of severe CAD, independent of BMI and CAC, as well as traditional risk factors.
Parallel mechanisms are increasingly being used as modular subsystem units in various robots and man-machine interfaces for their superior stiffness, payload-to-weight ratio and dynamic properties. This leads to series-parallel hybrid robotic systems which are difficult to model and control due to the presence of various closed loops. Most model based kinematic and dynamic modeling tools resolve loop closure constraints numerically and hence suffer from inefficiency and accuracy issues. Also, they do not exploit the modularity in robot design. In this paper, we present a modular and analytical approach towards kinematic and dynamic modeling of series-parallel hybrid robots. This approach has been implemented in a software framework called Hybrid Robot Dynamics (HyRoDyn) and its application is demonstrated with the help of a series-parallel hybrid humanoid robot recently developed at DFKI-RIC.
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