Mathematical modeling and trajectory planning of hand finger movements

This paper presents a mathematical model of trajectory during movement of human hand finger. Mathematical models of finger’s movement are necessary to control the finger of prosthetic hands, rehabilitation platform, and hand of cooperative robots. A simple platform is constructed with flexi force sensor and data acquisition circuit. Trajectories are calculated from the position data of the finger movement. Movement with various speed of the finger are maintained. Finally, Curve Fitting Toolbox of MATLAB is used to derive the mathematical model of finger trajectory. The obtained result could be beneficial to design the controller of the finger of prosthetic hands, rehabilitation platform, and hand of cooperative robots

Towards An Automatic Segmentation for Assessment of Cardiac Left Ventricle Function

Research on detecting, recognising and interpreting Cardiac MRI has started since the 1980s. The problem with manual tracing efforts hampering the adoption of cardiac MRI as routine investigation. Manual tracing is also dependent on image quality, and there is no one-size-fitsall MRI setting for the optimum image result. In this paper, we present a proposed approach to automatically detect the left ventricle (LV) wall in the effort to automatically assist the assessment of cardiac function. Using a standard bechmark dataset, our experiments have shown that our proposed method had effectively improve the automatic detection of the epicardium and endocardium

Automatic segmentation of CMRIs for LV contour detection

Research on detecting, recognising and interpreting cardiovascular magnetic resonance images (CMRIs) has started since the 1980s. Time consuming and the need of expert evaluation are the key problems in the manual tracing efforts of CMRIs in a routine investigation. CMRIs manual tracing is also dependent on image quality, and there is no one-size-fits-all MRI setting for an optimum image result. In this paper, we present an approach using 2-Standard Division (2-SD) correlation along with the Sum of Absolute Difference technique and Otsu Watershed to automatically detect the left ventricle (LV) wall and blood pool in the effort to automatically assist the assessment of cardiac function. We test the approach using the Sunnybrook Cardiac Data, a standard benchmark dataset. The results shown that the proposed method had improved the automatic detection of the epicardium and endocardiu