Assessment of facial paralysis (FP) and quantitative grading of facial asymmetry are essential in order to quantify the extent of the condition as well as to follow its improvement or progression. As such, there is a need for an accurate quantitative grading system that is easy to use, inexpensive and has minimal inter-observer variability. A comprehensive automated system to quantify and grade FP is the main objective of this work. An initial prototype has been presented by the authors. The present research aims to enhance the accuracy and robustness of one of this system's modules: the resting symmetry module. This is achieved by including several modifications to the computation method of the symmetry index (SI) for the eyebrows, eyes and mouth. These modifications are the gamma correction technique, the area of the eyes, and the slope of the mouth. The system was tested on normal subjects and showed promising results. The mean SI of the eyebrows decreased slightly from 98.42% to 98.04% using the modified method while the mean SI for the eyes and mouth increased from 96.93% to 99.63% and from 95.6% to 98.11% respectively while using the modified method. The system is easy to use, inexpensive, automated and fast, has no inter-observer variability and is thus well suited for clinical use.

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To address the need for a commercially feasible prosthetic hand, the current work presents the design of a new humanoid hand actuated using shape memory alloy (SMA) artificial muscle wires. The hand has 3 compliant fingers and a thumb attached to the palm. The palm structure is a novel design, which is based on the natural arches of the human hand to provide better grasping capabilities. A compact actuator module is proposed to house and cool the SMA wires. Design parameters of the hand were selected to maximize the work density. The hand is lightweight, low cost, and operates silently. It has functional opening and closing speeds and fingertip force.

Quantitative assessment and classification of facial paralysis (FP) are essential for treatment selection and progress evaluation of the condition. As part of a comprehensive framework towards this goal, this study aims to classify five normal facial functions: smiling, eye closure, raising the eyebrows, blowing cheeks, and whistling as well as the rest state. 3D facial landmarks and facial animation units (FAUs) were obtained using the Kinect V2, a fast and cost-effective depth camera. These were used to compute the features used in a Support Vector Machine (SVM) classifier. A dataset of 1650 records from 50 normal subjects was compiled for this study. The performances of different SVM kernel models were tested with different feature groups. The best performance (Accuracy=96.7%, Sensitivity=90.2%, and Specificity=98%) was found when using the RBF kernel model applied on just nine differences in FAUs. This research will be developed and extended to include FP classification.

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