Bayesian network aided grasp and grip efficiency estimation using a smart data glove for post-stroke diagnosis

Stroke is one of the major causes behind the increased mortality rate throughout the world and disability among the survivors. Such disabilities include several grasp and grip related impairment in daily activities like holding a glass of water, counting currency notes, producing correct signature in bank, etc., that seek serious attention. Present therapeutic facilities, being expensive and time-consuming, fail to cater the poverty stricken rural class of the society. In this paper, on the basis of an investigation, we developed a smart data glove based diagnostic device for better treatment of such patients by providing timely estimation of their grasp quality. Data collected from a VMG30 motion capture glove for six patients who survived stroke and two other healthy subjects was fused with suitable hypothesis obtained from a domain expert to reflect the required outcome on a Bayesian network. The end result could be made available to a doctor at a remote location through a smart phone for further advice or treatment. Results obtained clearly distinguished a patient from a healthy subject along with supporting estimates to study and compare different grasping gestures. The improvement in mobility could be assessed after physiotherapeutic treatments using the proposed method.