A Study of Hand Function in Stroke Patients Using Kinematic Metrics

Currently, many methods for assessing hand function in stroke patients are administered by humans, which can lack objectivity and make it difficult to achieve precise evaluations. In order to tackle this issue, we proposed a new assessment method that utilized hand movement data collected from the Leap motion device. By applying the independent sample T-test or Mann-Whitney U-test, we identified sensitive kinematic metrics from the 38 extracted metrics. We then used the principal component analysis (PCA) method to further analyze and rank the selected sensitive metrics. This processing enabled us to determine the most sensitive kinematic metrics that can distinguish differences in hand function between normal individuals and stroke patients. To validate the proposed method, we conducted an experiment with 15 volunteers. The results showed that MiddleMCP-Max was the most sensitive metric for distinguishing patients from normal individuals. The experimental results also demonstrated that the proposed method was effective, scientifically objective, and may be useful in assisting with the hand function evaluation of stroke-induced hemiplegia.