In the recent domain of motion capture and analysis, a new challenge has been the automatic evaluation of skill in gestures. Many methods have been proposed for gesture evaluation based on feature extraction, skill modeling and gesture comparison. However, movements can be influenced by many factors other than skill, including morphology. All these influences make comparison between gestures of different people difficult. In this paper, we propose a new method based on constrained linear regression to remove the influence of morphology on motion features. To validate our method, we compare it to a baseline method, consisting in a scaling of the skeleton data [14]. Results show that our method outperforms previous work both in removing morphology influence on feature, and in improving feature relation with skill. For a set of 326 features extracted from two datasets of Taijiquan gestures, we show that morphology influence is completely removed for 100% of the features using our method, whereas the baseline method only allows limited reduction of morphology influence for 74% of the features. Our method improves correlation with skill as assessed by an expert by 0.04 (p < 0.0001) in average for 98% of the features, against 0.001 (p = 0.68) for 58% of the features with the baseline method. Our method is also more general than previous work, as it could potentially be applied with any interindividual factor on any feature.
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