In this keynote paper we discuss how to automatically analyze the kinematics of human body movements in the context of dance analysis. We also propose new movement analysis handcrafted features based on Laban Movement Analysis (LMA) and 3D pose estimation of dancers. To evaluate the most significant body parts in kinematics analysis of human body movements, we computed the SHAP value of each body part. The preliminary tests and experiments we carried out using video sequences from AIST++ dataset demonstrate the relevance of these new features based either on short or long periods of time. We also demonstrate that the relevance of the most significant features depends on the specificities of the dance videos dataset to process. To improve the accuracy of handcraft features for dance style classification and their robustness to dataset specificities, we suggest to use domain adaptation techniques, such as feature-based, instance-based or parameter-based techniques.