Gradient boosting DD‐MLP Net: An ensemble learning model using near‐infrared spectroscopy to classify after‐stroke dyskinesia degree during exercise

Abstract This study aims to develop an automatic assessment of after‐stroke dyskinesias degree by combining machine learning and near‐infrared spectroscopy (NIRS). Thirty‐five subjects were divided into five stages (healthy, patient: Brunnstrom stages 3, 4, 5, 6). NIRS was used to record the muscular hemodynamic responses from bilateral femoris (biceps brachii) muscles during passive and active upper (lower) limbs circular exercise. We used the D‐S evidence theory to conduct feature information fusion and established a Gradient Boosting DD‐MLP Net model, combining the dendrite network and multilayer perceptron, to realize automatic dyskinesias degree evaluation. Our model classified the upper limb dyskinesias with high accuracy: 98.91% under the passive mode and 98.69% under the active mode, and classified the lower limb dyskinesias with high accuracy: 99.45% and 99.63% under the passive and active modes, respectively. Our model combined with NIRS has great potential in monitoring the after‐stroke dyskinesias degree and guiding rehabilitation training.