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Fig. 4 | Journal of NeuroEngineering and Rehabilitation

Fig. 4

From: The use of machine learning and deep learning techniques to assess proprioceptive impairments of the upper limb after stroke

Fig. 4

A Performance metrics when classified based on individual parameters (Var: variability, Cont/Exp: contraction/expansion, Shift, AE: absolute error) of arm position matching task, as well as overall task score to find the number of impaired participants. B Performance metrics (accuracy, precision, recall, and F1 score) for the machine learning and deep learning models (LR: Logistic Regression, DT: Decision Tree, RF: Random Forest, RFT: Random Forest with Hyperparameters Tuning, SVM: Support Vector Machine, DNN: Deep Neural Network)

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