Authors: Clement Lhos, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA; Emek Barıs¸ Kuc¸uktabak, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and Center for Robotics and Biosystems, Northwestern University, Evanston, IL, USA; Lorenzo Vianello, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA; Lorenzo Amato, Legs and Walking Lab of Shirley Ryan AbilityLab, Chicago, IL, USA and The Biorobotics Institute, Scuola Superiore Sant’Anna,...
Validation of Deep-Learning Predictions The proposed LSTM model was evaluated using a leaveone-out cross-validation approach: in each iteration, the model was trained on five subjects and then tested on the remaining subject. In this test, we compared a model using instantaneous kinematic data and another using a history of kinematic data . Fig. 3 shows the prediction for each model evaluated on the test set composed by Subject Four and the ground truth value.
Validation of Deep-Learning Predictions A. Validation of Deep-Learning Predictions The proposed LSTM model was evaluated using a leaveone-out cross-validation approach: in each iteration, the model was trained on five subjects and then tested on the remaining subject. In this test, we compared a model using instantaneous kinematic data and another using a history of kinematic data . Fig.
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