Extended Data Fig. 4: Model validation accuracies and transfer learning accuracies for sensor signal with and without substrate.

To investigate how substrate-less property contributes to the model discriminating different subtle hand motions, the same amount of sensor signals is collected while a user typing Numpad keys and interacting with 6 different objects. a, Collected dataset is divided into training and validation datasets with a ratio of 8:2 for normal supervised learning. b, For transfer learning, we apply our TD-C learning with unlabeled random motion data to pretrain our learning model and use the first five-shot demonstrations to further transfer learning. Directly attached to the finger surface, nanomesh without substrate outperforms sensor with substrates in different tasks and training conditions.