Haptic communication system using cutaneous actuators for simulation of continuous human touch
摘要
說明書
The features 408 may include a feature 408a describing a frequency-domain representation of the signals 216, 256. Extracting the feature 408a may include creating a frequency-domain representation of the signals 216, 256. The features 408 may include a feature 408b describing a time-domain representation of the signals 216, 256. Extracting the feature 408b may include performing time-domain sampling of the signals 216, 256. The features 408 may include a feature 408c describing aggregate values based on the signals 216, 256. The features 408 may include a feature 408d describing a shape of a wave of the signals 216, 256. The features 408 may include a feature 408e describing phonemes of the speech signals 216.
The machine learning circuit 242 functions as an autoencoder and is trained using training sets including information from the touch signatures store 412 and the speech subcomponents store 416. The touch signatures store 412 may store associations between known touch signatures for the touch lexicon. In embodiments, the machine learning circuit 242 is thereby configured to apply the transfer function to the signals 216, 256 by extracting features 408 from the signals 216, 256, determining a touch signature of the sending user based on the extracted features 408, and generating the haptic illusion signals 202 corresponding to the determined touch signature. In one embodiment, the machine learning circuit 242 determines speech subcomponents based on the extracted features 408 and generates haptic symbols corresponding to the determined speech subcomponents.
