Haptic communication system using cutaneous actuators for simulation of continuous human touch
摘要
說明書
In some embodiments, three datasets are used. Training for the machine learning circuit 242 is performed using the first dataset. The accuracy of the machine learning circuit 242 is tested using the second dataset. The third dataset, known as a validation set, may be formed of additional features, other than those in the training sets, which have already been determined to have or to lack the property in question. The process applies the trained machine learning circuit 242 to the features of the validation set to quantify the accuracy of the machine learning circuit 242. Common metrics applied in accuracy measurement include: Precision (P)=True Positives (TP)/(TP+False Positives (FP)) and Recall (R)=TP/(TP+False Negatives (FN)), where Precision refers to how many touch signatures the machine learning circuit 242 correctly predicted (TP) out of the total it predicted (TP+FP), and Recall refers to how many touch signatures the machine learning circuit 242 correctly predicted (TP) out of the total number of features that did have the property in question (TP+FN). In one embodiment, the process iteratively re-trains the machine learning circuit 242 until the occurrence of a stopping condition, such as the accuracy measurement indication that the model is sufficiently accurate, or a number of training rounds having taken place.
Example Process for Haptic Communication
