Haptic communication system using cutaneous actuators for simulation of continuous human touch
摘要
說(shuō)明書(shū)
2) A second constraint 1312 is high sparsity. This constrains the number of cutaneous actuators that are activated at once as humans may become confused when exposed to more than a few active cutaneous actuators. This sparsity constraint 1312 may be achieved by adding a cost that is proportional or increases with the number of cutaneous actuators that are indicated to be active by the neural network 1310 for a set of haptic cue outputs at a time slice. For example, the cost function for sparsity may compute the number of nodes indicating activation of cutaneous actuators, and derive a cost value in proportion to the number of nodes. In another embodiment, this sparsity constraint 1312 is achieved by filtering out outputs from the neural network 1310 that exceed a max number of activated cutaneous actuators. The result from the iteration of the neural network is discarded and a new iteration is performed with newly modified weights.
3) A third constraint 1312 is temporal stability. This constrains the frequency of changes in the state of cutaneous actuators so that they do not change too rapidly, which may cause confusion as a human may not be able to perceive such rapid changes. This temporal stability constraint 1312 may be achieved by adding a cost that is proportional to the number of changes from the output of the previous slice of the input spectrogram 1306. As the neural network 1310 may be a recurrent neural network, it can recall information from a prior state, and thus the cost may be increased when the prior information indicates that a change has occurred.
