Gesture recognition method, gesture recognition system, and performing device therefore

Abstract

A performing device of a gesture recognition system executes a performing procedure of a gesture recognition method. The performing procedure includes steps of: receiving a sensing signal; selecting one of sensing frames of the sensing signal; determining a soft label of the selected sensing frame; classifying a gesture event when the soft label of the selected sensing frame is approved. The gesture event is classified to determine the motion of the user. Therefore, the gesture recognition system does not need a predetermined time period to recognize the motion of the user. The time period for recognizing the motion of the user can be dynamical. A total time period for classifying a plurality of motions can be decreased, and the performance of the gesture recognition system can be improved.

What is claimed is: 
     
         1 . A gesture recognition method, comprising a performing procedure;
 wherein the performing procedure comprises steps of:
 receiving a sensing signal from a sensing unit; wherein the sensing signal comprises a plurality of sensing frames; 
 selecting one of the sensing frames; 
 determining a soft label of the selected sensing frame according to a neural network; 
 classifying a gesture event when to the soft label of the selected sensing frame is approved. 
   
     
     
         2 . The gesture recognition method as claimed in  claim 1 , wherein the soft label of the sensing frame is encoded into one-hot vectors to train the neural network. 
     
     
         3 . The gesture recognition method as claimed in  claim 1 , wherein the performing procedure comprises steps of:
 determining whether the soft label of the selected sensing frame exceeds a confidence threshold before classifying the gesture event;   when the soft label of the selected sensing frame exceeds the confidence threshold, the soft label of the selected sensing frame is approved, and the gesture event is classified.   
     
     
         4 . The gesture recognition method as claimed in  claim 3 , wherein the performing procedure further comprises steps of:
 when the soft label of the selected sensing frame does not exceed the confidence threshold, selecting another one of the sensing frames, determining a soft label of the selected another one of the sensing frames according to the neural network, and determining again whether the soft label of said selected another sensing frame exceeds the confidence threshold.   
     
     
         5 . The gesture recognition method as claimed in  claim 1 , further comprising a training procedure;
 wherein the training procedure comprises steps of:   receiving a training signal; wherein the training signal comprises a plurality of training frames;
 determining an amount of the training frames; 
 determining a function according to the amount of the training frames; 
 calculating a soft label of each training frame according to the function; and 
 training the neural network with the soft labels of the training frames as ground truth of the neural network. 
   
     
     
         6 . The gesture recognition method as claimed in  claim 5 , wherein the training procedure is executed two times for receiving two said training signals;
 wherein a percentage of a sequence of the training frames of one of the training signals equals to a percentage of a sequence of the training frames of another one of the training signals having the same soft label when the soft label equals to the confidence threshold.   
     
     
         7 . The gesture recognition method as claimed in  claim 6 , wherein:
 the function is a monotonic function;   the training frames of the training signal are arranged in sequence;   a first training frame of the training signal is mapped to the soft label of zero through the function; and   a last training frame of the training signal is mapped to the soft label of one through the function.   
     
     
         8 . A gesture recognition system, comprising a performing device and a training device; wherein the performing device comprises:
 a sensing unit, sensing a sensing signal and a training signal;   a memory unit, storing a neural network;   a processing unit, electrically connected to the sensing unit and the memory unit;   wherein the processing unit executes a performing procedure;   wherein the performing procedure comprises steps of:
 receiving the sensing signal from the sensing unit; wherein the sensing signal comprises a plurality of sensing frames; 
 selecting one of the sensing frames; 
 determining a soft label of the selected sensing frame according to the neural network; 
 classifying a gesture event when the soft label of the selected sensing frame is approved; 
   wherein the training device is electrically connected to the performing device, and executes a training procedure;   wherein the training procedure comprises steps of:
 receiving the training signal from the sensing unit of the performing device; 
   wherein the training signal comprises a plurality of training frames;
 determining an amount of the training frames of the training signal; 
 determining a function according to the amount of the training frames of the training signal; 
 calculating a soft label of each training frame of the training signal according to the function; and 
 training the neural network with the soft labels of the training frames of the training signal as ground truth of the neural network. 
   
     
     
         9 . The gesture recognition system as claimed in  claim 8 , wherein:
 the function is a monotonic function;   the training frames of the training signal are arranged in sequence;   a first training frame of the training signal is mapped to the soft label of zero through the function; and   a last training frame of the training signal is mapped to the soft label of one through the function.   
     
     
         10 . The gesture recognition system as claimed in  claim 8 , wherein the soft labels of the sensing frames of the sensing signal are encoded into one-hot vectors to train the neural network. 
     
     
         11 . The gesture recognition system as claimed in  claim 8 , wherein the performing procedure further comprises steps of:
 determining whether the soft label of the selected sensing frame exceeds a confidence threshold before classifying the gesture event;   when the soft label of the selected sensing frame exceeds the confidence threshold, the soft label of the selected sensing frame is approved, and the gesture event is classified.   
     
     
         12 . The gesture recognition system as claimed in  claim 11 , wherein when the soft label of the selected sensing frame of the sensing signal does not exceed the confidence threshold, the processing unit selects another one of the sensing frames, and further determines a soft label of the selected another one of the sensing frames according to the neural network, and the processing unit determines again whether the soft label of said selected another sensing frame exceeds the confidence threshold. 
     
     
         13 . The gesture recognition system as claimed in  claim 11 , wherein the training procedure is executed two times for receiving two said training signals;
 wherein a percentage of a sequence of the training frames of one of the training signals equals to a percentage of a sequence of the training frames of another one of the training signals having the same soft label when the soft label equals to the confidence threshold.   
     
     
         14 . A performing device, comprising:
 a sensing unit, sensing a sensing signal and a training signal;   a memory unit, storing a neural network;   a processing unit, electrically connected to the sensing unit and the memory unit;   wherein the processing unit executes a performing procedure;   wherein the performing procedure comprises steps of:
 receiving the sensing signal from the sensing unit; wherein the sensing signal comprises a plurality of sensing frames; 
 selecting one of the sensing frames; 
 determining a soft label of the selected sensing frame according to the neural network; 
 classifying the gesture event when the soft label of the selected sensing frame is approved. 
   
     
     
         15 . The performing device as claimed in  claim 14 , wherein the soft labels of the sensing frames are encoded into one-hot vectors to train the neural network. 
     
     
         16 . The performing device as claimed in  claim 14 , wherein the performing procedure further comprises steps of:
 determining whether the soft label of the selected sensing frame exceeds a confidence threshold before classifying the gesture event;   when the soft label of the selected sensing frame exceeds the confidence threshold, the soft label of the selected sensing frame is approved, and the gesture event is classified.   
     
     
         17 . The performing device as claimed in  claim 16 , wherein when the soft label of the selected sensing frame of the sensing signal does not exceed the confidence threshold, the processing unit selects another one of the sensing frames, classifies a gesture event of the selected sensing frame according to the neural network, and further determines a soft label of the selected another one of the sensing frames according to the neural network, and the processing unit determines again whether the soft label of said selected another sensing frame exceeds the confidence threshold.