Kaikutek Inc.

Institute for Information Industry

Wen-Jyi Hwang

Chun-Hsuan Kuo

Mike Chun-Hung Wang

Tsung-Ming Tai

Guan-Sian Wu

Chieh Wu

Yun-Jie Jhang

Fang Li

Chih-Hsiang Ho

Yu Feng Wu

Yi-Chih Tung

Ling Ya Huang

Wen-Yen Chou

Yu-Feng Wu

Chih-Wei Chen

Wen-Sheng Cheng

Hao-Gen Wong

Pang-Fu Liu

Khoi Duc Le

Wen-Yen Chang

Hung-Ju Wang

A temporal sequence alignment method includes the following steps: receiving gesture training data and gesture sample data; wherein the gesture training data includes multiple training frames and multiple training soft labels, and the gesture sample data includes multiple sample frames and multiple sample soft labels; compressing the training frames to generate a compressed training frame; compressing the sample frames to generate a compressed sample frame; calculating an alignment model of the compressed training frame and the compressed sample frame; aligning the sample soft labels to multiple aligned soft labels according to the alignment model; generating an aligned training data according to the gesture sample data and the aligned soft labels. The present invention uses the aforementioned steps to calibrate the sample soft labels of the gesture sample data, allowing a gesture recognition system to minimize time discrepancy for recognizing a gesture.

Temporal sequence alignment method for gesture training data

A performing device of an impulse-like gesture recognition system executes an impulse-like gesture recognition method. A performing procedure of the impulse-like gesture recognition method includes steps of: receiving a sensing signal from a sensing unit; determining a prediction with at least one impulse-like label according to the sensing frames by a deep learning-based model; and classifying at least one gesture event according to the prediction. The gesture event is classified to determine the motion of the user. Since the at least one impulse-like label is used to label at least one detection score of the deep learning-based model, the detection score is non-decreasing, reaction time of the at least one gesture event for an incoming gesture is fast, rapid consecutive gestures are easily decomposed, and an expensive post-processing is not needed.

Impulse-like gesture recognition method, and impulse-like gesture recognition system

A generic gesture detecting method executed by a generic gesture detecting device includes steps of: receiving a current sensing signal from a sensing unit; generating a current image according to the current sensing signal; determining whether the current image is similar with a stored image stored in a memory unit; when the current image is similar with the stored image, detecting the current image and the stored image to be a gesture signal; when the current image is different from the stored image, storing the current image into the memory unit, and returning to the step of receiving a current sensing signal. Since the generic gesture detecting device can automatically detect the gesture signal, the user may not need to enable a detecting time period before implementing a command motion. Therefore, the user can make the command motion without enabling the detecting time period, and the convenience can be increased.

Generic gesture detecting method and generic gesture detecting device

An earphone device having gesture recognition functions includes a gesture recognition element, a signal transmission unit, and a voice output element. The gesture recognition element includes a transmission unit, a reception chain and a processing unit. The transmission unit transmits a transmission signal to detect the gesture. The reception chain receives a gesture signal to generate a feature map data. The processing unit is coupled to the reception chain for receiving the feature map data and utilizes an identification algorithm to recognize gesture according to the feature map data to generate a gesture controlling signal. The signal transmission unit receives and transmits the gesture controlling signal to an electronic device. The processing unit receives a controlling action generated by the electronic device according to the gesture controlling signal via the signal transmission unit. The electronic device generates a voice message according to the controlling action and broadcasts the voice message.

Earphone device having gesture recognition functions

A range Doppler angle detection method executed by a range Doppler angle detection device includes steps of: receiving a first sensing signal and a second sensing signal; performing 1D Fast Fourier Transform (FFT) and 2D FFT to the first sensing signal for calculating one first 2D FFT map; performing the 1D FFT and the 2D FFT to the second sensing signal for calculating one second 2D FFT map; picking up one column of the first 2D FFT map and one column of the second 2D FFT map according to a given Doppler index; performing the 3D FFT to the picked column of the first 2D FFT map and the picked column of the second 2D FFT map for calculating a range Doppler angle. Therefore, a computation loading of the gesture recognition function can be reduced.

Range doppler angle detection method and range doppler angle detection device

A gesture recognition system executes a gesture recognition method which includes the following steps: receiving a sensing signal; selecting one of the sensing frames from the sensing signal; generating a sensing map by applying 2D FFT to the selected sensing frame; selecting a cell having a largest amplitude in the sensing map; calculating the velocity of the cell and setting the velocity of the selected sensing frame to be the velocity of the cell; labeling the selected sensing frame as a valid sensing frame if the velocity of the selected sensing frame exceeds a threshold value, otherwise labeling the selected sensing frame as an invalid sensing frame; using all of the sensing maps of the valid sensing frames in the sensing signal as the input data for the neural network of the gesture recognition system and accordingly performing gesture recognition and gesture event classification.

Gesture recognition system and gesture recognition method thereof

A gesture recognition system executes a gesture recognition method. The gesture recognition method includes steps of: receiving a training signal; selecting one of the sensing frames of the sensing signal; generating a sensing map; selecting a cell having the max-amplitude; determining a frame amplitude, a frame phase, and a frame range of the selected one of the sensing frames; setting the frame amplitudes, the frame phases, and the frame ranges of all of the sensing frames to input data of a neural network to classify a gesture event. The present invention just uses a few data to be the input data of the neural network. Therefore, the neural network may not require high computational complexity, the gesture recognition system may decrease the calculation load of the processing unit, and the gesture recognition function may not influence a normal operation of a smart device.

Gesture recognition method and gesture recognition system

A performing device of a gesture recognition system for reducing a false alarm rate executes a performing procedure of a gesture recognition method for reducing the false alarm rate. The gesture recognition system includes two neural networks. A first recognition neural network is used to classify a gesture event, and a first noise neural network is used to determine whether the sensing signal is the noise. Since the first noise neural network can determine whether the sensing signal is the noise, the gesture event may not be executed when the sensing signal is the noise. Therefore, the false alarm rate may be reduced.

Gesture recognition method for reducing false alarm rate, gesture recognition system for reducing false alarm rate, and performing device thereof

A gesture recognition system using siamese neural network executes a gesture recognition method. The gesture recognition method includes steps of: receiving a first training signal to calculate a first feature; receiving a second training signal to calculate a second feature; determining a distance between the first feature and the second feature in a feature space; adjusting the distance between the first feature and the second feature in feature space according to a predetermined parameter. Two neural networks are used to generate the first feature and the second feature, and determine the distance between the first feature and the second feature in the feature space for training the neural networks. Therefore, the gesture recognition system does not need a big amount of data to train one neural network for classifying a sensing signal. A user may easily define a new personalized gesture.

Gesture recognition method and system using siamese neural network

A rendezvous flow control apparatus, method, and non-transitory tangible computer readable medium thereof are provided. The rendezvous flow control apparatus includes a plurality of transceiving interfaces and a processing unit. Each of the transceiving interfaces is individually assigned with a first allocated bandwidth. The transceiving interfaces transmit a first data flow of a network service to a network apparatus by the first allocated bandwidths at a first stage. The transceiving interfaces receive a piece of feedback information from the network apparatus. The processing unit assigns a second allocated bandwidth to each of the transceiving interfaces according to the piece of feedback information. The transceiving interfaces transmit a second data flow of the network service to the network apparatus by the second allocated bandwidths at a second stage.

Rendezvous flow control apparatus, method, and non-transitory tangible computer readable medium

A heterogeneous network system, network apparatus, and rendezvous path selection method thereof are provided. The heterogeneous network system includes partner network apparatuses and a network apparatus. The transceiving interfaces of the network apparatus and the partner network apparatuses are not all the same. The network apparatus measures at least one first transmission parameter and receives a plurality of second transmission parameters. Each of the at least one first transmission parameter is associated with transmission from the network apparatus to one of its neighboring network apparatuses. Each of the second transmission parameters is associated with transmission between two network apparatuses neighboring each other. The network apparatus enumerates a plurality of paths from itself to a target network apparatus and calculates a path cost for each of the paths according to at least a portion of a combination of the at least one first transmission parameter and the second transmission parameters.

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Taipei, Taiwan

Wen-Jyi Hwang