# US 2019/0244016 A1 · Gesture recognition method for reducing false alarm rate, gesture recognition system for reducing false alarm rate, and performing device thereof

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- Number: US 2019/0244016 A1
- Document ID: US20190244016A1
- Status: Published application
- Kind: A1
- Application number: 16/103,215
- Filed: 2018-08-14
- Published: 2019-08-08
- Earliest claimed: 2018-02-04
- Assignees: KaiKuTek Inc.
- Applicants: KaiKuTek Inc.
- Inventors: Tsung-Ming TAI; Yun-Jie JHANG; Wen-Jyi HWANG; Chun-Hsuan KUO
- Web page: https://patentgrep.com/patent/US20190244016A1

## CPC

- G06F 3/017: {Gesture based interaction, e.g. based on a set of recognized hand gestures (interaction based on gestures traced on a digitiser G06F3/04883)}
- G01S 7/414: {Discriminating targets with respect to background clutter}
- G01S 7/417: {involving the use of neural networks}
- G01S 13/584: {adapted for simultaneous range and velocity measurements}
- G01S 13/89: for mapping or imaging
- G06F 9/5027: {the resource being a machine, e.g. CPUs, Servers, Terminals}
- G06F 17/18: for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)}
- G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F 18/2155: {characterised by the incorporation of unlabelled data, e.g. multiple instance learning [MIL], semi-supervised techniques using expectation-maximisation [EM] or naïve labelling}
- G06F 18/217: {Validation; Performance evaluation; Active pattern learning techniques}
- G06F 18/22: Matching criteria, e.g. proximity measures
- G06F 18/24: Classification techniques
- G06F 18/24133: {Distances to prototypes}
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
- G06N 3/045: Combinations of networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G06N 20/00: Machine learning
- G06T 7/20: Analysis of motion (motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51)
- G06V 40/20: Movements or behaviour, e.g. gesture recognition (recognition of facial expressions G06V40/16)
- H03B 21/02: by plural beating, i.e. for frequency synthesis {; Beating in combination with multiplication or division of frequency (digital frequency synthesis using a ROM G06F1/02; digital frequency synthesis in general H03K; indirect frequency synthesis using a PLL H03L7/16)}
- G01S 7/356: {involving particularities of FFT processing}
- G01S 7/415: {Identification of targets based on measurements of movement associated with the target}
- G01S 13/50: Systems of measurement based on relative movement of target
- G06F 2218/12: Classification; Matching
- G06T 2207/10028: Range image; Depth image; 3D point clouds
- G06T 2207/20056: Discrete and fast Fourier transform, [DFT, FFT]
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- H03B: GENERATION OF OSCILLATIONS, DIRECTLY OR BY FREQUENCY-CHANGING, BY CIRCUITS EMPLOYING ACTIVE ELEMENTS WHICH OPERATE IN A NON-SWITCHING MANNER; GENERATION OF NOISE BY SUCH CIRCUITS (generators adapted for electrophonic musical instruments G10H; masers or lasers H01S; generation of oscillations in plasma H05H)

## Abstract

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.

## Claims

### Claim 1

A gesture recognition method for reducing a false alarm rate, comprising a performing procedure;
wherein the performing procedure comprises steps of:
receiving a sensing signal from a sensing unit;
setting the sensing signal to input data of a first recognition neural network;
classifying a gesture event according to the sensing signal by using the first recognition neural network;
setting the sensing signal to input data of a first noise neural network;
determining whether the sensing signal is a noise by using the first noise neural network;
when the sensing signal is not the noise, executing the gesture event.

### Claim 2 (depends on 1)

The gesture recognition method for reducing the false alarm rate as claimed in claim 1, wherein the performing procedure further comprises steps of:
when the first noise neural network determines that the sensing signal is the noise, receiving another sensing signal from the sensing unit again.

### Claim 3 (depends on 1)

The gesture recognition method for reducing the false alarm rate 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 gesture time period and two transition time periods, and the gesture time period is between the two transition time periods in time series;
training the first recognition neural network with the gesture time period of the training signal;
setting the two transition time periods of the training signal to training data of the first noise neural network, and training the first noise neural network with a first result as ground truth of the first noise neural network; wherein when a result of the first noise neural network is the first result, the first noise neural network determines that the sensing signal is the noise.

### Claim 4 (depends on 1)

The gesture recognition method for reducing the false alarm rate as claimed in claim 1, wherein the first recognition neural network is a convolutional neural network (CNN) or a Long Short Memory (LSTM) network.

### Claim 5 (depends on 1)

The gesture recognition method for reducing the false alarm rate as claimed in claim 1, wherein the first noise neural network is a fully-connected neural network.

### Claim 6 (depends on 1)

The gesture recognition method for reducing the false alarm rate as claimed in claim 1, wherein the sensing signal is a Range Doppler Image (RDI) signal.

### Claim 7

A gesture recognition system for reducing a false alarm rate, comprising a performing device; wherein the performing device comprises:
a sensing unit, configured to sense a sensing signal;
a memory unit, configured to store a first recognition neural network and a first noise neural network;
a processing unit, configured to be electrically connected to the sensing unit and the memory unit; wherein the processing unit receives the sensing signal from the sensing unit, sets the sensing signal to input data of the first recognition neural network, and classifies a gesture event according to the sensing signal by using the first recognition neural network;
wherein the processing unit further sets the sensing signal to input data of the first noise neural network, and the processing unit determines whether the sensing signal is a noise by using the first noise neural network;
wherein when the sensing signal is not the noise, the processing unit executes the gesture event.

### Claim 8 (depends on 7)

The gesture recognition system for reducing the false alarm rate as claimed in claim 7, wherein when the first noise neural network determines that the sensing signal is the noise, the processing unit receives another sensing signal from the sensing unit.

### Claim 9 (depends on 7)

The gesture recognition system for reducing the false alarm rate as claimed in claim 7, further comprising a training device;
wherein the training device stores a second recognition neural network and a second noise neural network, and the training device is connected to the performing device to receive a training signal from the sensing unit;
wherein the training signal comprises a gesture time period and two transition time periods, and the gesture time period is between the two transition time periods in time series;
wherein the training device trains the second recognition neural network with the gesture time period of the training signal;
wherein the training device sets the two transition time periods of the training signal to training data of the first noise neural network, and trains the second neural network with a first result as ground truth of the first noise neural network;
wherein when a result of the second noise neural network is the first result, the second noise neural network determines that the sensing signal is the noise;
wherein the training device further updates the first recognition neural network of the performing device according to the second recognition neural network, and updates the first noise neural network of the performing device according to the second noise neural network.

### Claim 10 (depends on 7)

The gesture recognition system for reducing the false alarm rate as claimed in claim 7, wherein the first recognition neural network is a convolutional neural network (CNN) or a Long Short Memory (LSTM) network.

### Claim 11 (depends on 7)

The gesture recognition system for reducing the false alarm rate as claimed in claim 7, wherein the first noise neural network is a fully-connected neural network.

### Claim 12 (depends on 7)

The gesture recognition system for reducing the false alarm rate as claimed in claim 7, wherein the sensing signal is a Range Doppler Image (RDI) signal.

### Claim 13 (depends on 7)

The gesture recognition system for reducing the false alarm rate as claimed in claim 7, wherein the sensing unit is a Doppler radar.

### Claim 14

A performing device, comprising:
a sensing unit, configured to sense a sensing signal;
a memory unit, configured to store a first recognition neural network and a first noise neural network;
a processing unit, configured to be electrically connected to the sensing unit and the memory unit; wherein the processing unit receives the sensing signal from the sensing unit, sets the sensing signal to input data of the first recognition neural network, and classifies a gesture event according to the sensing signal by using the first recognition neural network;
wherein the processing unit further sets the sensing signal to input data of the first noise neural network, and the processing unit determines whether the sensing signal is a noise by using the first noise neural network;
wherein when the sensing signal is not the noise, the processing unit executes the gesture event.

### Claim 15 (depends on 14)

The performing device as claimed in claim 14, wherein when the first noise neural network determines that the sensing signal is the noise, the processing unit receives another sensing signal from the sensing unit.

### Claim 16 (depends on 14)

The performing device as claimed in claim 14, wherein the first recognition neural network is a convolutional neural network (CNN) or a Long Short Memory (LSTM) network.

### Claim 17 (depends on 14)

The performing device as claimed in claim 14, wherein the first noise neural network is a fully-connected neural network.

### Claim 18 (depends on 14)

The performing device as claimed in claim 14, wherein the sensing signal is a Range Doppler Image (RDI) signal.

### Claim 19 (depends on 14)

The performing device as claimed in claim 14, wherein the sensing unit is a Doppler radar.