CN112965605A - Short-range capacitive static gesture recognition system and method - Google Patents
Short-range capacitive static gesture recognition system and method Download PDFInfo
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- CN112965605A CN112965605A CN202110347397.1A CN202110347397A CN112965605A CN 112965605 A CN112965605 A CN 112965605A CN 202110347397 A CN202110347397 A CN 202110347397A CN 112965605 A CN112965605 A CN 112965605A
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- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/017—Gesture based interaction, e.g. based on a set of recognized hand gestures
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- G06F3/044—Digitisers, e.g. for touch screens or touch pads, characterised by the transducing means by capacitive means
- G06F3/0446—Digitisers, e.g. for touch screens or touch pads, characterised by the transducing means by capacitive means using a grid-like structure of electrodes in at least two directions, e.g. using row and column electrodes
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- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
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- G06V40/113—Recognition of static hand signs
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- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
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Abstract
The invention relates to a short-range capacitance type static gesture recognition system, which comprises: the capacitance sensor module adopts an electrode array mode, and switches various working electrodes through an analog switch to form various capacitance sensors; the capacitance value detection device is used for sensing a plurality of capacitance value variable quantities corresponding to the same gesture; the capacitance detection signal acquisition circuit is used for measuring and transmitting all capacitance values corresponding to each gesture; and the upper computer is used for controlling the receiving and storing of the capacitance data, simultaneously processing and analyzing the capacitance data acquired each time, and judging which gesture belongs to and displaying. The invention also discloses an identification method of the short-range capacitance type static gesture identification system. The invention is not influenced by light, complex background and other environmental factors, and has high identification precision; the invention has simple design, low cost and convenient use; the invention adopts the machine learning algorithm, reduces the operation amount of the algorithm under the condition of ensuring the identification precision, thereby ensuring the real-time property of the system.
Description
Technical Field
The invention relates to the technical field of sensors and application, in particular to a short-range capacitance type static gesture recognition system and a short-range capacitance type static gesture recognition method.
Background
With the explosive growth of man-machine interaction applications such as smart homes and motion sensing games, the development direction of man-machine interaction technology can be brought, brand-new experience can be brought, the man-machine interaction technology is easy to use, and the cost is low. Gesture recognition technology plays an important role in the field of human-computer interaction, and is particularly popular in aspects of engineering control, entertainment life, military safety, exhibition and display and the like.
Currently, the common gesture recognition is mainly divided into two types, namely wearable device-based and computer vision-based. The gesture recognition based on the wearable equipment is slightly influenced by the outside, can capture more precise actions, and has high sensitivity, good dynamic performance and wide movable range; the gesture recognition based on computer vision is high in precision and fast in speed, and the technology is not enough in that hardware equipment with high configuration is needed, and the technology is easily influenced by external environment, such as target capture which cannot be well completed under the conditions that the background is disordered, a shelter exists, the visual angle is blocked, the ambient light is dim and the like.
Disclosure of Invention
The invention aims to provide a short-range capacitance type static gesture recognition system which is high in recognition accuracy, low in cost and convenient to use.
In order to achieve the purpose, the invention adopts the following technical scheme: a proximity capacitance static gesture recognition system, the system comprising:
the capacitance sensor module adopts an electrode array mode, and switches various working electrodes through an analog switch to form various capacitance sensors; the capacitance value detection device is used for sensing a plurality of capacitance value variable quantities corresponding to the same gesture;
the capacitance detection signal acquisition circuit is used for measuring and transmitting all capacitance values corresponding to each gesture;
and the upper computer is used for controlling the receiving and storing of the capacitance data, simultaneously processing and analyzing the capacitance data acquired each time, and judging which gesture belongs to and displaying.
The capacitance detection signal acquisition circuit includes:
the integrated capacitance measuring chip is used for measuring the capacitance value of the capacitance sensor;
the main controller is used for realizing real-time capacitance data acquisition, processing and transmission;
and the analog switch is used for switching the electrode array into different electrodes to enter a working state, and the electrode array is configured into various capacitive sensors through the combination of the working electrodes.
Another object of the present invention is to provide a method for recognizing a short-range capacitive static gesture recognition system, which comprises the following steps:
(1) in a monitoring area of gesture recognition, a plurality of electrodes are laid on a capacitive sensor module in an electrode array mode;
(2) the capacitance detection signal acquisition circuit is switched by an analog switch, and a plurality of working electrodes are combined to form a plurality of capacitance sensors according to a plurality of sensitive configuration modes;
(3) the capacitance detection signal acquisition circuit acquires capacitance values of the capacitance sensors configured in the previous step and uploads the capacitance values to an upper computer;
(4) taking capacitance values of different capacitance sensors as input variables C of a machine learning classification model [ C1C 2 C3. ], performing model training by taking corresponding static gestures as output variables of the machine learning classification model, and establishing a machine learning classification model of the static gestures, wherein C1C 2 C3. represents capacitance values of a first sensor, a second sensor and a third sensor;
(5) when a static gesture is input in a gesture recognition monitoring area, a capacitance detection signal acquisition circuit acquires signals of each capacitance sensor and uploads the signals to an upper computer;
(6) and inputting variables composed of capacitance values of different capacitance sensors into a machine learning classification model utilizing the static gesture to obtain a static gesture classification result.
The sensitive configuration mode in the step (2) refers to a combination mode of an electrode array, that is, when the spatial positions of the transmitting electrode and the receiving electrode of the sensor are different, the electromagnetic field spatial distribution is different, the brought object sensitive areas are different, and the field energy intensity is different.
The electrode array is 4 x 4 of 14 sensitive configurations:
wherein T represents a transmitting electrode, R represents a receiving electrode, and N represents no electrical connection; SF1 to SF14 are 14 sensitive configurations.
According to the technical scheme, the beneficial effects of the invention are as follows: the invention is not influenced by light, complex background and other environmental factors, and has high identification precision; the invention has simple design, low cost and convenient use; the invention adopts the machine learning algorithm, reduces the operation amount of the algorithm under the condition of ensuring the identification precision, thereby ensuring the real-time property of the system.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
fig. 2 is a block diagram of the system of the present invention.
Detailed Description
As shown in fig. 2, a proximity capacitance type static gesture recognition system includes:
the capacitance sensor module adopts an electrode array mode, and switches various working electrodes through an analog switch to form various capacitance sensors; the capacitance value detection device is used for sensing a plurality of capacitance value variable quantities corresponding to the same gesture;
the capacitance detection signal acquisition circuit is used for measuring and transmitting all capacitance values corresponding to each gesture;
and the upper computer is used for controlling the receiving and storing of the capacitance data, simultaneously processing and analyzing the capacitance data acquired each time, and judging which gesture belongs to and displaying.
The capacitance detection signal acquisition circuit includes:
the integrated capacitance measuring chip is used for measuring the capacitance value of the capacitance sensor;
the main controller is used for realizing real-time capacitance data acquisition, processing and transmission;
and the analog switch is used for switching the electrode array into different electrodes to enter a working state, and the electrode array is configured into various capacitive sensors through the combination of the working electrodes.
As shown in fig. 1, the method comprises the following sequential steps:
(1) in a monitoring area of gesture recognition, a plurality of electrodes are laid on a capacitive sensor module in an electrode array mode;
(2) the capacitance detection signal acquisition circuit is switched by an analog switch, and a plurality of working electrodes are combined to form a plurality of capacitance sensors according to a plurality of sensitive configuration modes;
(3) the capacitance detection signal acquisition circuit acquires capacitance values of the capacitance sensors configured in the previous step and uploads the capacitance values to an upper computer;
(4) taking capacitance values of different capacitance sensors as input variables C of a machine learning classification model [ C1C 2 C3. ], performing model training by taking corresponding static gestures as output variables of the machine learning classification model, and establishing a machine learning classification model of the static gestures, wherein C1C 2 C3. represents capacitance values of a first sensor, a second sensor and a third sensor;
(5) when a static gesture is input in a gesture recognition monitoring area, a capacitance detection signal acquisition circuit acquires signals of each capacitance sensor and uploads the signals to an upper computer;
(6) and inputting variables composed of capacitance values of different capacitance sensors into a machine learning classification model utilizing the static gesture to obtain a static gesture classification result.
The sensitive configuration mode in the step (2) refers to a combination mode of an electrode array, that is, when the spatial positions of the transmitting electrode and the receiving electrode of the sensor are different, the electromagnetic field spatial distribution is different, the brought object sensitive areas are different, and the field energy intensity is different.
The electrode array is 4 x 4 of 14 sensitive configurations:
wherein T represents a transmitting electrode, R represents a receiving electrode, and N represents no electrical connection; SF1 to SF14 are 14 sensitive configurations.
In conclusion, the method is not influenced by light, complex background and other environmental factors, and has high identification precision; the invention has simple design, low cost and convenient use; the invention adopts the machine learning algorithm, reduces the operation amount of the algorithm under the condition of ensuring the identification precision, thereby ensuring the real-time property of the system.
Claims (5)
1. A short-range capacitive static gesture recognition system, comprising: the system comprises:
the capacitance sensor module adopts an electrode array mode, and switches various working electrodes through an analog switch to form various capacitance sensors; the capacitance value detection device is used for sensing a plurality of capacitance value variable quantities corresponding to the same gesture;
the capacitance detection signal acquisition circuit is used for measuring and transmitting all capacitance values corresponding to each gesture;
and the upper computer is used for controlling the receiving and storing of the capacitance data, simultaneously processing and analyzing the capacitance data acquired each time, and judging which gesture belongs to and displaying.
2. The proximity capacitive static gesture recognition method of claim 1, wherein: the capacitance detection signal acquisition circuit includes:
the integrated capacitance measuring chip is used for measuring the capacitance value of the capacitance sensor;
the main controller is used for realizing real-time capacitance data acquisition, processing and transmission;
and the analog switch is used for switching the electrode array into different electrodes to enter a working state, and the electrode array is configured into various capacitive sensors through the combination of the working electrodes.
3. The method of recognition in a proximity capacitive static gesture recognition system of any one of claims 1 to 2, wherein: the method comprises the following steps in sequence:
(1) in a monitoring area of gesture recognition, a plurality of electrodes are laid on a capacitive sensor module in an electrode array mode;
(2) the capacitance detection signal acquisition circuit is switched by an analog switch, and a plurality of working electrodes are combined to form a plurality of capacitance sensors according to a plurality of sensitive configuration modes;
(3) the capacitance detection signal acquisition circuit acquires capacitance values of the capacitance sensors configured in the previous step and uploads the capacitance values to an upper computer;
(4) taking capacitance values of different capacitance sensors as input variables C of a machine learning classification model [ C1C 2C 3 ], performing model training by taking corresponding static gestures as output variables of the machine learning classification model, and establishing the machine learning classification model of the static gestures, wherein C1C 2C 3.
(5) When a static gesture is input in a gesture recognition monitoring area, a capacitance detection signal acquisition circuit acquires signals of each capacitance sensor and uploads the signals to an upper computer;
(6) and inputting variables composed of capacitance values of different capacitance sensors into a machine learning classification model utilizing the static gesture to obtain a static gesture classification result.
4. The identification method according to claim 3, characterized in that: the sensitive configuration mode in the step (2) refers to a combination mode of an electrode array, that is, when the spatial positions of the transmitting electrode and the receiving electrode of the sensor are different, the electromagnetic field spatial distribution is different, the brought object sensitive areas are different, and the field energy intensity is different.
5. The identification method according to claim 4, characterized in that: the electrode array is 4 x 4 of 14 sensitive configurations:
SF1:
SF2:
SF3:
SF4:
SF5:
SF6:
SF7:
SF8:
SF9:
SF10:
SF11:
SF12:
SF13:
SF14:
wherein T represents a transmitting electrode, R represents a receiving electrode, and N represents no electrical connection; SF1 to SF14 are 14 sensitive configurations.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117742502A (en) * | 2024-02-08 | 2024-03-22 | 安徽大学 | Dual-mode gesture recognition system and method based on capacitance and distance sensor |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103067018A (en) * | 2012-12-18 | 2013-04-24 | 天津大学 | 12-digital segmentation capacity digital analogy converter circuit with adjustable quantization range |
| CN111121607A (en) * | 2019-12-13 | 2020-05-08 | 深圳大学 | Method for training three-dimensional positioning model and three-dimensional positioning method and device |
| US20200292601A1 (en) * | 2019-03-13 | 2020-09-17 | Aisin Seiki Kabushiki Kaisha | Capacitance detection device |
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- 2021-03-31 CN CN202110347397.1A patent/CN112965605A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103067018A (en) * | 2012-12-18 | 2013-04-24 | 天津大学 | 12-digital segmentation capacity digital analogy converter circuit with adjustable quantization range |
| US20200292601A1 (en) * | 2019-03-13 | 2020-09-17 | Aisin Seiki Kabushiki Kaisha | Capacitance detection device |
| CN111121607A (en) * | 2019-12-13 | 2020-05-08 | 深圳大学 | Method for training three-dimensional positioning model and three-dimensional positioning method and device |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117742502A (en) * | 2024-02-08 | 2024-03-22 | 安徽大学 | Dual-mode gesture recognition system and method based on capacitance and distance sensor |
| CN117742502B (en) * | 2024-02-08 | 2024-05-03 | 安徽大学 | Dual-mode gesture recognition system and method based on capacitance and distance sensor |
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