CN104914991A - Wearable intelligent bracelet gesture recognition method and device - Google Patents
Wearable intelligent bracelet gesture recognition method and device Download PDFInfo
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- CN104914991A CN104914991A CN201510117688.6A CN201510117688A CN104914991A CN 104914991 A CN104914991 A CN 104914991A CN 201510117688 A CN201510117688 A CN 201510117688A CN 104914991 A CN104914991 A CN 104914991A
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Abstract
The invention relates to a wearable intelligent bracelet gesture recognition device. A control method includes the steps that a gesture instruction sending module sends a gesture needing judging to a microprocessor; the microprocessor drives a position sensor to collect data through a DMP library and judges whether the gesture needs recognizing according to the collected data. The wearable intelligent bracelet gesture recognition device comprises the position sensor, the micro processor and the gesture instruction sending module, wherein the position sensor is an MP9150 module, the microprocessor is an STM32F103 single-chip microcomputer, the gesture instruction sending module comprises a mobile phone app for sending instructions, and instructions are transmitted through a Bluetooth module. When in work, the gesture instruction sending module sends a gesture needing judging to the microprocessor, and the microprocessor drives the position sensor to collect data through the DMP library and judges whether the gesture needs recognizing according to the collected data. The wearable intelligent bracelet gesture recognition device is simple, saves cost and is high in gesture recognition rate.
Description
Technical field
The invention belongs to intelligent wearable bracelet technical field, be specifically related to a kind of wearable Intelligent bracelet gesture identification method and device thereof.
Background technology
Because the action of staff is a lot, gesture is complicated, and different actions has overlapping part sometimes, makes gesture identification become one of difficult problem of research at present.The method of current gesture identification has a lot, and such as Myo bracelet utilizes bioelectricity, judges gesture by the current potential detecting muscle.For another example the patent No. is CN 103885645 li of described gesture judging methods, by Image sensor apparatus identification gesture.These recognition methodss all more complicated, gesture identification accuracy rate is not high simultaneously.If due to the identification carrying out various gesture during gesture identification simultaneously, disturb very large between different gestures, cause discrimination low.
Also part MPU6050 is had to carry out identifying the method (as patent No. CN 103593055) of gesture at present, but just use MPU6050 to carry out the collection of raw data, mostly Kalman filtering algorithm is used when data are processed, the performance of this algorithm to microprocessor has high requirements, and requires that speed wants fast.Many times must could realize gesture identification with two microprocessors, one is used process data, and one is carried out gesture identification, considerably increases the cost of gesture identification and the complexity of hardware circuit like this.
Summary of the invention
The present invention, for solving the problem, provides a kind of wearable Intelligent bracelet gesture identification method and device thereof.
A kind of wearable Intelligent bracelet gesture identifying device, comprises position transducer, microprocessor and gesture instruction sending module; Described position transducer is MP9150 module, described MPU9150 module integration three-axis gyroscope, three axis accelerometer, three axle magnetometers, an extendible digital moving processor DMP and I2C interface; Described microprocessor is STM32F103, and described MPU9150 module is electrically connected with described microprocessor by I2C interface; Described gesture instruction sending module comprises mobile phone A PP, and it is electrically connected with described microprocessor by bluetooth module; Shown bluetooth module is CC2540, and it is electrically connected with described microprocessor by serial ports.
Above-mentioned wearable Intelligent bracelet gesture identification method, being sent by gesture instruction sending module needs the gesture judged to microprocessor, and microprocessor judges whether it is the gesture needing to identify by DMP storehouse activation point sensor image data and microprocessor by the data collected.
Preferably, described gesture identification method comprises the following steps:
A STM32 initialization;
B MPU9150 initialization;
C STM32 is by DMP storehouse write MPU9150;
D STM32 reads acceleration, angular velocity, hypercomplex number, obtains Eulerian angle;
E judges whether gesture instruction sending module sends instruction, if so, then carries out next step, if not, then returns previous step;
F calls corresponding gesture function and carries out judgement gesture identification;
Whether the gesture that g reads is consistent with the gesture needing to identify, if so, then carries out next step, if not, then returns previous step;
It is correct to gesture instruction sending module prompting gesture identification that h STM32 sends corresponding information.
Preferably, the inner DMP of the described crack MPU9150 of opening comprises the following steps:
A sensor settings;
Whether b sensor settings is successful, and if so, then carry out next step, if not, then program enters endless loop;
C Fifo sets and judges whether successfully, if so, then carries out next step, and if not, then program enters endless loop;
D DMP sampling rate sets;
Whether the setting of e sampling rate is successful, and if so, then carry out next step, if not, then program enters endless loop;
F opens DMP function;
Whether g unlatching DMP function is successful, and if so, then carry out next step, if not, then program enters endless loop;
H inceptive direction setting deviation;
Whether i initialization direction setting deviation is successful, and if so, then carry out next step, if not, then program enters endless loop;
J DMP is enable;
Whether k DMP is enable successful, and if so, then carry out next step, if not, then program enters endless loop;
L Fifo Speed Setting;
Whether m Fifo Speed Setting is successful, and if so, then carry out next step, if not, then program enters endless loop;
N DMP tests oneself;
Whether o DMP tests oneself successful, and if so, then carry out next step, if not, then program enters endless loop;
P opens DMP success.
Preferably, judge whether it is need the process of the gesture identified to comprise the following steps described in:
A reads MPU9150 data;
Whether b MPU9150 reference position, setting within the scope of reference position, if so, then being carried out next step, if not, then being returned previous step;
C writes down the Eulerian angle of current location, acceleration, angular velocity;
The d time delay a bit of time;
E reads MPU9150 data again;
F compares the data collected for twice and judges that whether gesture is correct, if so, then carries out next step, if not, returns step a;
G gesture identification terminates.
Wearable Intelligent bracelet gesture identification method provided by the invention and device thereof, the data driving MPU9150 to collect by DMP storehouse are the data handled well, directly can obtain acceleration, angular velocity, Eulerian angle, no longer need to carry out complicated Kalman filtering algorithm, alleviate the burden of microprocessor, no longer need use two microprocessors, reduce costs.Need the action judged can reduce the complexity of gesture identification in microprocessor write in advance, improve gesture identification rate.
Accompanying drawing explanation
Accompanying drawing described herein is used to provide a further understanding of the present invention, forms a application's part, does not form inappropriate limitation of the present invention, in the accompanying drawings:
Fig. 1 is the systematic schematic diagram of the embodiment of the present invention;
Fig. 2 is the overall workflow figure of gesture identification method;
Fig. 3 is the process flow diagram opening the inner DMP of MPU9150;
Fig. 4 is the process flow diagram identifying gesture.
Embodiment
Describe the present invention in detail below in conjunction with accompanying drawing and specific embodiment, be used for explaining the present invention in this illustrative examples of the present invention and explanation, but not as a limitation of the invention.
As shown in Figure 1, a kind of wearable Intelligent bracelet gesture identifying device, comprises position transducer, microprocessor and gesture instruction sending module; Described position transducer is MP9150 module, described MPU9150 module integration three-axis gyroscope, three axis accelerometer, three axle magnetometers, an extendible digital moving processor DMP and I2C interface; Described microprocessor is STM32F103, and described MPU9150 module is electrically connected with described microprocessor by I2C interface; Described gesture instruction sending module comprises mobile phone A PP, and it is electrically connected with described microprocessor by bluetooth module; Shown bluetooth module is CC2540, and it is electrically connected with described microprocessor by serial ports.
Above-mentioned wearable Intelligent bracelet gesture identification method, being sent by gesture instruction sending module needs the gesture judged to microprocessor, and microprocessor judges whether it is the gesture needing to identify by DMP storehouse activation point sensor image data and microprocessor by the data collected.
Wherein, as shown in Figure 2, described gesture identification method comprises the following steps:
A STM32 initialization;
B MPU9150 initialization;
C STM32 is by DMP storehouse write MPU9150;
D STM32 reads acceleration, angular velocity, hypercomplex number, obtains Eulerian angle;
E judges whether gesture instruction sending module sends instruction, if so, then carries out next step, if not, then returns previous step;
F calls corresponding gesture function and carries out judgement gesture identification;
Whether the gesture that g reads is consistent with the gesture needing to identify, if so, then carries out next step, if not, then returns previous step;
It is correct to gesture instruction sending module prompting gesture identification that h STM32 sends corresponding information.
Wherein, as shown in Figure 3, the inner DMP of described unlatching MPU9150 comprises the following steps:
A sensor settings;
Whether b sensor settings is successful, and if so, then carry out next step, if not, then program enters endless loop;
C Fifo sets and judges whether successfully, if so, then carries out next step, and if not, then program enters endless loop;
D DMP sampling rate sets;
Whether the setting of e sampling rate is successful, and if so, then carry out next step, if not, then program enters endless loop;
F opens DMP function;
Whether g unlatching DMP function is successful, and if so, then carry out next step, if not, then program enters endless loop;
H inceptive direction setting deviation;
Whether i initialization direction setting deviation is successful, and if so, then carry out next step, if not, then program enters endless loop;
J DMP is enable;
Whether k DMP is enable successful, and if so, then carry out next step, if not, then program enters endless loop;
L Fifo Speed Setting;
Whether m Fifo Speed Setting is successful, and if so, then carry out next step, if not, then program enters endless loop;
N DMP tests oneself;
Whether o DMP tests oneself successful, and if so, then carry out next step, if not, then program enters endless loop;
P opens DMP success.
Wherein, as shown in Figure 4, judge whether it is need the process of the gesture identified to comprise the following steps described in:
A reads MPU9150 data;
Whether b MPU9150 reference position, setting within the scope of reference position, if so, then being carried out next step, if not, then being returned previous step;
C writes down the Eulerian angle of current location, acceleration, angular velocity;
The d time delay a bit of time;
E reads MPU9150 data again;
F compares the data collected for twice and judges that whether gesture is correct, if so, then carries out next step, if not, returns step a;
G gesture identification terminates.
Above the technical scheme that the embodiment of the present invention provides is described in detail, apply specific case herein to set forth the principle of the embodiment of the present invention and embodiment, the explanation of above embodiment is only applicable to the principle helping to understand the embodiment of the present invention; Meanwhile, for one of ordinary skill in the art, according to the embodiment of the present invention, embodiment and range of application all will change, and in sum, this description should not be construed as limitation of the present invention.
Claims (5)
1. wearable Intelligent bracelet gesture knows a recognition device, it is characterized in that:
Comprise position transducer, microprocessor and gesture instruction sending module;
Described position transducer is MP9150 module, described MPU9150 module integration three-axis gyroscope, three axis accelerometer, three axle magnetometers, an extendible digital moving processor DMP and I2C interface;
Described microprocessor is STM32F103, and described MPU9150 module is electrically connected with described microprocessor by I2C interface;
Described gesture instruction sending module is electrically connected with described microprocessor by bluetooth module;
Described bluetooth module is CC2540, and it is electrically connected with described microprocessor by serial ports.
2. wearable Intelligent bracelet gesture knows a recognition methods, it is characterized in that comprising the following steps:
Gesture instruction sending module sends needs the gesture judged to microprocessor, and microprocessor judges whether it is the gesture needing to identify by DMP storehouse activation point sensor image data and microprocessor by the data collected.
3. wearable Intelligent bracelet gesture as claimed in claim 2 knows recognition methods, it is characterized in that described gesture identification method comprises the following steps:
A STM32 initialization;
B MPU9150 initialization;
C STM32 is by DMP storehouse write MPU9150;
D STM32 reads acceleration, angular velocity, hypercomplex number, obtains Eulerian angle;
E judges whether gesture instruction sending module sends instruction, if so, then carries out next step, if not, then returns previous step;
F calls corresponding gesture function and carries out judgement gesture identification;
Whether the gesture that g reads is consistent with the gesture needing to identify, if so, then carries out next step, if not, then returns previous step;
It is correct to gesture instruction sending module prompting gesture identification that h STM32 sends corresponding information.
4. wearable Intelligent bracelet gesture as claimed in claim 2 knows recognition methods, it is characterized in that the inner DMP of described unlatching MPU9150 comprises the following steps:
A sensor settings;
Whether b sensor settings is successful, and if so, then carry out next step, if not, then program enters endless loop;
C Fifo sets and judges whether successfully, if so, then carries out next step, and if not, then program enters endless loop;
D DMP sampling rate sets;
Whether the setting of e sampling rate is successful, and if so, then carry out next step, if not, then program enters endless loop;
F opens DMP function;
Whether g unlatching DMP function is successful, and if so, then carry out next step, if not, then program enters endless loop;
H inceptive direction setting deviation;
Whether i initialization direction setting deviation is successful, and if so, then carry out next step, if not, then program enters endless loop;
J DMP is enable;
Whether k DMP is enable successful, and if so, then carry out next step, if not, then program enters endless loop;
L Fifo Speed Setting;
Whether m Fifo Speed Setting is successful, and if so, then carry out next step, if not, then program enters endless loop;
N DMP tests oneself;
Whether o DMP tests oneself successful, and if so, then carry out next step, if not, then program enters endless loop;
P opens DMP success.
5. wearable Intelligent bracelet gesture as claimed in claim 2 knows recognition methods, judges whether that the process of the gesture being needs identification comprises the following steps described in it is characterized in that:
A reads MPU9150 data;
Whether b MPU9150 reference position, setting within the scope of reference position, if so, then being carried out next step, if not, then being returned previous step;
C writes down the Eulerian angle of current location, acceleration, angular velocity;
The d time delay a bit of time;
E reads MPU9150 data again;
F compares the data collected for twice and judges that whether gesture is correct, if so, then carries out next step, if not, returns step a;
G gesture identification terminates.
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| CN201510117688.6A CN104914991A (en) | 2015-03-17 | 2015-03-17 | Wearable intelligent bracelet gesture recognition method and device |
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| CN201510117688.6A CN104914991A (en) | 2015-03-17 | 2015-03-17 | Wearable intelligent bracelet gesture recognition method and device |
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| CN106598231A (en) * | 2016-11-22 | 2017-04-26 | 深圳市元征科技股份有限公司 | Gesture identification method and apparatus |
| CN106774889A (en) * | 2016-12-15 | 2017-05-31 | 广州大学 | The gesture identification method and device of wearable device |
| CN107247974A (en) * | 2017-06-30 | 2017-10-13 | 中国科学院计算技术研究所 | Fitness sport recognizing method and system based on multisource data fusion |
| CN108196678A (en) * | 2018-01-19 | 2018-06-22 | 昆山国显光电有限公司 | Gesture operation method and the electronic equipment with gesture operation function |
| CN109996371A (en) * | 2019-04-24 | 2019-07-09 | 深圳尚一互联技术有限公司 | The method of gesture control light for Intelligent bracelet |
| CN109992093A (en) * | 2017-12-29 | 2019-07-09 | 博世汽车部件(苏州)有限公司 | A kind of gesture comparative approach and gesture comparison system |
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| CN106598231A (en) * | 2016-11-22 | 2017-04-26 | 深圳市元征科技股份有限公司 | Gesture identification method and apparatus |
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| CN106774889A (en) * | 2016-12-15 | 2017-05-31 | 广州大学 | The gesture identification method and device of wearable device |
| CN106774889B (en) * | 2016-12-15 | 2020-09-11 | 广州大学 | Gesture recognition method and device of wearable device |
| CN107247974A (en) * | 2017-06-30 | 2017-10-13 | 中国科学院计算技术研究所 | Fitness sport recognizing method and system based on multisource data fusion |
| CN107247974B (en) * | 2017-06-30 | 2020-07-31 | 中国科学院计算技术研究所 | Body-building exercise identification method and system based on multi-source data fusion |
| CN109992093A (en) * | 2017-12-29 | 2019-07-09 | 博世汽车部件(苏州)有限公司 | A kind of gesture comparative approach and gesture comparison system |
| CN109992093B (en) * | 2017-12-29 | 2024-05-03 | 博世汽车部件(苏州)有限公司 | Gesture comparison method and gesture comparison system |
| CN108196678A (en) * | 2018-01-19 | 2018-06-22 | 昆山国显光电有限公司 | Gesture operation method and the electronic equipment with gesture operation function |
| CN109996371A (en) * | 2019-04-24 | 2019-07-09 | 深圳尚一互联技术有限公司 | The method of gesture control light for Intelligent bracelet |
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Inventor after: Liu Changhong Inventor after: Chen Haibing Inventor after: Tan Ziwei Inventor after: Zhang Hongkang Inventor after: Dan Xiaoming Inventor after: Yan Yier Inventor before: Chen Haibing Inventor before: Liu Changhong Inventor before: Tan Ziwei Inventor before: Zhang Hongkang Inventor before: Dan Xiaoming Inventor before: Yan Yier |
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Application publication date: 20150916 |