CN112370050A - Human body posture and motion energy consumption identification system and identification method - Google Patents
Human body posture and motion energy consumption identification system and identification method Download PDFInfo
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
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Abstract
A human body posture and motion energy consumption identification system and an identification method are provided. The method mainly comprises two main researches for solving the problem of gesture recognition at present, wherein the two researches are respectively carried out through image analysis and sensor analysis such as acceleration and a gyroscope. The former has extremely high requirements on acquisition equipment and does not have universality, and the latter is convenient to integrate and carry, but has the problems of very complex identification method for human body postures and no good robustness at the present stage. The invention comprises the following components: the system comprises wearing equipment, a smart phone end and a server end; wearing equipment include a six-axis acceleration sensor, microprocessor and a bluetooth module, acceleration sensor with microprocessor connect, microprocessor with bluetooth module connect for gather human attitude information, information filters the information that some errors are big through microprocessor module, then sends for the smart mobile phone end through bluetooth module. The invention is used for recognizing human body posture and motion energy consumption.
Description
Technical Field
The invention relates to a recognition method of a human body posture and motion energy consumption recognition system.
Background
Human motion is complex and variable and is often influenced by many factors. With the improvement of the social intelligence degree, the human posture information recognition and the research on the consumption of the motion energy have important significance. For example, the analysis of the fatigue state of a human body is greatly facilitated by the consumption of energy; the data of human motion can be more accurate collection to discernment one person's motion state, and there are two kinds to gesture recognition's research now mainly, are through image analysis and through sensor analysis such as acceleration, gyroscope respectively. The former has extremely high requirements on acquisition equipment and does not have universality, and the latter is convenient to integrate and carry, but has very complex and not good robustness on a human body posture identification method at the present stage.
Human body action recognition method based on three-axis acceleration sensor of Yunnan university. The method adopts the combined acceleration peak value of the three-axis acceleration signal as the center, and intercepts a small segment of signal as a human body action sample to be identified.
An intelligent stone lock, a motion state and motion energy consumption determination method and system of Nanjing Mingsi software Co. And further generating an acceleration curve set through a three-axis acceleration sensor to determine the motion state of the stone lock, or determining the energy consumption in the motion according to an energy consumption model and data of characteristic points obtained from the curve set.
The method for identifying human body actions based on the peak value of the triaxial acceleration sensor needs some preparation work such as noise removal of analog signals, and the process is quite complicated. The gesture recognition is performed based on the triaxial acceleration curve, which needs to draw the acquired data into an acceleration curve set for gesture recognition. Currently, most of the energy consumption calculation is determined by calculating the content of carbon dioxide in exhaled air or by measuring physiological indexes such as the saturation of a human blood sample.
The calculation of human body posture and energy is complex, and the current technical method with multiple influencing factors cannot simply and efficiently test results.
Disclosure of Invention
In order to solve the defects of the prior art, the invention aims to provide a recognition method of a human body posture and movement energy consumption recognition system, so as to overcome the defects in the prior art.
The human body posture and movement energy consumption identification system comprises wearing equipment, a smart phone end and a server end;
the wearable device comprises a six-axis acceleration sensor, a microprocessor and a Bluetooth module, wherein the acceleration sensor is connected with the microprocessor, the microprocessor is connected with the Bluetooth module and used for acquiring posture information of a human body, the information is filtered by the microprocessor module to remove information with large errors, and then the information is sent to the smart phone end through the Bluetooth module;
the smart phone end receives data sent by the Bluetooth module, stores the data locally, sends the data to the network interface end through the server uploading and downloading module of the mobile phone in an HTTP mode, and after the network interface end processes the human body data, the smart phone end downloads the processed attitude data and energy consumption data by accessing the network interface module of the server end and displays the data on the mobile phone end of the smart phone end through UI display;
after receiving the data sent by the smart phone end, the server end firstly stores the data in a database, then the data enters the data processing module for processing, and the processed attitude information and the processed movement energy consumption information can be downloaded by the smart phone through the network port module.
The human body posture and motion energy consumption identification system is characterized in that the six-axis acceleration sensor comprises a three-axis MEMS accelerometer and a three-axis MEMS gyroscope. The acceleration sensor can acquire three acceleration components and three rotation angular velocities, and comprises a self-contained data processing sub-module DMP.
A human gesture and athletic energy consumption identification system, bluetooth module adopt low-power consumption BLE bluetooth.
According to the human body posture and motion energy consumption identification system, the acceleration sensor can acquire three acceleration components and three rotation angular velocities, and the system comprises a self-contained data processing sub-module DMP.
The recognition method of the human body posture and motion energy consumption recognition system comprises the following steps: the gesture recognition is to utilize acceleration and angular velocity data of x, y and z three axes acquired by a six-axis acceleration sensor to define six variables of a, b, c, d, e and f, respectively correspond to the acceleration and the angular velocity of the x, y and z axes, test 10 groups of data each time in different time periods, respectively calculate the respective standard deviation of a, b, c, d, e and f in the 10 groups of data through a standard deviation calculation formula, and then substitute the formula of the comprehensive standard deviation to calculate the comprehensive standard deviation of 6 accelerations;
wherein i is 1,2,3,4,5, 6; j ═ 1,2,3, …, 10;
the integrated standard deviation is the integration of six axes of acceleration, i.e. the square root of the sum of the squares of the standard deviations of the six axes, and the integrated standard deviation formula:
wherein i is 1,2,3,4,5, 6;
wherein SDiRepresents the standard deviation of six axes where SDiThe standard deviation of six axes is shown, and 1-6 correspond to the acceleration and angular acceleration of x, y and z axes respectively;
the exercise energy consumption identification comprises the following steps:
and taking the intensity of the resultant motion as the intensity of the motion for fusion, wherein the intensity is represented by the following formula:
the SDsumThe module value of (2) is the comprehensive standard deviation, and the instantaneous motion energy consumption of the human body can be reflected by the data because the data in all directions are superposed;
and E is the energy consumption of the movement, but the data obtained by the attitude sensor is discrete data, and then the evaluation of the energy consumption of the human body in the movement process is described by the following formula:
E＝k×∑t(SDsum×Δt)
wherein: k is a motion energy coefficient, and is obtained by acquiring motion data of different human qualities and analyzing the motion data;
at is the time for measuring the energy consumption of the movement.
The invention has the beneficial effects that:
1. compared with the method that the combined acceleration peak value of the three-axis acceleration signal is taken as the center and a small segment of signal is intercepted to be used as a human body action sample to be identified. The novel method is simpler in calculation and clearer in reflected gesture recognition.
2. The invention has high recognition precision, more accurate acquisition equipment, higher practical value and the whole system is more fit with the current concept of the Internet of things. The whole wearing device is smaller and has better ergonomic design.
3. The calculation of the exercise energy consumption of the invention is more practical, and the human exercise consumption can be comprehensively evaluated by adding the time factor.
4. The data transmission of the whole system is in a wireless mode, the human motion data can be conveniently collected, and the accurate recognition of the human posture information and the calculation of the energy consumption of the motion are achieved. Can better play a role in the medical field of human body posture recognition and sports. For example, training can be performed with physical abilities of auxiliary athletes, and training status can be better assessed by looking at historical data. The posture recognition and motion energy consumption device can also be used for the existing medical health equipment, and the state characteristics of the human body can be more accurately judged through posture recognition and motion energy consumption, so that the accuracy and stability of the medical health equipment are ensured.
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FIG. 1 is a schematic diagram of the invention;
Detailed Description
To further understand the structure, characteristics and other objects of the present invention, the following detailed description is given with reference to the accompanying preferred embodiments, which are only used to illustrate the technical solutions of the present invention and are not to limit the present invention.
In a first specific embodiment, the human body posture and exercise energy consumption recognition system in this embodiment includes a wearable device, a smartphone terminal, and a server terminal;
the wearable device comprises a six-axis acceleration sensor, a microprocessor and a Bluetooth module, wherein the acceleration sensor is connected with the microprocessor, the microprocessor is connected with the Bluetooth module and used for acquiring posture information of a human body, the information is filtered by the microprocessor module to remove information with large errors, and then the information is sent to the smart phone end through the Bluetooth module;
the six-axis acceleration sensor comprises a three-axis MEMS accelerometer and a three-axis MEMS gyroscope. Data format: every 2 bytes are a small group, which respectively corresponds to the upper 8 bits and the lower 8 bits of the acceleration, and a large whole group has 12 bytes. The sampling frequency is 100HZ, the sampling precision is 16384LSB/mg, and the communication protocol of hardware communication is I2C. The Bluetooth module adopts Low-power-consumption BLE Bluetooth, the Bluetooth communication protocol is BLE (Bluetooth Low energy), the hardware communication protocol is UART (1), and the baud rate is set to 115200 (default 9600 and adjustable).
The smart phone end receives data sent by the Bluetooth module, stores the data locally, sends the data to the network interface end through the server uploading and downloading module of the mobile phone in an HTTP mode, and after the network interface end processes the human body data, the smart phone end downloads the processed attitude data and energy consumption data by accessing the network interface module of the server end and displays the data on the mobile phone end of the smart phone end through UI display;
after receiving the data sent by the smart phone end, the server end firstly stores the data in a database, then the data enters the data processing module for processing, and the processed attitude information and the processed movement energy consumption information can be downloaded by the smart phone through the network port module.
The six-axis acceleration sensor is used for collecting motion data of a human body, so that the human body can be simply and clearly identified to be in three states of static state, walking state and running state. And the energy consumed by the human body movement can be calculated through data center processing. The system can be used for detecting the physical condition of the human body and can also assist some medical systems and equipment. For example, an auxiliary electrocardiogram monitoring system, a cardiovascular health prediction system, etc. The accuracy of these systems can be improved by incorporating the determination of the body posture and the energy consumption profile of the body condition.
In a second embodiment, the present embodiment is a further description of the human body posture and exercise energy consumption recognition system in the first embodiment, and the six-axis acceleration sensor includes a three-axis MEMS accelerometer and a three-axis MEMS gyroscope. The acceleration sensor can acquire three acceleration components and three rotation angular velocities, and comprises a self-contained data processing sub-module DMP.
In a third specific embodiment, the present embodiment is a further description of the human body posture and exercise energy consumption recognition system described in the first specific embodiment, and the bluetooth module employs low-power BLE bluetooth.
In a fourth embodiment, the present embodiment is a further description of the human body posture and exercise energy consumption recognition system in the first embodiment, the acceleration sensor can acquire three acceleration components and three rotation angular velocities, and the acceleration sensor includes a self-contained data processing sub-module DMP.
In a fifth embodiment, the present embodiment is further described with respect to the recognition method of the human body posture and exercise energy consumption recognition system in the first embodiment, where the posture recognition includes the following steps: the gesture recognition is to utilize acceleration and angular velocity data of x, y and z three axes acquired by a six-axis acceleration sensor to define six variables of a, b, c, d, e and f, respectively correspond to the acceleration and the angular velocity of the x, y and z axes, test 10 groups of data each time in different time periods, respectively calculate the respective standard deviation of a, b, c, d, e and f in the 10 groups of data through a standard deviation calculation formula, and then substitute the formula of the comprehensive standard deviation to calculate the comprehensive standard deviation of 6 accelerations;
wherein i is 1,2,3,4,5, 6; j ═ 1,2,3, …, 10;
the integrated standard deviation is the integration of six axes of acceleration, i.e. the square root of the sum of the squares of the standard deviations of the six axes, and the integrated standard deviation formula:
wherein i is 1,2,3,4,5, 6;
wherein SDiRepresents the standard deviation of six axes where SDiThe standard deviation of six axes is shown, and 1-6 correspond to the acceleration and angular acceleration of x, y and z axes respectively;
the exercise energy consumption identification comprises the following steps:
and taking the intensity of the resultant motion as the intensity of the motion for fusion, wherein the intensity is represented by the following formula:
the SDsumThe module value of (2) is the comprehensive standard deviation, and the instantaneous motion energy consumption of the human body can be reflected by the data because the data in all directions are superposed;
and E is the energy consumption of the movement, but the data obtained by the attitude sensor is discrete data, and then the evaluation of the energy consumption of the human body in the movement process is described by the following formula:
E＝k×∑t(SDsum×Δt)
wherein: k is a motion energy coefficient, and is obtained by acquiring motion data of different human qualities and analyzing the motion data;
at is the time for measuring the energy consumption of the movement.
It should be noted that the above summary and the detailed description are intended to demonstrate the practical application of the technical solutions provided by the present invention, and should not be construed as limiting the scope of the present invention. Various modifications, equivalent substitutions, or improvements may be made by those skilled in the art within the spirit and principles of the invention. The scope of the invention is to be determined by the appended claims.
Claims (5)
1. A human body posture and movement energy consumption recognition system is characterized by comprising wearable equipment, a smart phone end and a server end;
the wearable device comprises a six-axis acceleration sensor, a microprocessor and a Bluetooth module, wherein the acceleration sensor is connected with the microprocessor, the microprocessor is connected with the Bluetooth module and used for acquiring posture information of a human body, the information is filtered by the microprocessor module to remove information with large errors, and then the information is sent to the smart phone end through the Bluetooth module;
the smart phone end receives data sent by the Bluetooth module, stores the data locally, sends the data to the network interface end through the server uploading and downloading module of the mobile phone in an HTTP mode, and after the network interface end processes the human body data, the smart phone end downloads the processed attitude data and energy consumption data by accessing the network interface module of the server end and displays the data on the mobile phone end of the smart phone end through UI display;
after receiving the data sent by the smart phone end, the server end firstly stores the data in a database, then the data enters the data processing module for processing, and the processed attitude information and the processed movement energy consumption information can be downloaded by the smart phone through the network port module.
2. The system for human body posture and motion energy expenditure identification of claim 1, wherein the six-axis acceleration sensor comprises a three-axis MEMS accelerometer and a three-axis MEMS gyroscope. The acceleration sensor can acquire three acceleration components and three rotation angular velocities, and comprises a self-contained data processing sub-module DMP.
3. The system according to claim 2, wherein the bluetooth module is BLE bluetooth low energy.
4. A human body posture and motion energy consumption recognition system as claimed in claim 3 wherein said acceleration sensor is capable of acquiring three acceleration components and three rotation angular velocities and wherein it comprises a self contained data processing sub-module DMP.
5. A recognition method of a human body posture and movement energy consumption recognition system as claimed in any one of claims 1 to 4, wherein the posture recognition comprises the steps of: the gesture recognition is to utilize acceleration and angular velocity data of x, y and z three axes acquired by a six-axis acceleration sensor to define six variables of a, b, c, d, e and f, respectively correspond to the acceleration and the angular velocity of the x, y and z axes, test 10 groups of data each time in different time periods, respectively calculate the respective standard deviation of a, b, c, d, e and f in the 10 groups of data through a standard deviation calculation formula, and then substitute the formula of the comprehensive standard deviation to calculate the comprehensive standard deviation of 6 accelerations;
wherein i is 1,2,3,4,5, 6; j ═ 1,2,3, …, 10;
the integrated standard deviation is the integration of six axes of acceleration, i.e. the square root of the sum of the squares of the standard deviations of the six axes, and the integrated standard deviation formula:
wherein i is 1,2,3,4,5, 6;
wherein SDiRepresents the standard deviation of six axes where SDiThe standard deviation of six axes is shown, and 1-6 correspond to the acceleration and angular acceleration of x, y and z axes respectively;
the exercise energy consumption identification comprises the following steps:
and taking the intensity of the resultant motion as the intensity of the motion for fusion, wherein the intensity is represented by the following formula:
the SDsumThe module value of (2) is the comprehensive standard deviation, and the instantaneous motion energy consumption of the human body can be reflected by the data because the data in all directions are superposed;
and E is the energy consumption of the movement, but the data obtained by the attitude sensor is discrete data, and then the evaluation of the energy consumption of the human body in the movement process is described by the following formula:
E＝k×∑t(SDsum×Δt)
wherein: k is a motion energy coefficient, and is obtained by acquiring motion data of different human qualities and analyzing the motion data;
at is the time for measuring the energy consumption of the movement.
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| KR20120130306A (en) * | 2011-05-22 | 2012-11-30 | 주식회사 머글 | Smart shoes system for improving healthcare |
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