CN110811578A - Step counting device and step counting method thereof, controller and readable storage medium - Google Patents
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Abstract
The invention discloses a step counting method, which comprises the following steps: acquiring acceleration information and heart rate information of a target object; determining the motion state of the target object according to the acceleration information and the heart rate information; and analyzing the acceleration information according to the motion state, and determining the step number of the target object. The invention also discloses a controller, a step counting device and a readable storage medium. The invention aims to accurately identify different motion states of a target object and improve the accuracy of step number detection in different motion states.
Description
Technical Field
The invention relates to the technical field of step counting, in particular to a step counting method, a controller, a step counting device and a readable storage medium.
Background
With the improvement of living standard, people are more and more concerned about their health, and recording their life through the number of steps has become a common life style. The activities of walking, jogging, running and the like are the most common and popular activities in daily life of people, so that the deep research of a step-counting algorithm has very important significance.
The step-counting algorithm needs to collect data of the sensor in various motion states, such as slow walking, fast walking, still, mobile phone holding, mobile phone pocket motion and the like. However, at present, different motion states of the object to be measured are identified based on data of the acceleration sensor, and the data detected by the acceleration sensor is easily affected by non-gait motions of a user, the position of the sensor and the like, so that data characteristics of the different motion states are difficult to judge only by the data of the acceleration sensor, different motion states of the object cannot be accurately identified, and a large error exists in step counting.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide an air conditioner control method, which aims to accurately identify different motion states of a target object and improve the accuracy of step number detection in different motion states.
In order to achieve the above object, the present invention provides a step counting method, comprising the steps of:
acquiring acceleration information and heart rate information of a target object;
determining the motion state of the target object according to the acceleration information and the heart rate information;
and analyzing the acceleration information according to the motion state, and determining the step number of the target object.
Optionally, the step of determining the motion state of the target object according to the acceleration information and the heart rate information includes:
determining a plurality of alternative states according to the acceleration information;
and determining the motion state of the target object in the alternative state according to the heart rate information.
Optionally, the step of determining the motion state of the target object in the alternative state according to the heart rate information includes:
acquiring a preset heart rate range corresponding to each alternative state;
determining a preset heart rate range in which the heart rate information is positioned as a target range;
and taking the alternative state corresponding to the target range as the motion state of the target object.
Optionally, the step of determining a plurality of candidate states according to the acceleration information includes:
and processing the acceleration information by adopting a preset classifier based on machine learning, and determining the plurality of alternative states.
Optionally, the preset classifier is a decision tree classifier, the processing the acceleration information by using a preset classifier based on machine learning, and before the step of determining the plurality of candidate states, the method further includes:
acquiring acceleration data of a detected object detected by an acceleration sensor in a plurality of different preset motion states;
determining a training sample according to the acceleration data;
training the characteristic parameters of the decision tree classifier by using the training samples;
and constructing the decision tree classifier according to the characteristic parameters.
Optionally, the step counting method further includes:
and dividing the preset motion state based on the motion characteristics of the measured object and the carrying state of the acceleration sensor.
Optionally, the acceleration data is triaxial acceleration data, and the step of determining the training sample according to the acceleration data includes:
respectively selecting preset feature data from each axis data of the acceleration data to obtain a feature vector;
taking the data obtained after the dimensionality reduction of the feature vector as the sample;
and selecting partial data from the samples as the training samples.
Optionally, the analyzing the acceleration information according to the motion state, and determining the number of steps of the target object includes:
determining a corresponding peak detection algorithm according to the motion state;
analyzing a peak value in the acceleration information by adopting a determined peak value detection algorithm;
and determining the step number of the target object according to the peak value obtained by analysis.
In addition, this application also proposes a controller, the controller including: a memory, a processor and a step-counting program stored on the memory and executable on the processor, the step-counting program implementing the steps of the step-counting method as claimed in any one of the above when executed by the processor.
In addition, this application still proposes a meter step device, meter step device includes:
an acceleration sensor;
a heart rate sensor; and
the controller as described above, the acceleration sensor and the heart rate sensor are both connected to the controller.
Furthermore, the present application also proposes a readable storage medium having stored thereon a step-counting program which, when executed by a processor, implements the steps of the step-counting method according to any one of the above.
The invention provides a step counting method, which combines acceleration information and heart rate information to determine the motion state of a target object, analyzes the acceleration information according to the determined motion state to obtain the step number of the target object, realizes accurate judgment of the motion state because the step number detection does not singly adopt the acceleration data to identify the motion state of the target object, but combines the motion states of the acceleration and heart rate target object to realize accurate judgment of the motion state, analyzes the acceleration based on the determined motion state to obtain the step number, and adopts different step number analysis methods for different motion states, thereby effectively improving the accuracy of step number detection under different motion states.
Drawings
FIG. 1 is a schematic diagram of the hardware configuration involved in the operation of one embodiment of the step-counting device of the present invention;
FIG. 2 is a schematic flow chart of a step counting method according to a first embodiment of the present invention;
FIG. 3 is a schematic flow chart of a step counting method according to a second embodiment of the present invention;
FIG. 4 is a flowchart illustrating a step counting method according to a third embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The main solution of the embodiment of the invention is as follows: acquiring acceleration information and heart rate information of a target object; determining the motion state of the target object according to the acceleration information and the heart rate information; and analyzing the acceleration information according to the motion state, and determining the step number of the target object.
In the prior art, the step number of the user in different states is detected only by means of data characteristic analysis of acceleration data, and the data detected by the acceleration sensor is easily influenced by non-gait actions of the user, the position of the sensor and the like, so that the step number statistics in different states is not accurate enough.
The invention provides the solution, and aims to accurately identify different motion states of a target object and improve the accuracy of step number detection in different motion states.
The invention provides a step counting device. The step counting device refers to any device with a step counting function, and can be a mobile terminal (such as a mobile phone) with the step counting function, and can also be wearable equipment (such as an intelligent bracelet) with the step counting function.
In the embodiment of the present invention, referring to fig. 1, the step counting device specifically includes an acceleration sensor 100, a heart rate sensor 200, and a controller 300. The acceleration sensor 100, the heart rate sensor 200, and the controller 300 may be integrally mounted together. In addition, in other embodiments, the controller 300 may be further provided independently of the acceleration sensor 100 and the heart rate sensor 200, and the controller 300 may acquire the detection data of the acceleration sensor 100 and the heart rate sensor 200 through wireless communication or the like.
Wherein, the object carrying (such as holding, putting in pocket, wearing) the step counting device can be used as the target object. The acceleration sensor 100 is mainly used to detect acceleration information of a target object, and the heart rate sensor 200 is mainly used to detect heart rate information of the target object.
In an embodiment of the present invention, a controller includes: a processor 3001, such as a CPU, memory 3002, or the like. The memory 1002 may be a high-speed RAM memory or a non-volatile memory (e.g., a disk memory). The memory 3002 may alternatively be a storage device separate from the processor 1001.
The processor 3001 is in communication with the memory 3002, the acceleration sensor 100, and the heart rate sensor 200, respectively. The processor 3001 may call the data related to step counting in the memory 3002 or save the data related to step counting to the memory 3002. The processor 3001 may also acquire acceleration data and heart rate data detected by it from the acceleration sensor 100 and the heart rate sensor 200.
Those skilled in the art will appreciate that the configuration of the device shown in fig. 1 is not intended to be limiting of the device and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, an air conditioner control program may be included in the memory 3002, which is a readable storage medium. In the apparatus shown in fig. 1, the processor 3001 may be configured to call an air conditioning control program stored in the memory 3002 and perform operations of the steps associated with the step counting method in the following embodiments.
The invention also provides a step counting method.
Referring to fig. 2, a first embodiment of the step counting method of the present invention is provided, the step counting method including:
step S10, acquiring acceleration information and heart rate information of the target object;
the target object is specifically an object needing step counting, and can be a human or an animal. Specifically, an object carrying the step-counting device may be used as the target object.
Acquiring detection data of an acceleration sensor in the step counting device at intervals of preset duration or continuously as acceleration information; and (3) acquiring detection data of the heart rate sensor of the step counting device as heart rate information through interval preset duration or continuous acquisition. It should be noted that the heart rate information and the acceleration information are detected synchronously. Here, the acceleration information is specifically triaxial acceleration information.
Step S20, determining the motion state of the target object according to the acceleration information and the heart rate information;
different acceleration information and different heart rate information correspond to different motion states. And fitting the corresponding relation among the acceleration information, the heart rate information and the motion state in advance. And determining the motion states corresponding to the current acceleration information and the heart rate information of the target object according to the corresponding relation obtained by pre-fitting. The motion characteristic parameters can be directly calculated according to the acceleration information and the heart rate information, different motion characteristic parameters can correspond to different motion states, and the corresponding motion states are obtained according to the motion characteristic parameters obtained through calculation and serve as the current motion states of the target object. In addition, the motion state of the target object possibly positioned can be preliminarily determined according to one of the acceleration information and the heart rate information, and then the current motion state of the target object can be determined in the possible motion state according to the other one of the acceleration information and the heart rate information.
Step S30, analyzing the acceleration information according to the motion state, and determining the number of steps of the target object.
Different acceleration information analysis methods can be correspondingly set for different motion states. The analysis method may specifically include a zero-crossing point detection method, a flat region detection method, a peak detection method, and the like. And analyzing the acceleration information according to the determined analysis method of the motion state to obtain the step number of the target object.
The step counting method provided by the embodiment of the invention determines the motion state of a target object by combining acceleration information and heart rate information, analyzes the acceleration information according to the determined motion state to obtain the step number of the target object, realizes accurate judgment of the motion state by combining the motion states of the acceleration and heart rate target objects instead of singly identifying the motion state of the target object by singly adopting acceleration data for detecting the step number, analyzes the acceleration based on the determined motion state to obtain the step number, and adopts different step number analysis methods for different motion states, so that the step number detection accuracy under different motion states can be effectively improved.
Specifically, step S30 includes:
step S31, determining a corresponding peak detection algorithm according to the motion state;
specifically, different motion states may be provided with different detection parameters (such as time intervals and pole detection criteria) of the peak detection algorithm.
Step S32, analyzing the peak value in the acceleration information by adopting a determined peak value detection algorithm;
the acceleration information formed by a plurality of acceleration data acquired continuously or at intervals of preset duration can form waveform data, and the waveform data is processed according to the peak detection algorithm corresponding to the determined detection parameters, so that the peak value (maximum value and minimum value) in the acceleration information can be identified.
And step S33, determining the step number of the target object according to the peak value obtained by analysis.
A maximum and a minimum are continuously identified, a step count can be characterized. Therefore, based on all the analyzed peaks, the number of steps of the target object can be determined.
In this embodiment, in the above manner, when the step number of the target object is detected based on the peak detection algorithm, the peak value detected by the user in different motion states can be more accurate, so that the step number of the target object counted according to the peak value in different motion states is more accurate.
Further, based on the first embodiment, a second embodiment of the step counting method of the present application is provided. In the second embodiment, referring to fig. 3, the step S20 includes:
step S21, determining a plurality of alternative states according to the acceleration information;
specifically, a plurality of motion states of the step number detection object, such as a stationary state, a walking state, a running state, an ascending/descending state, and the like, may be configured in advance. In order to determine the motion state of the target object more accurately, the plurality of pre-configured motion states may be further divided according to the motion states of the detection object represented by the acceleration sensor in different carrying manners (such as holding, putting in a pocket, wearing, and the like), such as a stationary state, a walking state (sensor on hand), a walking state (sensor on pocket), a running state (sensor on hand), a running state (sensor on pocket), a stair climbing state, and the like. The preset different motion states correspond to different preset requirements (such as acceleration range, change characteristics and the like) of acceleration information. And taking the preset motion state corresponding to all preset requirements met by the current acceleration information as the alternative state.
The alternative states here are all motion states that the target object may currently exist.
Step S22, determining the motion state of the target object in the alternative state according to the heart rate information.
Based on the heart rate characteristics of the tested object in different motion states, for example, the heart rate of the tested object is lower when the tested object slowly walks, and the heart rate of the tested object is faster when the tested object quickly walks. Different preset motion states can be correspondingly provided with different preset heart rate ranges. Based on the above, a preset heart rate range corresponding to each alternative state can be obtained; determining a preset heart rate range in which the heart rate information is positioned as a target range; and taking the alternative state corresponding to the target range as the motion state of the target object.
Furthermore, in other embodiments, each preset motion state may also be provided with different state characterizing parameters based on the heart rate. Therefore, the corresponding state characterizing parameters can be calculated according to the heart rate information, and the candidate state corresponding to the calculated state characterizing parameters can be used as the motion state of the target object.
In the embodiment, the acceleration information is adopted to primarily screen and determine the motion state of the target object to obtain the alternative state, and then the current motion state of the target object is determined by combining the heart rate information in the alternative state, so that the accuracy of the obtained motion state of the target object is ensured by primarily analyzing the acceleration information and further analyzing the heart rate information.
Further, based on the second embodiment, a third embodiment of the step counting method of the present application is provided. In the third embodiment, the step S21 includes:
and step S211, processing the acceleration information by adopting a preset classifier based on machine learning, and determining the plurality of alternative states.
The preset classifier based on machine learning is specifically obtained by acquiring acceleration data of a plurality of step detection objects in different motion states through a large amount of data, training and learning according to a preset algorithm, and can be used for distinguishing a data model of the motion states based on acceleration detection information. In this embodiment, the preset classifier is specifically a decision tree classifier. In other embodiments, other machine learning based classifiers are also possible.
And inputting the acceleration information into a preset classifier, wherein the output result of the preset classifier is in an alternative state.
In the embodiment, the candidate state is determined by combining the acceleration information and the preset classifier based on the machine learning, and the accuracy of the output result of the preset classifier based on the machine learning is high, so that the determined candidate state can be ensured to be accurate and effective. The decision tree classifier is adopted, so that the motion state of the target object can be accurately judged.
In this embodiment, referring to fig. 4, before step S211, the method further includes:
step S01, acquiring acceleration data of the detected object detected by the acceleration sensor in a plurality of different motion states;
the measured object is an object including a type to which the target object belongs. For example, if the target object is a human, the object to be measured includes at least a human. The number of objects to be measured is not limited, but the larger the number is, the better the corresponding acquired acceleration data is. The preset motion state refers to a state which is mainly divided according to different motion characteristics (such as motion speed, motion direction and the like) of the object to be measured, and the motion state can specifically include a static state, a walking state, a running state, a stair climbing state and the like. In addition to the accident that the motion state is divided according to the motion characteristics of the measured object, in order to make the motion state represented by the data detected by the acceleration sensor more accurate, the preset motion state can be divided based on the motion characteristics of the measured object and the carrying state of the acceleration sensor.
Step S02, determining a training sample according to the acceleration data;
specifically, all the acceleration data may be used as training samples, and a part of the acceleration data may also be used as training samples to generate the decision tree classifier. The acceleration data is specifically triaxial acceleration data in order to make the step number representation more accurate. Step S02 may specifically include: respectively selecting preset feature data from each axis data of the acceleration data to obtain a feature vector; taking the data obtained after the dimensionality reduction of the feature vector as the sample; and selecting partial data from the samples as the training samples. Specifically, M pieces of feature data (such as a maximum value, a minimum value, an average value, a variance, and the like) can be selected from the three-axis data acquired by the acceleration sensor, and M-3 × M pieces of feature data are obtained as feature vectors for distinguishing different motion states. And D, performing data dimension reduction on the extracted M data by using a linear discriminant analysis method. And establishing a training sample and a testing sample by taking the data subjected to the dimensionality reduction as a sample. Wherein the test samples are used to verify the accuracy of the decision tree model.
Step S03, training the characteristic parameters of the decision tree classifier by adopting the training samples;
and inputting the training samples into a training decision tree model to obtain the characteristic parameters of the decision tree model such as the maximum depth, the minimum sample number and the like.
And step S04, constructing the decision tree classifier according to the characteristic parameters.
And obtaining the decision tree classifier after obtaining all the characteristic parameters of the decision tree classifier.
In this embodiment, acceleration data of the measured object in a plurality of different preset motion states is obtained and used as a training sample of the decision tree classifier, so that the decision tree classifier can determine a motion state of the target object based on the actually detected acceleration information of the target object. The motion state is further classified by combining the carrying state of the sensor and the motion characteristics of the object to be detected, so that the motion state of the target object obtained by the decision tree classifier is more accurate.
In addition, an embodiment of the present invention further provides a readable storage medium, where an air conditioning control program is stored on the readable storage medium, and when the air conditioning control program is executed by a processor, the air conditioning control program implements the relevant steps of any of the above step counting methods.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention essentially or contributing to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, a pedometer, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (11)
1. A step counting method is characterized by comprising the following steps:
acquiring acceleration information and heart rate information of a target object;
determining the motion state of the target object according to the acceleration information and the heart rate information;
and analyzing the acceleration information according to the motion state, and determining the step number of the target object.
2. The step counting method of claim 1, wherein the step of determining the motion state of the target object based on the acceleration information and the heart rate information comprises:
determining a plurality of alternative states according to the acceleration information;
and determining the motion state of the target object in the alternative state according to the heart rate information.
3. The step counting method according to claim 2, wherein the step of determining the motion state of the target object in the alternative state based on the heart rate information comprises:
acquiring a preset heart rate range corresponding to each alternative state;
determining a preset heart rate range in which the heart rate information is positioned as a target range;
and taking the alternative state corresponding to the target range as the motion state of the target object.
4. A step-counting method according to claim 2 or 3, wherein said step of determining a number of alternative states from said acceleration information comprises:
and processing the acceleration information by adopting a preset classifier based on machine learning, and determining the plurality of alternative states.
5. The step counting method according to claim 4, wherein the preset classifier is a decision tree classifier, and the step of processing the acceleration information by using a preset classifier based on machine learning and determining the plurality of candidate states is preceded by the step of:
acquiring acceleration data of a detected object detected by an acceleration sensor in a plurality of different preset motion states;
determining a training sample according to the acceleration data;
training the characteristic parameters of the decision tree classifier by using the training samples;
and constructing the decision tree classifier according to the characteristic parameters.
6. The step counting method of claim 5, further comprising:
and dividing the preset motion state based on the motion characteristics of the measured object and the carrying state of the acceleration sensor.
7. The step counting method of claim 5, wherein the acceleration data is three-axis acceleration data, and the step of determining training samples from the acceleration data comprises:
respectively selecting preset feature data from each axis data of the acceleration data to obtain a feature vector;
taking the data obtained after the dimensionality reduction of the feature vector as the sample;
and selecting partial data from the samples as the training samples.
8. The step counting method according to claim 2 or 3, wherein the analyzing the acceleration information according to the motion state and determining the number of steps of the target object comprises:
determining a corresponding peak detection algorithm according to the motion state;
analyzing a peak value in the acceleration information by adopting a determined peak value detection algorithm;
and determining the step number of the target object according to the peak value obtained by analysis.
9. A controller, characterized in that the controller comprises: memory, processor and a step-counting program stored on the memory and executable on the processor, the step-counting program, when executed by the processor, implementing the steps of the step-counting method according to any one of claims 1 to 8.
10. A step counting device, characterized in that the step counting device comprises:
an acceleration sensor;
a heart rate sensor; and
the controller of claim 9, wherein the acceleration sensor and the heart rate sensor are both connected to the controller.
11. A readable storage medium, having stored thereon a step-counting program which, when executed by a processor, implements the steps of the step-counting method according to any one of claims 1 to 8.
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