CN113138679A - Processing method and device for six-axis sensing signals - Google Patents
Processing method and device for six-axis sensing signals Download PDFInfo
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Abstract
The invention discloses a method and a device for processing six-axis sensing signals, wherein the method comprises the following steps: acquiring a current sensing signal, wherein the current sensing signal is a six-axis sensing signal, and the current sensing signal comprises data of a plurality of sampling points and a timestamp of a first sampling point in the plurality of sampling points; judging whether the current sensing signal is effective or not; if the current sensing signal is valid, judging whether the current sensing signal is continuous with the previous sensing signal, wherein the previous sensing signal is the last stored sensing signal and comprises data of a plurality of sampling points and a timestamp of a first sampling point in the plurality of sampling points; and if the current sensing signal is continuous with the previous sensing signal, combining the current sensing signal with the previous sensing signal and storing after removing the timestamp of the first sampling point contained in the current sensing signal.
Description
Technical Field
The invention relates to the field of data processing, in particular to a method and a device for processing six-axis sensing signals.
Background
The six-axis sensing signal acquired by an Inertial Measurement Unit (IMU) is used as a tremor monitoring signal, so that the applicability is better, the probability of misjudgment and missing judgment of tremor in the monitoring process can be effectively reduced by using the six-axis sensing signal as the tremor monitoring signal, and higher requirements are provided for data storage and endurance capacity of the wearing equipment;
the data storage and battery endurance of the wearable device are improved from a hardware perspective, but the method sacrifices the convenience of the wearable device and improves the price cost of the wearable device, so that a method capable of improving the storage of six-axis sensing signal data and the endurance of work of the wearable device without changing the data storage and the battery endurance of the wearable device is urgently needed in the field.
Disclosure of Invention
The invention provides a method and a device for processing six-axis sensing signals, which at least solve the technical problems in the prior art.
The invention provides a processing method of six-axis sensing signals, which comprises the following steps:
acquiring a current sensing signal, wherein the current sensing signal is a six-axis sensing signal, and the current sensing signal comprises data of a plurality of sampling points and a timestamp of a first sampling point in the plurality of sampling points;
judging whether the current sensing signal is effective or not;
if the current sensing signal is valid, judging whether the current sensing signal is continuous with a previous sensing signal, wherein the previous sensing signal is a last stored sensing signal and comprises data of a plurality of sampling points and a timestamp of a first sampling point in the plurality of sampling points;
and if the current sensing signal is continuous with the previous sensing signal, combining the current sensing signal with the previous sensing signal and storing the combined signal after removing the timestamp of the first sampling point contained in the current sensing signal.
Wherein, the judging whether the current sensing signal is effective includes:
calculating a first effective value according to the following formula:
wherein x is an acceleration value of the wearable device in the x-axis direction contained in the current sensing signal, y is an acceleration value of the wearable device in the y-axis direction contained in the current sensing signal, and z is an acceleration value of the wearable device in the z-axis direction contained in the current sensing signal;
calculating a second effective value according to the following formula:
|roll|+|pitch|+|yaw|；
wherein roll is an angular velocity of the wearable device rotating around an x-axis included in the current sensing signal, pitch is an angular velocity of the wearable device rotating around a y-axis included in the current sensing signal, and yaw is an angular velocity of the wearable device rotating around a z-axis included in the current sensing signal;
and if the first effective value is larger than a first preset threshold value and the second effective value is also larger than a second preset threshold value, determining that the current sensing signal is effective.
If the first effective value is smaller than a first preset threshold value or the second effective value is smaller than a second preset threshold value, the method further comprises:
and determining that the current sensing signal is invalid, and discarding the current sensing signal.
Wherein, the judging step is to judge whether the current sensing signal is continuous with the previous sensing signal, and if the current sensing signal is discontinuous with the previous sensing signal, the method further comprises the following steps:
and storing the current sensing signal and the time stamp of the first sampling point.
After the current sensing signal and the previous sensing signal are combined and stored, the method further comprises the following steps:
and if the data storage capacity of the wearable device reaches a preset threshold value, the wearable device uploads the stored data to a cloud server.
Another aspect of the present invention provides a six-axis sensor signal processing apparatus, including:
the acquisition module is used for acquiring a current sensing signal, wherein the current sensing signal is a six-axis sensing signal, and the current sensing signal comprises data of a plurality of sampling points and a timestamp of a first sampling point in the plurality of sampling points;
the judging module is used for judging whether the current sensing signal is effective or not;
the judging module is further configured to judge whether the current sensing signal is continuous with a previous sensing signal if the current sensing signal is valid, where the previous sensing signal is a last stored sensing signal, and the previous sensing signal includes data of a plurality of sampling points and a timestamp of a first sampling point of the plurality of sampling points;
and the processing module is used for removing the timestamp of the first sampling point contained in the current sensing signal if the current sensing signal is continuous with the previous sensing signal, and then combining and storing the current sensing signal and the previous sensing signal.
Wherein, the device still includes:
a calculating module for calculating a first effective value according to the following formula:
wherein x is an acceleration value of the wearable device in the x-axis direction contained in the current sensing signal, y is an acceleration value of the wearable device in the y-axis direction contained in the current sensing signal, and z is an acceleration value of the wearable device in the z-axis direction contained in the current sensing signal;
the calculating module is further configured to calculate the second effective value according to the following formula:
|roll|+|pitch|+|yaw|；
wherein roll is an angular velocity of the wearable device rotating around an x-axis included in the current sensing signal, pitch is an angular velocity of the wearable device rotating around a y-axis included in the current sensing signal, and yaw is an angular velocity of the wearable device rotating around a z-axis included in the current sensing signal;
the judging module is further configured to determine that the current sensing signal is valid if the first effective value is greater than a first preset threshold and the second effective value is also greater than a second preset threshold.
The processing module is further configured to determine that the current sensing signal is invalid and discard the current sensing signal when the first valid value is smaller than a first preset threshold or the second valid value is smaller than a second preset threshold.
The processing module is further configured to store the current sensing signal and the timestamp of the first sampling point when the current sensing signal is not continuous with the previous sensing signal.
The processing module is further used for uploading the stored data to a cloud server when the data storage capacity of the wearable device reaches a preset threshold value.
In the scheme, data when the wearable device is not worn by a user is removed by judging whether the wearable device is worn or not, invalid data is removed by calculating a first effective value and a second effective value of a current sensing signal and comparing the first effective value with a first preset threshold value and a second preset threshold value, storage space required for storing six-axis sensing data is reduced, whether the current sensing signal is continuous with a previous sensing signal is judged, if the current sensing signal is continuous, the current sensing signal is combined with the previous sensing signal and then stored together, the step of storing a current sensing signal timestamp is reduced, the efficiency of storing the sensing signal is improved, the data stored in the wearable device is automatically uploaded to a cloud server when the data reaches the storage threshold value, continuous power consumption of real-time transmission data is avoided, and the cruising ability of the wearable device is improved.
Drawings
Fig. 1 is a schematic flow chart illustrating a processing method of six-axis sensing signals according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a six-axis sensing signal processing apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to improve the storage of six-axis sensing signal data and the endurance time of work of the wearable device, an embodiment of the present invention provides a processing method of six-axis sensing signals, as shown in fig. 1, the method includes:
step 101, collecting a current sensing signal, wherein the current sensing signal is a six-axis sensing signal, and the current sensing signal comprises data of a plurality of sampling points and a timestamp of a first sampling point in the plurality of sampling points.
The wearable device is provided with a six-Axis sensor which can detect the action of wearing or taking off the wearable device, when the wearable device is worn, the wearable device is determined to be worn, when the wearable device is taken off, the wearable device is determined to be taken off, when the state of the wearable device is worn, a current sensing signal is collected, the current sensing signal is a six-Axis sensing signal which contains data of a plurality of sampling points, the six-Axis sensing signal comprises data of an x Axis, a y Axis and a z Axis in a 3-Axis ACC and data of a Roll Axis, a Pitch Axis and a Yaw Axis in a 3-Axis GYRO, the x Axis, the y Axis and the z Axis are three axes of a standard three-dimensional space coordinate system, the Roll Axis, the Pitch Axis and the Yaw Axis are angular velocities generated by rotating around the x Axis, the y Axis and the z Axis respectively, and if the wearable device is not worn, the collected sensing signal is not generated by the action of the user, the wearable device is not effective, so that sensing signals are collected when the wearable device is worn;
if the wearable device is worn for 24 hours and the sampling rate is 50Hz, the continuously acquired six-axis sensor requires the following storage space:
6×50×2×60×60×24/1024/1024≈41.2MB；
if the total 10 hours of unworn time for the user to sleep and eat or other activities is removed, then the required storage space is:
(6×50×2×60×60×(24-2-8)+10*2)/1024/1024≈24.0MB；
the required storage space is reduced by about 40%.
And 102, judging whether the current sensing signal is effective or not.
Calculating a first effective value according to the following formula:
wherein x is an acceleration value of the wearable device in the x-axis direction contained in the current sensing signal, y is an acceleration value of the wearable device in the y-axis direction contained in the current sensing signal, and z is an acceleration value of the wearable device in the z-axis direction contained in the current sensing signal;
calculating a second effective value according to the following formula:
|roll|+|pitch|+|yaw|；
wherein roll is an angular velocity of the wearable device rotating around an x-axis included in the current sensing signal, pitch is an angular velocity of the wearable device rotating around a y-axis included in the current sensing signal, and yaw is an angular velocity of the wearable device rotating around a z-axis included in the current sensing signal;
and if the first effective value is larger than a first preset threshold value and the second effective value is also larger than a second preset threshold value, determining that the current sensing signal is effective.
According to the data of six axial directions in the current sensing signal: wearing equipment is at the ascending acceleration value of x axle direction, wearing equipment is at the ascending acceleration value of y axle direction, wearing equipment is at the ascending acceleration value of z axle direction, wearing equipment is around the angular velocity of x axle rotation, wearing equipment is around the angular velocity of y axle rotation and wearing equipment around the first virtual value of angular velocity calculation and the second virtual value of z axle rotation, and first preset threshold value and the preset threshold value of second have been preset to this embodiment, when first virtual value is greater than first preset threshold value and second virtual value also is greater than the preset threshold value of second, then it is the user production when doing the action to mean current sensing signal, then it is effective to confirm current sensing signal.
And if the first effective value is smaller than a first preset threshold value or the second effective value is smaller than a second preset threshold value, determining that the current sensing signal is invalid, and discarding the current sensing signal.
If the first effective value is smaller than the first preset threshold value or the second effective value is smaller than the second preset threshold value, the current sensing signal is generated when the user does not act, the current sensing signal is invalid, the current sensing signal is discarded, and the next processing is not needed.
Step 103, if the current sensing signal is valid, determining whether the current sensing signal is continuous with a previous sensing signal, where the previous sensing signal is a last stored sensing signal, and the previous sensing signal includes data of a plurality of sampling points and a timestamp of a first sampling point in the plurality of sampling points.
And if the current sensing signal is effective, judging whether the current sensing signal is continuous with the previous sensing signal, wherein the previous sensing signal is a previously stored sensing signal and comprises data of a plurality of sampling points and a timestamp of a first sampling point in the plurality of sampling points, and the timestamp can represent time information when the data of the first sampling point in the data comprising the plurality of sampling points in the previous sensing signal is acquired.
And 104, if the current sensing signal is continuous with the previous sensing signal, combining the current sensing signal with the previous sensing signal and storing the combined signals after removing the timestamp of the first sampling point contained in the current sensing signal.
If the current sensing signal is continuous with the previous sensing signal, the current sensing signal and the previous sensing signal are combined and stored, and the time stamps of all sampling points in the combined sensing signal can be obtained through the time stamp and the sampling rate calculation of the first sampling point in the data of the multiple sampling points in the previous sensing signal stored in the previous sensing signal, so that the time stamp of the first sampling point in the data of the multiple sampling points contained in the current sensing signal does not need to be stored, and the current sampling signal and the previous sampling signal can be directly stored after being combined.
In step 103, determining whether the current sensing signal and the previous sensing signal are continuous, and if the current sensing signal and the previous sensing signal are discontinuous, in an implementation manner, storing timestamps of the current sensing signal and the first sampling point;
and if the current sensing signal is discontinuous with the previous sensing signal, storing a plurality of sampling point data contained in the current sensing signal and the time stamp of the first sampling point in the plurality of sampling points together.
In step 104, after the current sensing signal and the previous sensing signal are merged and stored, in an implementation manner, if the data storage amount of the wearable device reaches a preset threshold, the wearable device uploads the stored data to the cloud server.
If the data storage capacity of the wearable device reaches a preset threshold value, namely the storage device in the wearable device is full, the wearable device uploads all data stored in the storage device to a cloud server for application and analysis;
if the user needs to check the data just collected by the wearable device, all data stored by the storage device in the wearable device can be manually uploaded to the cloud server for checking.
And after the sensing signal data is uploaded to the cloud server, if the timestamp of the Nth sampling point in a plurality of sampling points contained in the sensing signal is needed, the formula can be used according to the formula
For example, the time stamp of the first sampling point of the sensing signal is 1600000000, which represents that 20 points of 9 and 13 days of 2020, 26 minutes and 40 seconds are used for acquiring the first sampling point of the sensing signal, and at this time we need to acquire the time stamp of the 51 st sampling point of the sensing signalAnd the sampling rate is 50HZ, that is, data of 50 sampling points is collected in one second, then according to the formula
The time stamp of the 51 st sampling point in the sensing signal is calculated to be 1600000000 plus (51-1)/50, which is equal to 1600000001, namely 26 minutes, 41 seconds at 20 points of 9, 13 and 2020.
In the scheme, data when the wearable device is not worn by a user is removed by judging whether the wearable device is worn or not, invalid data is removed by calculating a first effective value and a second effective value of a current sensing signal and comparing the first effective value with a first preset threshold value and a second preset threshold value, storage space required for storing six-axis sensing data is reduced, whether the current sensing signal is continuous with a previous sensing signal is judged, if the current sensing signal is continuous, the current sensing signal is combined with the previous sensing signal and then stored together, the step of storing a current sensing signal timestamp is reduced, the efficiency of storing the sensing signal is improved, the data stored in the wearable device is automatically uploaded to a cloud server when the data reaches the storage threshold value, continuous power consumption of real-time transmission data is avoided, and the cruising ability of the wearable device is improved.
In order to implement the foregoing method, an embodiment of the present invention further provides a six-axis sensing signal processing apparatus, as shown in fig. 2, including:
the acquisition module 10 is used for acquiring a current sensing signal, wherein the current sensing signal is a six-axis sensing signal, and the current sensing signal comprises data of a plurality of sampling points and a timestamp of a first sampling point in the plurality of sampling points;
a judging module 20, configured to judge whether the current sensing signal is valid;
the determining module 20 is further configured to determine whether the current sensing signal is continuous with a previous sensing signal if the current sensing signal is valid, where the previous sensing signal is a last stored sensing signal, and the previous sensing signal includes data of a plurality of sampling points and a timestamp of a first sampling point of the plurality of sampling points;
and the processing module 30 is configured to, if the current sensing signal is continuous with the previous sensing signal, remove a timestamp of a first sampling point included in the current sensing signal, and then combine and store the current sensing signal and the previous sensing signal.
Wherein, the device still includes:
a calculating module 40, configured to calculate the first effective value according to the following formula:
wherein x is an acceleration value of the wearable device in the x-axis direction contained in the current sensing signal, y is an acceleration value of the wearable device in the y-axis direction contained in the current sensing signal, and z is an acceleration value of the wearable device in the z-axis direction contained in the current sensing signal;
the calculating module 40 is further configured to calculate the second effective value according to the following formula:
|roll|+|pitch|+|yaw|；
wherein roll is an angular velocity of the wearable device rotating around an x-axis included in the current sensing signal, pitch is an angular velocity of the wearable device rotating around a y-axis included in the current sensing signal, and yaw is an angular velocity of the wearable device rotating around a z-axis included in the current sensing signal;
the determining module 20 is further configured to determine that the current sensing signal is valid if the first valid value is greater than a first preset threshold and the second valid value is also greater than a second preset threshold.
The processing module 30 is further configured to determine that the current sensing signal is invalid and discard the current sensing signal when the first valid value is smaller than a first preset threshold or the second valid value is smaller than a second preset threshold.
The processing module 30 is further configured to store the current sensing signal and the timestamp of the first sampling point when the current sensing signal is not continuous with the previous sensing signal.
The processing module 30 is further configured to upload the stored data to a cloud server when the data storage amount of the wearable device reaches a preset threshold.
In addition to the above-described methods and apparatus, embodiments of the present application may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to the various embodiments of the present application described in the "exemplary methods" section of this specification, above.
The computer program product may be written with program code for performing the operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present application may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform steps in a method according to various embodiments of the present application described in the "exemplary methods" section above of this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing describes the general principles of the present application in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present application are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present application. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the foregoing disclosure is not intended to be exhaustive or to limit the disclosure to the precise details disclosed.
The block diagrams of devices, apparatuses, systems referred to in this application are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
It should also be noted that in the devices, apparatuses, and methods of the present application, the components or steps may be decomposed and/or recombined. These decompositions and/or recombinations are to be considered as equivalents of the present application.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the application to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
Claims (10)
1. A method for processing six-axis sensor signals, comprising:
acquiring a current sensing signal, wherein the current sensing signal is a six-axis sensing signal, and the current sensing signal comprises data of a plurality of sampling points and a timestamp of a first sampling point in the plurality of sampling points;
judging whether the current sensing signal is effective or not;
if the current sensing signal is valid, judging whether the current sensing signal is continuous with a previous sensing signal, wherein the previous sensing signal is a last stored sensing signal and comprises data of a plurality of sampling points and a timestamp of a first sampling point in the plurality of sampling points;
and if the current sensing signal is continuous with the previous sensing signal, combining the current sensing signal with the previous sensing signal and storing the combined signal after removing the timestamp of the first sampling point contained in the current sensing signal.
2. The method for processing six-axis sensing signals according to claim 1, wherein the determining whether the current sensing signal is valid comprises:
calculating a first effective value according to the following formula:
wherein x is an acceleration value of the wearable device in the x-axis direction contained in the current sensing signal, y is an acceleration value of the wearable device in the y-axis direction contained in the current sensing signal, and z is an acceleration value of the wearable device in the z-axis direction contained in the current sensing signal;
calculating a second effective value according to the following formula:
|roll|+|pitch|+|yaw|；
wherein roll is an angular velocity of the wearable device rotating around an x-axis included in the current sensing signal, pitch is an angular velocity of the wearable device rotating around a y-axis included in the current sensing signal, and yaw is an angular velocity of the wearable device rotating around a z-axis included in the current sensing signal;
and if the first effective value is larger than a first preset threshold value and the second effective value is also larger than a second preset threshold value, determining that the current sensing signal is effective.
3. The method for processing six-axis sensor signals according to claim 2, wherein if the first effective value is smaller than a first preset threshold or the second effective value is smaller than a second preset threshold, the method further comprises:
and determining that the current sensing signal is invalid, and discarding the current sensing signal.
4. The method for processing six-axis sensing signals according to claim 1, wherein the determining whether the current sensing signal is continuous with the previous sensing signal further comprises:
and storing the current sensing signal and the time stamp of the first sampling point.
5. The method for processing six-axis sensor signals according to claim 1, wherein after combining and storing the current sensor signal and the previous sensor signal, the method further comprises:
and if the data storage capacity of the wearable device reaches a preset threshold value, the wearable device uploads the stored data to a cloud server.
6. A six-axis sensor signal processing apparatus, comprising:
the acquisition module is used for acquiring a current sensing signal, wherein the current sensing signal is a six-axis sensing signal, and the current sensing signal comprises data of a plurality of sampling points and a timestamp of a first sampling point in the plurality of sampling points;
the judging module is used for judging whether the current sensing signal is effective or not;
the judging module is further configured to judge whether the current sensing signal is continuous with a previous sensing signal if the current sensing signal is valid, where the previous sensing signal is a last stored sensing signal, and the previous sensing signal includes data of a plurality of sampling points and a timestamp of a first sampling point of the plurality of sampling points;
and the processing module is used for removing the timestamp of the first sampling point contained in the current sensing signal if the current sensing signal is continuous with the previous sensing signal, and then combining and storing the current sensing signal and the previous sensing signal.
7. The six-axis sensor signal processing apparatus according to claim 6, further comprising:
a calculating module for calculating a first effective value according to the following formula:
wherein x is an acceleration value of the wearable device in the x-axis direction contained in the current sensing signal, y is an acceleration value of the wearable device in the y-axis direction contained in the current sensing signal, and z is an acceleration value of the wearable device in the z-axis direction contained in the current sensing signal;
the calculating module is further configured to calculate the second effective value according to the following formula:
|roll|+|pitch|+|yaw|；
wherein roll is an angular velocity of the wearable device rotating around an x-axis included in the current sensing signal, pitch is an angular velocity of the wearable device rotating around a y-axis included in the current sensing signal, and yaw is an angular velocity of the wearable device rotating around a z-axis included in the current sensing signal;
the judging module is further configured to judge whether the first effective value is greater than a first preset threshold value, and judge whether the second effective value is greater than a second preset threshold value;
the judging module is further configured to determine that the current sensing signal is valid if the first effective value is greater than a first preset threshold and the second effective value is also greater than a second preset threshold.
8. The six-axis sensor signal processing apparatus according to claim 7,
the processing module is further configured to determine that the current sensing signal is invalid and discard the current sensing signal when the first valid value is smaller than a first preset threshold or the second valid value is smaller than a second preset threshold.
9. The six-axis sensor signal processing apparatus according to claim 6,
and the processing module is further used for storing the current sensing signal and the timestamp of the first sampling point when the current sensing signal is discontinuous from the previous sensing signal.
10. The six-axis sensor signal processing method according to claim 6,
the processing module is further used for uploading the stored data to a cloud server when the data storage capacity of the wearable device reaches a preset threshold value.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150164377A1 (en) * | 2013-03-13 | 2015-06-18 | Vaidhi Nathan | System and method of body motion analytics recognition and alerting |
| US20170086711A1 (en) * | 2015-09-25 | 2017-03-30 | J-Mex Inc. | Motion recognition device and method |
| CN106777060A (en) * | 2016-12-09 | 2017-05-31 | 合肥国为电子有限公司 | Node type seismic detector data file management system and method |
| CN107943271A (en) * | 2016-10-10 | 2018-04-20 | 北京诺亦腾科技有限公司 | Exercise data detection method, apparatus and system |
| CN108542369A (en) * | 2018-04-16 | 2018-09-18 | 华南师范大学 | A kind of heart rate breathing detection method and system |
| CN111568405A (en) * | 2020-05-13 | 2020-08-25 | 南昌大学 | Wearable impact detection device based on MEMS six sensors |
-
2021
- 2021-04-01 CN CN202110357224.8A patent/CN113138679A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150164377A1 (en) * | 2013-03-13 | 2015-06-18 | Vaidhi Nathan | System and method of body motion analytics recognition and alerting |
| US20170086711A1 (en) * | 2015-09-25 | 2017-03-30 | J-Mex Inc. | Motion recognition device and method |
| CN107943271A (en) * | 2016-10-10 | 2018-04-20 | 北京诺亦腾科技有限公司 | Exercise data detection method, apparatus and system |
| CN106777060A (en) * | 2016-12-09 | 2017-05-31 | 合肥国为电子有限公司 | Node type seismic detector data file management system and method |
| CN108542369A (en) * | 2018-04-16 | 2018-09-18 | 华南师范大学 | A kind of heart rate breathing detection method and system |
| CN111568405A (en) * | 2020-05-13 | 2020-08-25 | 南昌大学 | Wearable impact detection device based on MEMS six sensors |
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