WO2023134663A1 - Procédé d'identification de mouvement, appareil, dispositif électronique et support de stockage lisible - Google Patents

Procédé d'identification de mouvement, appareil, dispositif électronique et support de stockage lisible Download PDF

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Publication number
WO2023134663A1
WO2023134663A1 PCT/CN2023/071548 CN2023071548W WO2023134663A1 WO 2023134663 A1 WO2023134663 A1 WO 2023134663A1 CN 2023071548 W CN2023071548 W CN 2023071548W WO 2023134663 A1 WO2023134663 A1 WO 2023134663A1
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acceleration data
preset
value
average value
moment
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PCT/CN2023/071548
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English (en)
Chinese (zh)
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王丰
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维沃移动通信有限公司
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Publication of WO2023134663A1 publication Critical patent/WO2023134663A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality

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  • the present application belongs to the field of data processing, and in particular relates to a motion recognition method, device, electronic equipment and readable storage medium.
  • a gyroscope is used to acquire teeth brushing direction and angular velocity information, so as to identify brushing teeth movement based on the acquired teeth brushing direction and angular velocity information.
  • the power consumption of the gyroscope is relatively high, therefore, the use of the above detection scheme will shorten the battery life of electronic devices such as watches.
  • the purpose of the embodiments of the present application is to provide a motion recognition method, device, electronic device and readable storage medium, which can solve the problem that the existing solutions for recognizing brushing motion will shorten the battery life of electronic devices such as watches.
  • the embodiment of the present application provides a motion recognition method.
  • the motion recognition method includes: acquiring acceleration information of a wearable device, wherein the acceleration information includes first acceleration data in a first direction, and acceleration data in a second direction.
  • the second acceleration data on the above and the third acceleration data on the third direction, the first direction, the second direction and the third direction are perpendicular to each other; obtain the time between the first moment and the second moment within the first preset time The first time less than the preset time threshold, wherein the first moment is the moment when the extreme value appears in the first target acceleration data, and the second moment is the moment when the extreme value appears in the second target acceleration data, wherein the first target
  • the acceleration data and the second target acceleration data are any two acceleration data in the first acceleration data, the second acceleration data and the third acceleration data; it is determined that the first target acceleration data has an extreme value within the first preset time.
  • the second number determine the recognition result of the movement according to the ratio of the first number to the second number.
  • the embodiment of the present application provides a motion recognition device, including: an acquisition module configured to acquire acceleration information of a wearable device, wherein the acceleration information includes first acceleration data in a first direction, second acceleration data in a first direction, The second acceleration data in the direction and the third acceleration data in the third direction, the first direction, the second direction and the third direction are perpendicular to each other; the statistical module is used to obtain the first preset time, the first moment and the second acceleration data The time between the two moments is less than the first number of preset time thresholds, wherein the first moment is the moment when the extreme value appears in the first target acceleration data, and the second moment is the moment when the extreme value appears in the second target acceleration data , wherein, the first target acceleration data and the second target acceleration data are any two acceleration data in the first acceleration data, the second acceleration data and the third acceleration data; the determination module is used to determine the first target acceleration data at the The second number of occurrences of the extremum within a preset period of time; the identification module is used to determine the recognition
  • an embodiment of the present application provides an electronic device, including the above-mentioned motion recognition device.
  • an embodiment of the present application provides an electronic device, the electronic device includes a processor and a memory, and the memory stores programs or instructions that can run on the processor, and when the programs or instructions are executed by the processor, the first aspect is implemented. The steps of the motion recognition method.
  • the embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the motion recognition method in the first aspect are implemented.
  • the embodiment of the present application provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the steps of the motion recognition method in the first aspect.
  • an embodiment of the present application provides a computer program product, where the program product is stored in a storage medium, and the program product is executed by at least one processor to implement the method in the first aspect.
  • the motion identification scheme proposed can use the data detected by the acceleration sensor to realize motion identification. Since the power consumption of the acceleration sensor is lower than that of the gyroscope, the technology of this application is adopted While realizing motion recognition through the scheme, the impact of motion recognition on the battery life of the electronic device is reduced, and the battery life of the electronic device is improved.
  • the technical solution of the present application realizes motion recognition through the following methods. Specifically, when the user wears a wearable device while brushing teeth, such as back and forth, back and forth, right front and left back and forth, left front and right back and forth when brushing teeth , the acceleration data detected by the wearable device will be characterized, specifically, two of the first acceleration data in the first direction, the second acceleration data in the second direction, and the third acceleration data in the third direction Or the extreme values in the acceleration data in multiple directions appear relatively close to each other. Since brushing teeth is a process of constantly repeating the same action, the recognition of the movement can be realized by counting the proportion of extreme values satisfying the above conditions within the first preset time.
  • Fig. 1 is a schematic flow chart of a brushing motion recognition method in an embodiment of the present application
  • Fig. 2 is a schematic diagram of the acceleration data of the X-axis direction, the Y-axis direction, the Z-axis direction and the combined acceleration when the toothbrush is held in hand in the embodiment of the application and moves back and forth;
  • Fig. 3 is a schematic diagram of the acceleration data of the X-axis direction, the Y-axis direction, the Z-axis direction and the resultant acceleration of the left hand wearing a watch and holding the toothbrush to the right front and left rear back and forth;
  • Fig. 4 is a schematic diagram of the acceleration data of the X-axis direction, the Y-axis direction, the Z-axis direction and the combined acceleration when the right hand wears a watch and holds a toothbrush to move back and forth;
  • Figure 5 is a schematic diagram of filtered acceleration data when the toothbrush is held in the left hand, the wrist is tilted to the upper right, the dial screen of the wearable device is directed to the upper left, and the toothbrush is moved back and forth in the hand;
  • Fig. 6 is a data schematic diagram of the acceleration data of the X-axis direction, the Y-axis direction, the Z-axis direction and the combined acceleration of the acceleration data of the left hand wearing the watch and holding the toothbrush to the right front and the left rear back and forth after filtering;
  • Fig. 7 is a data schematic diagram of the filtered acceleration data in the X-axis direction, the Y-axis direction, the Z-axis direction and the combined acceleration when the right hand wears a watch and holds a toothbrush to move back and forth;
  • Fig. 8 is a schematic block diagram of a brushing motion recognition device in an embodiment of the present application.
  • Fig. 9 is one of the schematic block diagrams of the electronic device in the embodiment of the present application.
  • Fig. 10 is the second schematic block diagram of the electronic device in the embodiment of the present application.
  • FIG. 11 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application.
  • a toothbrushing motion recognition method including:
  • Step 102 acquiring acceleration information of the wearable device.
  • the acceleration information includes first acceleration data in the first direction, second acceleration data in the second direction and third acceleration data in the third direction, and the first direction, the second direction and the third direction are perpendicular to each other.
  • Step 104 acquiring the first number of times that the time between the first moment and the second moment is less than the preset time threshold within the first preset time.
  • the first moment is the moment when the extremum appears in the first target acceleration data
  • the second moment is the moment when the extremum appears in the second target acceleration data
  • the first target acceleration data and the second target acceleration data are the first Any two acceleration data among the acceleration data, the second acceleration data and the third acceleration data.
  • Step 106 determining the second number of extreme values of the first target acceleration data within the first preset time.
  • Step 108 determine the motion recognition result according to the ratio of the first count to the second count.
  • a toothbrushing movement recognition method in which the toothbrushing movement recognition can be realized by using the data detected by the acceleration sensor.
  • the technical solution of the present application realizes the toothbrushing motion recognition in the following manner.
  • the first direction may be the X-axis direction
  • the second direction may be the Y-axis direction
  • the third direction may be the Z-axis direction.
  • Acceleration data collected by the acceleration sensor in the X-axis direction, Y-axis direction, Z-axis direction and the combined acceleration at 25 Hz, the collection results are shown in Figure 2, the abscissa in Figure 2 is time in milliseconds, and the ordinate is acceleration , the unit is m/s 2 , where the resultant acceleration is shifted up by 5 units as a whole to avoid aliasing with acceleration data in other directions.
  • the first average value in the X-axis direction, the second average value in the Y-axis direction, and the third average value in the Z-axis direction are stable at around 9m/s 2 , 0m/s 2 and 5m/s 2 respectively , which means that the left hand is holding the toothbrush, the wrist is tilted to the upper right, the dial screen of the wearable device is facing the upper left, and the acceleration data in the X-axis direction, the Y-axis direction, the Z-axis direction and the combined acceleration when the toothbrush is held in the hand moves back and forth.
  • FIG. 3 a schematic diagram of the acceleration data of the X-axis direction, the Y-axis direction, the Z-axis direction and the resultant acceleration when the left hand wears a watch and holds a toothbrush to the right front and left rear back and forth.
  • FIG. 4 a schematic diagram of the acceleration data in the X-axis direction, the Y-axis direction, the Z-axis direction and the resultant acceleration when the right hand wears a watch and holds a toothbrush and moves back and forth.
  • the acceleration data detected by the wearable device When the user wears a wearable device and brushes his teeth, such as back and forth, back and forth, right front and left back and forth, left front and right back and forth, the acceleration data detected by the wearable device will be characterized. Specifically, the first The extreme values in the acceleration data in two or more directions among the first acceleration data in the direction, the second acceleration data in the second direction and the third acceleration data in the third direction appear relatively close to each other. Since brushing teeth is a process of constantly repeating the same action, the recognition of the movement can be realized by counting the proportion of extreme values satisfying the above conditions within the first preset time.
  • the technical solution of the present application is used to realize motion recognition while reducing the impact of motion recognition on the battery life of the electronic device. Improved battery life of electronic devices.
  • determining the recognition result of the motion according to the ratio of the first count to the second count includes: determining that motion is recognized when the ratio of the first count to the second count is greater than a preset threshold; If the ratio of the first count to the second count is less than or equal to the preset threshold, it is determined that no motion is recognized.
  • the ratio is greater than the preset threshold, it can be inferred that repeating the same action occupies most of the user's behavior within the first preset time.
  • the threshold value the user repeats the same number of times is relatively small, in order to avoid misjudgment, it is determined that the brushing movement has not been recognized. Reduces the chance of motion recognition errors by setting preset thresholds to distinguish between brushing and non-brushing motions.
  • it also includes: determining the second number of extreme values of the first target acceleration data within the first preset time, and the extreme value of the second target acceleration data within the first preset time The third number of times, in the case that the second number is less than or equal to the third number, the motion recognition result is determined according to the ratio of the first number to the second number.
  • the motion recognition result is determined according to the ratio of the first number to the third number.
  • the second time when the second time is less than or equal to the third time, it also includes: determining the ratio of the third time to the second time, and the ratio of the third time to the second time is greater than or equal to 2 In this case, the current acceleration data cannot meet the requirements of motion recognition.
  • a reminder message may be output, or no reminder message may be output.
  • the reminder information is used to indicate that the current acceleration data cannot meet the requirements of motion recognition.
  • the first acceleration data, the second acceleration data and the third acceleration data are updated.
  • the time length from the first moment when the extreme value appears in the first target acceleration data to the second moment when the extreme value appears in the second target acceleration data is less than the preset time
  • it also includes: obtaining the first average value of the first acceleration data, the second average value of the second acceleration data, and the third average value of the third acceleration data within the second preset time, wherein, The second preset time is less than the first preset time; when the first average value is within the first preset average value interval, the second average value is within the second preset average value interval, and the third average value is within the third preset average value interval
  • the time length from the first moment when the extreme value appears in the first target acceleration data to the second moment when the extreme value appears in the second target acceleration data within the first preset time is less than the preset The first number of the duration.
  • a pre-judgment is made on whether to perform motion recognition according to the detected acceleration data.
  • the determination method of the first preset average interval, the second preset average interval and the third preset average interval it is ensured to determine whether the user wearing the wearable device is in the position of holding the toothbrush.
  • the accuracy of gestures improves the recognition accuracy of brushing movements.
  • it also includes: obtaining the fourth average value and the first variance value of the first acceleration data within the third preset time, the fifth average value and the first variance value of the second acceleration data within the third preset time The second variance value, the sixth average value and the third-party variance value of the third acceleration data within the third preset time; the fluctuation value in the fourth average value is in the first preset fluctuation value interval and the first variance value is in Within the first preset variance range; the fluctuation value of the fifth average value is within the second preset fluctuation value interval, and the second variance value is within the second preset variance range; and/or the fluctuation value of the sixth average value is within In the case of the third preset fluctuation value range and the third-party difference within the third preset variance range, acquire the first average value of the first acceleration data and the second average value of the second acceleration data within the second preset time value, the third average value of the third acceleration data, wherein the third preset time is less than the second preset time.
  • the fluctuation value of the fourth average value is in the first preset fluctuation value interval and the first variance value is in the first preset variance range
  • the fluctuation value of the fifth average value is in the second preset fluctuation value interval and the second square If the difference is within the second preset variance range, the fluctuation value of the sixth average value is smaller than the third preset fluctuation value interval, and at least one of the third-party difference is within the third preset variance range is established, then it is determined that in this During the pre-judgment process, the user's actions are relatively stable.
  • the first preset fluctuation value interval, the first preset variance range, the second preset fluctuation value interval, the second preset variance range, the third preset fluctuation value interval and the third preset variance range are obtained through a large number of It is obtained by counting the acceleration data when the user is brushing teeth.
  • the fluctuation value of the fourth average value is in the first preset fluctuation value interval and the first variance value is in the first preset variance range
  • the fluctuation value of the fifth average value is in the second preset fluctuation value interval and the second
  • the variance value is in the second preset variance range
  • the fluctuation value of the sixth average value is in the third preset fluctuation value range
  • the third-party difference is in the third preset variance range
  • the first preset time before the time between the first moment and the second moment is less than the preset time threshold for the first time, it also includes: the first acceleration data, the second The acceleration data and the third acceleration data are filtered.
  • the toothbrush is held in the left hand, the wrist is tilted to the upper right, the dial screen of the wearable device is facing the upper left, and the acceleration data is filtered when the toothbrush is moved back and forth in the hand.
  • the left hand wears a watch and holds a toothbrush to the right front and left back to brush teeth back and forth in the X-axis direction, Y-axis direction, Z-axis direction and the acceleration data of the combined acceleration after filtering.
  • the reduction of the mutated data on judging motion recognition improves the accuracy of motion recognition.
  • low-pass filtering is performed on the first acceleration data, the second acceleration data and the third acceleration data.
  • the extreme values include peaks and/or valleys.
  • the situation that the time between the first moment and the second moment is less than the preset time threshold may be: the time between the moment when the peak value of the first acceleration data appears and the moment when the valley value of the second acceleration data appears less than the preset time threshold; or the time between the moment when the peak value of the first acceleration data appears and the moment when the peak value of the second acceleration data appears is less than the preset time threshold.
  • the value of the preset threshold is greater than or equal to 0.9.
  • the value of the preset threshold is reasonably selected so as to distinguish motion from non-motion and reduce the probability of motion recognition errors.
  • the time between the first moment and the second moment can be understood as the length of time from the first moment to the second moment, or the length of time from the second moment to the first moment.
  • the preset time threshold is less than or equal to 80 milliseconds, such as 70 milliseconds, 50 milliseconds, 30 milliseconds, and so on.
  • the method further includes: determining that motion is recognized, and outputting the duration of the motion.
  • the duration of the exercise is output to perform control during the exercise, wherein the control during the exercise includes but not limited to the control of the total duration of the exercise, and also includes outputting reminder information for the end of the exercise.
  • the method further includes: determining that a motion is recognized, and outputting the strength of the motion.
  • the strength of the movement is output so as to adjust the operating mode of the device according to the strength of the movement.
  • the motion recognition method provided in the embodiment of the present application may be executed by a motion recognition device.
  • the motion recognition device provided in the embodiment of the present application is described by taking the motion recognition device performing the motion recognition method as an example.
  • a motion recognition device 800 including: an acquisition module 802, configured to acquire acceleration information of a wearable device, wherein the acceleration information includes The first acceleration data, the second acceleration data in the second direction and the third acceleration data in the third direction, the first direction, the second direction and the third direction are perpendicular to each other; the statistical module 804 is used to obtain the first preset Within the time period, the time between the first moment and the second moment is less than the first time of the preset time threshold, wherein the first moment is the moment when the extreme value appears in the first target acceleration data, and the second moment is the second target acceleration data The moment when the extreme value appears in the acceleration data, wherein, the first target acceleration data and the second target acceleration data are any two acceleration data in the first acceleration data, the second acceleration data and the third acceleration data; the determination module 806 uses To determine the second number of extremum occurrences of the first target acceleration data within the first preset time; the identification module 808 is configured to determine the recognition result of the movement
  • a motion recognition device 800 which can realize motion recognition by using data detected by an acceleration sensor.
  • the acceleration data detected by the wearable device will be characterized, specifically , among the first acceleration data in the first direction, the second acceleration data in the second direction, and the third acceleration data in the third direction, the extreme values in the acceleration data in two or more directions appear relatively close to . Since brushing teeth is a process of constantly repeating the same action, the recognition of the movement can be realized by counting the proportion of extreme values satisfying the above conditions within the first preset time.
  • the technical solution of the present application is used to realize motion recognition while reducing the impact of motion recognition on the battery life of the electronic device. Improved battery life of electronic devices.
  • the recognition module 808 is specifically configured to: determine that motion is recognized when the ratio of the first count to the second count is greater than a preset threshold; or equal to the preset threshold, it is determined that no motion is recognized.
  • the ratio is greater than the preset threshold, it can be inferred that repeating the same action occupies most of the user's behavior within the first preset time, therefore, it is determined that the current user is in motion, and in In the case where the ratio is less than or equal to the preset threshold, the user repeats the same number of times is relatively small, in order to avoid misjudgment, it is determined that the teeth brushing movement has not been recognized. Reduces the chance of motion recognition errors by setting preset thresholds to distinguish between brushing and non-brushing motions.
  • it also includes: determining the second number of extreme values of the first target acceleration data within the first preset time, and the extreme value of the second target acceleration data within the first preset time The third number of times, in the case that the second number is less than or equal to the third number, the motion recognition result is determined according to the ratio of the first number to the second number.
  • the motion recognition result is determined according to the ratio of the first number to the third number.
  • the second time when the second time is less than or equal to the third time, it also includes: determining the ratio of the third time to the second time, and the ratio of the third time to the second time is greater than or equal to 2 In this case, the current acceleration data cannot meet the requirements of motion recognition.
  • a reminder message may be output, or no reminder message may be output.
  • the reminder information is used to indicate that the current acceleration data cannot meet the requirements of motion recognition.
  • the first acceleration data, the second acceleration data and the third acceleration data are updated.
  • the determination module 806 is also used to: obtain the first average value of the first acceleration data, the second average value of the second acceleration data, and the third average value of the third acceleration data within the second preset time value, wherein the second preset time is less than the first preset time; when the first average value is within the first preset average value interval, the second average value is within the second preset average value interval, and the third average value In the case of being within the third preset average value interval, the time from the first moment when the extreme value appears in the first target acceleration data to the second moment when the extreme value appears in the second target acceleration data within the first preset time is obtained The first number of times the length is less than the preset time length.
  • a pre-judgment is made on whether to perform motion recognition according to the detected acceleration data.
  • the determination method of the first preset average interval, the second preset average interval and the third preset average interval it is ensured to determine whether the user wearing the wearable device is in the position of holding the toothbrush.
  • the accuracy of gestures improves the recognition accuracy of brushing movements.
  • the determination module 806 is further configured to: obtain the fourth average value and the first variance value of the first acceleration data within the third preset time, the first variance value of the second acceleration data within the third preset time The fifth average value and the second variance value, the sixth average value and the third-party difference value of the third acceleration data within the third preset time; the fluctuation value of the fourth average value is within the first preset fluctuation value interval and the first One variance value is within the first preset variance range; the fluctuation value of the fifth average value is within the second preset fluctuation value range, and the second variance value is within the second preset variance range; and/or the sixth average When the fluctuation value of the value is within the third preset fluctuation value interval and the third-party difference is within the third preset variance range, the first average value and the second acceleration data of the first acceleration data within the second preset time period are obtained. The second average value of the data and the third average value of the third acceleration data, wherein the third preset time is shorter than the second preset time.
  • the fluctuation value of the fourth average value is in the first preset fluctuation value interval and the first variance value is in the first preset variance range
  • the fluctuation value of the fifth average value is in the second preset fluctuation value interval and the second square If the difference is within the second preset variance range, the fluctuation value of the sixth average value is smaller than the third preset fluctuation value interval, and at least one of the third-party difference is within the third preset variance range is established, then it is determined that in this During the pre-judgment process, the user's actions are relatively stable.
  • the first preset fluctuation value interval, the first preset variance range, the second preset fluctuation value interval, the second preset variance range, the third preset fluctuation value interval and the third preset variance range are obtained through a large number of It is obtained by counting the acceleration data when the user is brushing teeth.
  • the fluctuation value of the fourth average value is in the first preset fluctuation value interval and the first variance value is in the first preset variance range
  • the fluctuation value of the fifth average value is in the second preset fluctuation value interval and the second
  • the variance value is in the second preset variance range
  • the fluctuation value of the sixth average value is in the third preset fluctuation value range
  • the third-party difference is in the third preset variance range
  • the determining module 806 is further configured to: filter the first acceleration data, the second acceleration data and the third acceleration data.
  • low-pass filtering is performed on the first acceleration data, the second acceleration data and the third acceleration data.
  • the extreme values include peaks and/or valleys.
  • the value of the preset threshold is greater than or equal to 0.9.
  • the value of the preset threshold is reasonably selected so as to distinguish motion from non-motion and reduce the probability of motion recognition errors.
  • the time between the first moment and the second moment can be understood as the length of time from the first moment to the second moment, or the length of time from the second moment to the first moment.
  • the preset time threshold is less than or equal to 80 milliseconds, such as 70 milliseconds, 50 milliseconds, 30 milliseconds, and so on.
  • the recognition module 808 is further configured to: determine that motion is recognized, and output the duration of the motion.
  • the duration of the exercise is output to perform control during the exercise, wherein the control during the exercise includes but not limited to the control of the total duration of the exercise, and also includes outputting reminder information for the end of the exercise.
  • the recognition module 808 is further configured to: determine that a movement is recognized, and output the strength of the movement.
  • the strength of the movement is output so as to adjust the operating mode of the device according to the strength of the movement.
  • the motion recognition apparatus 800 in the embodiment of the present application may be an electronic device, or may be a component in the electronic device, such as an integrated circuit or a chip.
  • the electronic device may be a terminal, or other devices other than the terminal.
  • the electronic device can be a mobile phone, a tablet computer, a notebook computer, a handheld computer, a vehicle electronic device, a mobile Internet device (Mobile Internet Device, MID), an augmented reality (augmented reality, AR)/virtual reality (virtual reality, VR) ) equipment, robots, wearable devices, ultra-mobile personal computer (ultra-mobile personal computer, UMPC), netbook or personal digital assistant (personal digital assistant, PDA), etc.
  • the motion recognition device 800 in the embodiment of the present application may be a device with an operating system.
  • the operating system may be an Android (Android) operating system, an ios operating system, or other possible operating systems, which are not specifically limited in this embodiment of the present application.
  • the motion recognition device 800 provided by the embodiment of the present application can realize various processes realized by the method embodiments in FIG. 1 to FIG. 7 , and details are not repeated here to avoid repetition.
  • an electronic device 900 is proposed, including the above-mentioned motion recognition device 800 .
  • the proposed electronic device 900 has the above-mentioned movement recognition device 800 and can achieve the same technical effect, so to avoid repetition, details are not repeated here.
  • the embodiment of the present application also provides an electronic device 1000, including a processor 1002 and a memory 1004, and the memory 1004 stores programs or instructions that can run on the processor 1002, When the program or instruction is executed by the processor 1002, each step of the above-mentioned embodiment of the motion recognition method can be realized, and the same technical effect can be achieved. To avoid repetition, details are not repeated here.
  • the electronic device 1000 in the embodiment of the present application includes the above-mentioned mobile electronic device and non-mobile electronic device.
  • FIG. 11 is a schematic diagram of a hardware structure of an electronic device implementing an embodiment of the present application.
  • the electronic device 1100 includes but is not limited to: a radio frequency unit 1101, a network module 1102, an audio output unit 1103, an input unit 1104, a sensor 1105, a display unit 1106, a user input unit 1107, an interface unit 1108, and a memory 1109 , and the processor 1110 and other components.
  • the electronic device 1100 can also include a power supply (such as a battery) for supplying power to various components, and the power supply can be logically connected to the processor 1110 through the power management system, so that the management of charging, discharging, and function can be realized through the power management system. Consumption management and other functions.
  • a power supply such as a battery
  • the structure of the electronic device shown in FIG. 11 does not constitute a limitation to the electronic device.
  • the electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange different components, and details will not be repeated here. .
  • the processor 1110 is configured to acquire acceleration information of the wearable device, where the acceleration information includes first acceleration data in a first direction, second acceleration data in a second direction, and third acceleration data in a third direction, the first The first direction, the second direction, and the third direction are perpendicular to each other; obtain the first number of times that the time between the first moment and the second moment is less than the preset time threshold within the first preset time, where the first moment is The moment when the extreme value appears in the first target acceleration data, and the second moment is the moment when the extreme value appears in the second target acceleration data, wherein the first target acceleration data and the second target acceleration data are the first acceleration data, the second acceleration data Any two acceleration data in the data and the third acceleration data; determine the second number of times that the extreme value occurs in the first target acceleration data within the first preset time; Recognition results.
  • Processor 1110 configured to determine that motion is recognized when the ratio of the first count to the second count is greater than a preset threshold; , make sure no motion is detected.
  • the processor 1110 is configured to obtain within the first preset time, the time length from the first moment when the extreme value appears in the first target acceleration data to the second moment when the extreme value appears in the second target acceleration data is less than the preset time length Before the first count, it also includes: obtaining the first average value of the first acceleration data, the second average value of the second acceleration data, and the third average value of the third acceleration data within the second preset time, wherein the second The preset time is less than the first preset time; when the first average value is within the first preset average value interval, the second average value is within the second preset average value interval, and the third average value is within the third preset average value In the case of the value interval, within the first preset time period, the time length from the first moment when the extreme value appears in the first target acceleration data to the second moment when the extreme value appears in the second target acceleration data is less than the preset time length the first number of .
  • Processor 1110 configured to obtain a fourth average value and a first variance value of the first acceleration data within a third preset time period, and a fifth average value and a second variance value of the second acceleration data within a third preset time period , the sixth average value and the third-party difference of the third acceleration data within the third preset time; the fluctuation value of the fourth average value is within the first preset fluctuation value interval and the first variance value is within the first preset Within the variance range; the fluctuation value of the fifth average value is within the second preset fluctuation value interval, the second variance value is within the second preset variance range; and/or the fluctuation value of the sixth average value is within the third preset In the fluctuation value interval, when the third-party difference is within the third preset variance range, the first average value of the first acceleration data, the second average value of the second acceleration data, the third average value of the second acceleration data, and the third The third average value of the acceleration data, wherein the third preset time is shorter than the second preset time.
  • the input unit 1104 may include a graphics processor (Graphics Processing Unit, GPU) 11041 and a microphone 11042, and the graphics processor 11041 is used for the image capture device (such as the image data of the still picture or video obtained by the camera) for processing.
  • the display unit 1106 may include a display panel 11061, and the display panel 11061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like.
  • the user input unit 1107 includes at least one of a touch panel 11071 and other input devices 11072 .
  • Touch panel 11071 also called touch screen.
  • the touch panel 11071 may include two parts, a touch detection device and a touch controller.
  • Other input devices 11072 may include, but are not limited to, physical keyboards, function keys (such as volume control keys, switch keys, etc.), trackballs, mice, and joysticks, which will not be repeated here.
  • the memory 1109 can be used to store software programs as well as various data.
  • the memory 1109 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required by at least one function (such as a sound playing function, image playback function, etc.), etc.
  • memory 1109 may include volatile memory or nonvolatile memory, or, memory 1109 may include both volatile and nonvolatile memory.
  • the non-volatile memory can be read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electrically programmable Erase Programmable Read-Only Memory (Electrically EPROM, EEPROM) or Flash.
  • ROM Read-Only Memory
  • PROM programmable read-only memory
  • Erasable PROM Erasable PROM
  • EPROM electrically programmable Erase Programmable Read-Only Memory
  • Flash Flash.
  • Volatile memory can be random access memory (Random Access Memory, RAM), static random access memory (Static RAM, SRAM), dynamic random access memory (Dynamic RAM, DRAM), synchronous dynamic random access memory (Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDRSDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous connection dynamic random access memory (Synch link DRAM , SLDRAM) and Direct Memory Bus Random Access Memory (Direct Rambus RAM, DRRAM).
  • RAM Random Access Memory
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • DRAM synchronous dynamic random access memory
  • SDRAM double data rate synchronous dynamic random access memory
  • Double Data Rate SDRAM Double Data Rate SDRAM
  • DDRSDRAM double data rate synchronous dynamic random access memory
  • Enhanced SDRAM, ESDRAM enhanced synchronous dynamic random access memory
  • Synch link DRAM , SLDRAM
  • Direct Memory Bus Random Access Memory Direct Rambus
  • the processor 1110 may include one or more processing units; optionally, the processor 1110 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to the operating system, user interface, and application programs, etc., Modem processors mainly process wireless communication signals, such as baseband processors. It can be understood that the foregoing modem processor may not be integrated into the processor 1110 .
  • An embodiment of the present invention also provides an electronic device configured to execute the processes of the above embodiment of the motion recognition method, and can achieve the same technical effect. To avoid repetition, details are not repeated here.
  • the embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, each of the above embodiments of the toothbrushing motion recognition method is realized. process, and can achieve the same technical effect, in order to avoid repetition, it will not be repeated here.
  • the processor is the processor in the electronic device in the foregoing embodiments.
  • the readable storage medium includes a computer-readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk, and the like.
  • the embodiment of the present application further provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to realize the various processes of the above-mentioned teeth brushing motion recognition method embodiment, and can achieve the same To avoid repetition, the technical effects will not be repeated here.
  • chips mentioned in the embodiments of the present application may also be called system-on-chip, system-on-chip, system-on-a-chip, or system-on-a-chip.
  • the embodiment of the present application further provides a computer program product, the program product is stored in a storage medium, and the program product is executed by at least one processor to realize the various processes in the above embodiment of the motion recognition method, and can achieve the same To avoid repetition, the technical effects will not be repeated here.
  • the term “comprising”, “comprising” or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or device. Without more limitations, an element defined by the phrase “comprising a” does not exclude the presence of additional same elements in the process, method, article or apparatus that includes the element.
  • the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved. Functions are performed, for example, the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

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Abstract

L'invention concerne un procédé d'identification de mouvement, un appareil, un dispositif électronique et un support de stockage lisible, qui appartiennent au domaine du traitement de données. Le procédé consiste : à acquérir des informations d'accélération d'un dispositif portable ; à acquérir, pendant une première durée prédéfinie, un premier nombre de fois où une durée entre un premier moment et un second moment est inférieure à un seuil de durée prédéfini, le premier moment étant un moment où une valeur extrême apparaît dans des premières données d'accélération cibles, et le second moment étant un moment où une valeur extrême apparaît dans des deuxièmes données d'accélération cibles, les premières données d'accélération cibles et les deuxièmes données d'accélération cibles étant deux éléments quelconques de données d'accélération dans des premières données d'accélération, des deuxièmes données d'accélération et des troisièmes données d'accélération ; à déterminer un second nombre de fois où une valeur extrême apparaît dans les premières données d'accélération cible pendant la première durée prédéfinie ; et à déterminer un résultat d'identification de mouvement en fonction d'un rapport du premier nombre de fois au second nombre de fois.
PCT/CN2023/071548 2022-01-17 2023-01-10 Procédé d'identification de mouvement, appareil, dispositif électronique et support de stockage lisible WO2023134663A1 (fr)

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