A method and device for analyzing and reminding a twisting gesture

Abstract

The present invention discloses a method and device for analyzing and reminding a twisting gesture, the method comprising: collecting a set of finger motion trajectory information of a user's operating hand; the set of finger motion trajectory information includes index finger motion trajectory information, middle finger motion trajectory information and thumb motion trajectory information; preprocessing the set of finger motion trajectory information to obtain a preprocessed finger motion trajectory set; twisting and analyzing the preprocessed finger motion trajectory set to obtain twisting frequency information and twisting time information; distinguishing and processing the twisting frequency information and twisting time information, and generating and displaying early warning reminder information. The present invention models the trajectory of the twisting gesture of an operator in a specific scenario, uses a quantitative analysis model to extract the gesture movement frequency and duration, and distinguishes the frequency and time, thereby realizing continuous reminders of whether a specific action meets the requirements.

Description

Technical Field

[0001] The present invention relates to the field of gesture recognition and signal processing, and in particular to a method and device for analyzing and reminding a twisting gesture. Background Art

[0002] In the field of medical care, for specific nursing operations, relatively strict requirements are placed on parameters such as the operator's gesture movement frequency. For example, for assisted suction operations, the operator is required to wear relatively sterile gloves with one hand (usually the right hand), pinch the suction tube and twist it, and pull it up with the other hand. The suction should not exceed 15 seconds at a time, and the frequency of the twisting gesture should not exceed the set value. Therefore, when performing assisted suction operations, the operator's twisting gesture needs to be monitored. There are currently no corresponding products and methods for monitoring twisting gestures.

[0003] How to analyze and remind the operator's twisting gesture in a specific scenario to meet the requirements of specific operations is a problem that needs to be solved at present. Summary of the invention

[0004] The present invention mainly solves the problem of analyzing and reminding an operator of a twisting gesture in a specific scenario to meet the requirements of a specific operation. The present invention discloses a twisting gesture analysis and reminder method and device.

[0005] In a first aspect of the embodiments of the present application, a method for analyzing and reminding a twisting gesture is disclosed, comprising:

[0006] S1, collecting and obtaining a set of finger motion trajectory information of a user's operating hand; the set of finger motion trajectory information includes index finger motion trajectory information, middle finger motion trajectory information and thumb motion trajectory information;

[0007] S2, preprocessing the finger motion trajectory information set to obtain a preprocessed finger motion trajectory set;

[0008] S3, performing twisting analysis processing on the pre-processed finger motion trajectory set to obtain twisting frequency information and twisting time information;

[0009] S4, distinguishing and processing the twisting frequency information and the twisting time information, and generating and displaying early warning reminder information.

[0010] The acquisition obtains a set of finger motion trajectory information of the user's operating hand, including:

[0011] S11, using accelerometer sensors disposed on the index finger, middle finger and thumb of the user's operating hand, respectively acquiring position coordinates of the user's index finger, middle finger and thumb at each moment;

[0012] S12, arranging the position coordinates of each finger at all times in chronological order to obtain motion trajectory information corresponding to each finger; the motion trajectory information includes trajectory points and corresponding collection time;

[0013] S13, performing fusion processing on all the motion trajectory information to obtain a finger motion trajectory information set.

[0014] The preprocessing of the finger motion trajectory information set to obtain a preprocessed finger motion trajectory set includes:

[0015] S21, performing category discrimination processing on the finger motion trajectory information set to obtain a first motion trajectory information set;

[0016] S22, performing abnormality identification processing on the first motion trajectory information set to obtain a second motion trajectory information set;

[0017] S23, performing trajectory consistency determination processing on the second motion trajectory information set to obtain a preprocessed finger motion trajectory set.

[0018] The performing trajectory consistency determination processing on the second motion trajectory information set to obtain a preprocessed finger motion trajectory set includes:

[0019] S231, performing boundary calculation processing on the second motion trajectory information set to obtain an index finger motion boundary, a thumb motion boundary, and a middle finger motion boundary;

[0020] S232, using the motion boundary of each finger, performing boundary discrimination processing on each track point of the motion trajectory information corresponding to the finger in the second motion trajectory information set to obtain a boundary discrimination result; deleting the track point whose boundary discrimination result is not within the motion boundary from the corresponding motion trajectory information to obtain a pre-processed motion trajectory;

[0021] S233, performing fusion processing on all preprocessed motion trajectories to obtain a preprocessed finger motion trajectory set.

[0022] The boundary discrimination process is expressed as follows:

[0023] p=|x 0 -x 11 | / |x 0 +x 11 |+|1-y 0 / y 11 |+|exp(z 0 / z 11 )-γ1| / |z 0 +z 11 |+

[0024] |y 0 -y 12 | / |y 0 +y 12 |+|1-z 0 / z 12 |+|exp(x 0 / x 12 )-γ2| / |x 0 +x 12 |,

[0025] p≤p 0 ,

[0026] Among them, [x 0 ,y 0 ,z 0 ] is the position coordinate of the trajectory point to be identified, [x 11 ,x 12 ]、[y 11 ,y 12 ]、[z 11 ,z 12 ] are the motion boundaries of the x-axis, y-axis and z-axis respectively, 11 ,y 11 、z 11 are the lower bounds of the motion boundaries of the x-axis, y-axis, and z-axis, respectively. 12 ,y 12 、z 12 are the upper limits of the motion boundaries of the x-axis, y-axis, and z-axis, respectively; γ1 and γ2 are the preset first calculation constant and second calculation constant, respectively; p is the boundary calculation value; and p 0 is the preset boundary discrimination threshold; when p≤p 0 When p>p 0 When , the boundary judgment result is that the trajectory point to be judged is not within the motion boundary.

[0027] The twisting analysis processing is performed on the pre-processed finger motion trajectory set to obtain twisting frequency information and twisting time information, including:

[0028] S31, performing segmented extraction processing on the pre-processed finger motion trajectory set to obtain a finger motion segmented trajectory set; the finger motion segmented trajectory set includes a segmented trajectory information set of each finger; the segmented trajectory information set includes a plurality of sub-trajectories;

[0029] S32, performing frequency extraction processing on the finger motion segmented trajectory set to obtain twisting frequency information of each finger;

[0030] S33, performing motion time accumulation calculation processing on the finger motion segment trajectory set to obtain twisting time information.

[0031] The step of distinguishing and processing the twisting frequency information and the twisting time information, and generating and displaying early warning reminder information, includes:

[0032] S41, determining whether the twisting frequency information is greater than a set frequency threshold, and obtaining a frequency determination result; if the frequency determination result is greater than, generating a frequency warning reminder message;

[0033] S42, determining whether there is a twisting time value greater than a set time threshold in the twisting time information, and obtaining a time determination result; if the time determination result is yes, generating time warning reminder information; the warning reminder information includes frequency warning reminder information and time warning reminder information;

[0034] S43, displaying the generated warning reminder information.

[0035] In a second aspect of the embodiments of the present application, a twisting gesture analysis and reminder device is disclosed, the device comprising:

[0036] A memory storing executable program code;

[0037] a processor coupled to the memory;

[0038] The processor calls the executable program code stored in the memory to execute the twisting gesture analysis and reminder method.

[0039] In a third aspect of the embodiments of the present application, a computer-storable medium is disclosed, wherein the computer-storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the analysis and reminder method of the twisting gesture.

[0040] In a fourth aspect of the embodiments of the present application, an information data processing terminal is disclosed, and the information data processing terminal is used to implement the analysis and reminder method of the twisting gesture.

[0041] The beneficial effects of the present invention are:

[0042] The present invention models the trajectory of the operator's twisting gesture in a specific scenario, uses a quantitative analysis model to extract the gesture movement frequency and duration, and distinguishes the frequency and time, thereby achieving continuous reminders on whether specific actions meet the requirements.

[0043] In the process of trajectory modeling for the twisting gesture, the present invention establishes a boundary discrimination processing model, and effectively eliminates the collected clutter points by performing trajectory consistency discrimination on the collected data, thereby ensuring the accuracy of trajectory modeling and the validity of data, and ensuring the real-time and accuracy of gesture reminders. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart for implementing the method of the present invention. DETAILED DESCRIPTION

[0045] In order to better understand the content of the present invention, an embodiment is given here.

[0046] Figure 1 It is a flow chart for implementing the method of the present invention.

[0047] In order to analyze and remind an operator of a twisting gesture in a specific scenario to meet the requirements of a specific operation, the present invention discloses a method and device for analyzing and reminding a twisting gesture.

[0048] In a first aspect of the embodiments of the present application, a method for analyzing and reminding a twisting gesture is disclosed, comprising:

[0049] S1, collecting and obtaining a set of finger motion trajectory information of a user's operating hand; the set of finger motion trajectory information includes index finger motion trajectory information, middle finger motion trajectory information and thumb motion trajectory information;

[0050] S2, preprocessing the finger motion trajectory information set to obtain a preprocessed finger motion trajectory set;

[0051] S3, performing twisting analysis processing on the pre-processed finger motion trajectory set to obtain twisting frequency information and twisting time information;

[0052] S4, distinguishing and processing the twisting frequency information and the twisting time information, and generating and displaying early warning reminder information;

[0053] The acquisition obtains a set of finger motion trajectory information of the user's operating hand, including:

[0054] S11, using accelerometer sensors disposed on the index finger, middle finger and thumb of the user's operating hand, respectively acquiring position coordinates of the user's index finger, middle finger and thumb at each moment;

[0055] S12, arranging the position coordinates of each finger at all times in chronological order to obtain motion trajectory information corresponding to each finger; the motion trajectory information includes trajectory points and corresponding collection time;

[0056] S13, performing fusion processing on all the motion trajectory information to obtain a finger motion trajectory information set.

[0057] The position coordinates are three-dimensional position coordinates [x, y, z] in the geodetic coordinate system;

[0058] The preprocessing of the finger motion trajectory information set to obtain a preprocessed finger motion trajectory set includes:

[0059] S21, performing category discrimination processing on the finger motion trajectory information set to obtain a first motion trajectory information set;

[0060] S22, performing abnormality identification processing on the first motion trajectory information set to obtain a second motion trajectory information set;

[0061] S23, performing trajectory consistency determination processing on the second motion trajectory information set to obtain a preprocessed finger motion trajectory set;

[0062] The performing trajectory consistency determination processing on the second motion trajectory information set to obtain a preprocessed finger motion trajectory set includes:

[0063] S231, performing boundary calculation processing on the second motion trajectory information set to obtain an index finger motion boundary, a thumb motion boundary, and a middle finger motion boundary;

[0064] S232, using the motion boundary of each finger, performing boundary discrimination processing on each track point of the motion trajectory information corresponding to the finger in the second motion trajectory information set to obtain a boundary discrimination result; deleting the track point whose boundary discrimination result is not within the motion boundary from the corresponding motion trajectory information to obtain a pre-processed motion trajectory;

[0065] S233, performing fusion processing on all pre-processed motion trajectories to obtain a pre-processed finger motion trajectory set;

[0066] The boundary discrimination process is expressed as follows:

[0067]

[0068] p≤p 0 ,

[0069] Among them, [x 0 ,y 0 ,z 0 ] is the position coordinate of the trajectory point to be identified, [x 11 ,x 12 ]、[y 11 ,y 12 ]、[z 11 ,z12 ] are the motion boundaries of the x-axis, y-axis and z-axis respectively, 11 ,y 11 、z 11 are the lower bounds of the motion boundaries of the x-axis, y-axis, and z-axis, respectively. 12 ,y 12 、z 12 are the upper limits of the motion boundaries of the x-axis, y-axis, and z-axis, respectively; γ1 and γ2 are the preset first calculation constant and second calculation constant, respectively; p is the boundary calculation value; and p 0 is the preset boundary discrimination threshold, which can be 1.5; when p≤p 0 When p>p 0 When , the boundary judgment result is that the trajectory point to be judged is not within the motion boundary;

[0070] The twisting analysis processing is performed on the pre-processed finger motion trajectory set to obtain twisting frequency information and twisting time information, including:

[0071] S31, performing segmented extraction processing on the pre-processed finger motion trajectory set to obtain a finger motion segmented trajectory set; the finger motion segmented trajectory set includes a segmented trajectory information set of each finger; the segmented trajectory information set includes a plurality of sub-trajectories;

[0072] S32, performing frequency extraction processing on the finger motion segmented trajectory set to obtain twisting frequency information of each finger;

[0073] S33, performing motion time accumulation calculation processing on the finger motion segmented trajectory set to obtain twisting time information;

[0074] The step of distinguishing and processing the twisting frequency information and the twisting time information, and generating and displaying early warning reminder information, includes:

[0075] S41, determining whether the twisting frequency information is greater than a set frequency threshold, and obtaining a frequency determination result; if the frequency determination result is greater than, generating a frequency warning reminder message;

[0076] S42, determining whether there is a twisting time value greater than a set time threshold in the twisting time information, and obtaining a time determination result; if the time determination result is yes, generating time warning reminder information; the warning reminder information includes frequency warning reminder information and time warning reminder information;

[0077] S43, displaying the generated warning reminder information.

[0078] The pre-processed finger motion trajectory set is subjected to segmented extraction processing to obtain a finger motion segmented trajectory set, including:

[0079] S311, performing slope calculation on all trajectory points in each finger motion trajectory in the preprocessed finger motion trajectory set to obtain a slope vector sequence of the finger motion trajectory;

[0080] The slope calculation is expressed as (dx1, dy1, dz1) = (x2-x1, y2-y1, z2-z1), (dx1, dy1, dz1) is an element in the slope vector sequence corresponding to the first trajectory point in the finger motion trajectory, (x1, y1, z1) is the first trajectory point in the finger motion trajectory, and (x2, y2, z2) is the second trajectory point in the finger motion trajectory;

[0081] S312, initializing the slope subsequence; adding the first element in the slope vector sequence to the slope subsequence;

[0082] S313, performing positive and negative discrimination on the front and rear adjacent elements in the slope vector sequence in turn to obtain a sign discrimination result; dividing the elements with the same sign discrimination result into the same slope subsequence; using the next element of the front and rear adjacent elements with different sign discrimination results to establish a new slope subsequence; the same sign discrimination result means that the front and rear adjacent elements are all positive or all negative; the different sign discrimination result means that the front and rear adjacent elements are not both positive or negative;

[0083] S314, constructing a sub-trajectory corresponding to the slope sub-sequence according to the trajectory points of the finger motion trajectory corresponding to the elements in each slope sub-sequence contained in the slope vector sequence; the sub-trajectory includes the trajectory points corresponding to all elements belonging to the same slope sub-sequence; and constructing a sub-trajectory set of the finger motion trajectory using the sub-trajectories corresponding to all the slope sub-sequences;

[0084] S315, executing S312 to S314 for all finger motion trajectories to obtain a sub-trajectory set of the finger motion trajectories; using the sub-trajectory sets of all finger motion trajectories, construct a finger motion segmented trajectory set;

[0085] The step of performing frequency extraction processing on the finger motion segmented trajectory set to obtain twisting frequency information of each finger includes:

[0086] S321, for each sub-trajectory set of the finger motion trajectory in the finger motion segmented trajectory set, statistically obtain the start time and end time of each sub-trajectory, and calculate the difference between the end time and the start time of the sub-trajectory as the duration of the sub-trajectory;

[0087] S322, for each finger motion trajectory, calculate the average value of the duration of all its sub-trajectories, and determine twice the average value as the motion period of the finger;

[0088] S323, determining the inverse of the movement cycle of each finger as the twisting frequency information of the finger;

[0089] The step of performing motion time accumulation calculation processing on the finger motion segmented trajectory set to obtain twisting time information includes:

[0090] S331, sorting each sub-trajectory contained in the finger motion trajectory of the finger motion segmented trajectory set according to the start time to obtain a sub-trajectory sequence;

[0091] S332, initialization judgment sequence number is 1;

[0092] S333, initializing the twisting time value to the duration of the first sub-track in the sub-track sequence;

[0093] S334, calculating the absolute value of the difference between the end time of the sub-trajectory with the sequence number being the judgment sequence number in the sub-trajectory sequence and the start time of the sub-trajectory with the sequence number being the sequence number after the judgment sequence number in the sub-trajectory sequence;

[0094] S335, determining whether the absolute value is greater than a set threshold, and obtaining a first determination result;

[0095] If the first judgment result is greater than, the twisting time value is added to the twisting time information, the judgment sequence number is increased by 1, and the judgment sequence number is compared to see whether it is equal to the total number of sub-trajectories contained in the sub-trajectory sequence. If not, the twisting time value is initialized to the duration of the sub-trajectory with the judgment sequence number in the sub-trajectory sequence, and S334 is executed. If it is equal, the twisting time information of the finger motion trajectory is obtained;

[0096] If the first judgment result is less than, the twisting time value is increased by the duration of the sub-track with the judgment number in the sub-track sequence, the judgment number is increased by 1, and the judgment number is compared to see whether it is equal to the total number of sub-tracks included in the sub-track sequence. If not, execute S334. If equal, obtain the twisting time information of the finger motion track.

[0097] S336, for each finger motion track, execute S332 to S335 to obtain the twisting time information of all finger motion tracks. The twisting time information includes the twisting time value.

[0098] The step of performing motion time accumulation calculation processing on the finger motion segmented trajectory set to obtain twisting time information includes:

[0099] For each sub-trajectory contained in each finger motion trajectory in the finger motion segmented trajectory set, calculate the time interval between each sub-trajectory;

[0100] Find all time intervals greater than a set threshold, determine the sum of the durations of all sub-trajectories between every two adjacent time intervals as the twisting time value; add all the twisting time values ​​to the twisting time information;

[0101] Determine the sum of the durations of all sub-trajectories between the first sub-trajectory and the first time interval as the twisting time value, and add the twisting time value to the twisting time information;

[0102] The performing category discrimination processing on the finger motion trajectory information set to obtain a first motion trajectory information set includes:

[0103] Determining a data attribute range of the finger motion trajectory information set;

[0104] For each observation data in the finger motion trajectory information set, determine whether the data attribute of the observation data is within the data attribute range to obtain a type determination result; delete the observation data for which the type determination result is negative from the finger motion trajectory information set; after completing the determination for all observation data, obtain the first motion trajectory information set

[0105] The abnormal discrimination process includes filling missing values, smoothing noise data, smoothing or deleting outliers; the smoothed noise data is firstly discriminated to obtain noise data, and then the noise data is smoothed according to the data before and after the noise data; the noise data is a value whose value is less than the detection sensitivity of the sensor of the observed data, or greater than the measurement upper limit of the sensor of the observed data. The outlier point discrimination can adopt the Kalman filtering method. The filling value of the missing value can be determined by averaging the measured values ​​within a certain sampling interval before and after the missing value.

[0106] In a second aspect of the embodiments of the present application, a twisting gesture analysis and reminder device is disclosed, the device comprising:

[0107] A memory storing executable program code;

[0108] a processor coupled to the memory;

[0109] The processor calls the executable program code stored in the memory to execute the twisting gesture analysis and reminder method.

[0110] In a third aspect of the embodiments of the present application, a computer-storable medium is disclosed, wherein the computer-storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the analysis and reminder method of the twisting gesture.

[0111] In a fourth aspect of the embodiments of the present application, an information data processing terminal is disclosed, and the information data processing terminal is used to implement the analysis and reminder method of the twisting gesture.

[0112] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for analyzing and reminding a twisting gesture, characterized in that: include: S1, collecting and obtaining a set of finger motion trajectory information of a user's operating hand; the set of finger motion trajectory information includes index finger motion trajectory information, middle finger motion trajectory information and thumb motion trajectory information; S2, preprocessing the finger motion trajectory information set to obtain a preprocessed finger motion trajectory set; S3, performing twisting analysis processing on the pre-processed finger motion trajectory set to obtain twisting frequency information and twisting time information; S4, distinguishing and processing the twisting frequency information and the twisting time information, and generating and displaying early warning reminder information; The preprocessing of the finger motion trajectory information set to obtain a preprocessed finger motion trajectory set includes: S21, performing category discrimination processing on the finger motion trajectory information set to obtain a first motion trajectory information set; S22, performing abnormality identification processing on the first motion trajectory information set to obtain a second motion trajectory information set; S23, performing trajectory consistency determination processing on the second motion trajectory information set to obtain a preprocessed finger motion trajectory set; The performing trajectory consistency determination processing on the second motion trajectory information set to obtain a preprocessed finger motion trajectory set includes: S231, performing boundary calculation processing on the second motion trajectory information set to obtain an index finger motion boundary, a thumb motion boundary, and a middle finger motion boundary; S232, using the motion boundary of each finger, performing boundary discrimination processing on each track point of the motion trajectory information corresponding to the finger in the second motion trajectory information set to obtain a boundary discrimination result; deleting the track point whose boundary discrimination result is not within the motion boundary from the corresponding motion trajectory information to obtain a pre-processed motion trajectory; S233, performing fusion processing on all preprocessed motion trajectories to obtain a preprocessed finger motion trajectory set.

2. The twisting gesture analysis and reminder method according to claim 1, characterized in that: The acquisition obtains a set of finger motion trajectory information of the user's operating hand, including: S11, using accelerometer sensors disposed on the index finger, middle finger and thumb of the user's operating hand, respectively acquiring position coordinates of the user's index finger, middle finger and thumb at each moment; S12, arranging the position coordinates of each finger at all times in chronological order to obtain motion trajectory information corresponding to each finger; the motion trajectory information includes trajectory points and corresponding collection time; S13, performing fusion processing on all the motion trajectory information to obtain a finger motion trajectory information set.

3. The analysis and reminder method of the twisting gesture according to claim 1, characterized in that: The boundary discrimination process is expressed as follows: p=|x0-x 11 | / |x0+x 11 |+|1-y0 / y 11 |+|exp(z0 / z 11 )-γ1| / |z0+z 11 |+|y0-y 12 | / |y0+y 12 |+|1-z0 / z 12 |+|exp(x0 / x 12 )-γ2| / |x0+x 12 |, p≤p0, Among them, [x0, y0, z0] is the position coordinate of the trajectory point to be judged, [x 11 ,x 12 ]、[y 11 ,y 12 ]、[z 11 ,z 12 ] are the motion boundaries of the x-axis, y-axis and z-axis respectively, 11 ,y 11 、z 11 are the lower bounds of the motion boundaries of the x-axis, y-axis, and z-axis, respectively. 12 ,y 12 、z 12 are the upper limits of the motion boundaries of the x-axis, y-axis and z-axis respectively, γ1 and γ2 are the preset first calculation constants and the second calculation constants respectively, p is the boundary calculation value, and p0 is the preset boundary judgment threshold; when p≤p0, the boundary judgment result is that the trajectory point to be judged is within the motion boundary; when p>p0, the boundary judgment result is that the trajectory point to be judged is not within the motion boundary.

4. The twisting gesture analysis and reminder method according to claim 2, characterized in that: The twisting analysis processing is performed on the pre-processed finger motion trajectory set to obtain twisting frequency information and twisting time information, including: S31, performing segmented extraction processing on the pre-processed finger motion trajectory set to obtain a finger motion segmented trajectory set; the finger motion segmented trajectory set includes a segmented trajectory information set of each finger; the segmented trajectory information set includes a plurality of sub-trajectories; S32, performing frequency extraction processing on the finger motion segmented trajectory set to obtain twisting frequency information of each finger; S33, performing motion time accumulation calculation processing on the finger motion segment trajectory set to obtain twisting time information.

5. The twisting gesture analysis and reminder method according to claim 2, characterized in that: The step of distinguishing and processing the twisting frequency information and the twisting time information, and generating and displaying early warning reminder information, includes: S41, determining whether the twisting frequency information is greater than a set frequency threshold, and obtaining a frequency determination result; if the frequency determination result is greater than, generating a frequency warning reminder message; S42, determining whether there is a twisting time value greater than a set time threshold in the twisting time information, and obtaining a time determination result; if the time determination result is yes, generating time warning reminder information; the warning reminder information includes frequency warning reminder information and time warning reminder information; S43, displaying the generated warning reminder information.

6. A twisting gesture analysis and reminder device, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the twisting gesture analysis and reminder method as described in any one of claims 1 to 5.

7. A computer storable medium, characterized in that: The computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the analysis and reminder method of the twisting gesture as described in any one of claims 1 to 5.

8. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the twisting gesture analysis and reminder method as described in any one of claims 1 to 5.

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