Parkinson's disease diagnosis patient handwriting behavior hand dynamics acquisition instrument and acquisition system

Abstract

The present invention discloses a handwriting behavior and hand-dynamic collection instrument and collection system for Parkinson's disease diagnosis, which belongs to the field of medical equipment technology. The handwriting behavior and hand-dynamic collection instrument for Parkinson's disease diagnosis includes a digital handwriting board, a support table, an activity detection board, a detection chair, a trunk activity monitoring module and an active capacitive pen. The front end of the trunk activity monitoring module is provided with a laser positioning sensor; the lower end of the detection chair is provided with a leg activity monitor. The present invention solves the problem that the existing handwriting behavior feature recognition system has no reliable structure for multi-directional behavior feature recognition during actual use, which affects the diagnosis effect. The present invention collects the patient's handwriting behavior movement information by arranging a movable digital handwriting board at the upper end of the support table, and collects the patient's hand movement information in conjunction with the infrared camera capture module of the adjustable activity detection board, makes judgments, and makes intelligent adjustments to improve the diagnosis effect.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment, and in particular to a patient handwriting behavior and hand dynamics acquisition device and acquisition system for Parkinson's disease diagnosis. Background Art

[0002] Parkinson's disease, also often known as "tremor paralysis", is a neurodegenerative disease. The main cause of this disease is the degeneration and death of dopaminergic neurons in the substantia nigra. It may be related to multiple factors such as genetics, environmental factors and aging of the nervous system. Currently, early diagnosis of Parkinson's disease is still very difficult.

[0003] Chinese patent publication number CN119296765A discloses a Parkinson's disease early diagnosis system based on handwriting behavioral feature recognition. This system, which falls within the field of Parkinson's disease technology, includes a data acquisition module, a data processing module, a model building module, a test optimization module, a predictive diagnosis module, and an intelligent management and control module. This patent addresses the current problem of the inability to diagnose Parkinson's disease early based on handwriting behavioral feature recognition, resulting in a lack of early intervention and treatment recommendations for patients, and thus reduced treatment effectiveness.

[0004] The handwriting behavior feature recognition system of the above patent does not have a reliable structure for multi-faceted behavior feature recognition during actual use, which affects the diagnostic effect. Therefore, it does not meet the existing needs. To this end, we have proposed a patient handwriting behavior hand dynamic acquisition device and acquisition system for Parkinson's disease diagnosis. Summary of the Invention

[0005] The purpose of the present invention is to provide a handwriting behavior dynamics acquisition instrument and acquisition system for Parkinson's disease diagnosis, which solves the problem that the handwriting behavior feature recognition system proposed in the above background technology has no reliable structure for multi-faceted behavior feature recognition during actual use, thereby affecting the diagnostic effect.

[0006] To achieve the above-mentioned objectives, the present invention provides the following technical solution: a hand gesture collection device for Parkinson's disease diagnosis, comprising a digital handwriting tablet, a support table, an activity detection board, a detection chair, a trunk activity monitoring module, and an active capacitive stylus, wherein the trunk activity monitoring module is disposed inside the support table, and a laser positioning sensor is disposed at the front end of the trunk activity monitoring module;

[0007] The testing chair is arranged at the front end of the digital handwriting board, and a leg activity monitor is installed at the lower end of the testing chair;

[0008] The activity detection board is installed above the digital handwriting board, and the lower end surface of the activity detection board is installed with an infrared camera capture module for tracking the movement trajectory of the wrist joint;

[0009] The support table is installed at the lower end of the digital handwriting tablet;

[0010] The active capacitive stylus includes a piezoelectric ceramic sensor, a three-axis accelerometer, a high-frequency vibration capture sensor and a gyroscope.

[0011] Preferably, a first sensing layer is provided on both sides of the upper end surface of the digitizer tablet, and a second sensing layer is provided in the middle of the upper end surface of the digitizer tablet, and both the first sensing layer and the second sensing layer are used to detect the signal of the active capacitive stylus.

[0012] Preferably, the first sensing layer is set as an anti-glare coating of frosted glass, and the second sensing layer is set as an anti-scratch coating. The first sensing layer and the second sensing layer are both provided with a transmitting coil grid for generating a magnetic field. The first sensing layer and the second sensing layer are also installed with a receiving circuit for detecting the pen tip resonance signal.

[0013] Preferably, setting the center spacing of adjacent coils corresponding to the transmitting coil grid according to the diameter of the tip of the active capacitive stylus includes:

[0014] Extract the tip diameter of the active capacitive stylus;

[0015] Retrieve the thickness of the first sensing layer and the second sensing layer;

[0016] comparing the thickness of the first sensing layer and the second sensing layer;

[0017] When the thickness of the first sensing layer and the second sensing layer are the same, the diameter of the tip of the active capacitive stylus is Set the center distance between adjacent coils of the transmitting coil grid to the corresponding size;

[0018] When the thicknesses of the first induction layer and the second induction layer are different, the magnetic permeabilities of the induction layer materials corresponding to the first induction layer and the second induction layer are retrieved;

[0019] Retrieve the operating frequencies corresponding to the transmitting coil networks corresponding to the first induction layer and the second induction layer;

[0020] Obtaining a difference in magnetic permeability between the first and second induction layers using the magnetic permeability of the induction layer materials corresponding to the first and second induction layers and the operating frequency of the transmitting coil network;

[0021] The center spacing of adjacent coils corresponding to the transmitting coil grid is set using the total thickness of the first and second sensing layers combined with the diameter of the active capacitive stylus tip and the difference in magnetic permeability between the first and second sensing layers. The center spacing of adjacent coils specifically refers to the straight-line distance between the geometric center points of two adjacent transmitting coils in the transmitting coil grid arranged in the first and second sensing layers.

[0022] Preferably, an intelligent integrated circuit box is installed at the lower end of the digital handwriting board, and the intelligent integrated circuit box includes a data acquisition module and a handwriting preprocessing module. The interior of the digital handwriting board also includes a temperature sensor.

[0023] Preferably, a rotating shaft is installed at one end of the digital handwriting tablet, and the digital handwriting tablet is rotatably connected to the support table through the rotating shaft. A first electric cylinder is installed at the other end of the support table and the digital handwriting tablet, and both ends of the first electric cylinder are rotatably connected to the digital handwriting tablet and the support table respectively through connecting ears.

[0024] Preferably, fixed sleeves are installed on both sides of the support table, a lifting frame is installed inside the fixed sleeve, a positioning frame is installed at the lower end of the lifting frame, a servo motor is fixedly connected to the lower end of the positioning frame, a screw rod is provided between the servo motor and the lifting frame, a movable arm is installed at the upper end of the lifting frame, a first motor is installed on the outside of the movable arm, a motor shaft of the first motor is fixedly connected to the movable arm, and the first motor is fixedly connected to the lifting frame.

[0025] Preferably, a second motor is installed between the movable detection plate and the movable arm, the motor shaft of the second motor is fixedly connected to the movable detection plate, and the second motor is fixedly connected to the upper end of the movable arm, and the infrared camera capture module includes an infrared camera.

[0026] Preferably, support legs are installed under the support table, a stepper motor is installed in the middle of the support legs, a threaded screw is fixedly installed on the motor shaft of the stepper motor, a movable plate is fixedly installed under the detection chair, one end of the movable plate is welded with a positioning block, the threaded screw passes through the positioning block and is threadedly connected to the positioning block, a rolling wheel is installed at the lower end of the movable plate, an integrally formed movable groove is provided inside the support table, a second electric cylinder is installed inside the movable groove, and both ends of the second electric cylinder are fixedly connected to the torso activity monitoring module and the support table respectively.

[0027] Preferably, a seat cushion is provided in the middle of the detection chair, the leg activity monitor is fixedly mounted on the lower end of the seat cushion, a visual sensor is mounted on the lower end of the leg activity monitor, and the infrared camera, laser positioning sensor and visual sensor are all used for identifying the patient's behavioral movements.

[0028] The handwriting behavior and hand dynamics acquisition system for Parkinson's disease diagnosis includes:

[0029] Core processor module, used for receiving and processing data information;

[0030] A data acquisition module is used to collect data information detected by the handwriting pre-processing module, the digital handwriting tablet, the infrared camera capture module, the leg activity monitor, the torso activity monitoring module and the active capacitive pen, and send the collected data information to the core processor module;

[0031] The core processor module is also used to organize the data information transmitted by the data acquisition module, perform real-time signal processing, build a lightweight LSTM model, and control the first electric cylinder, servo motor, first electric motor, stepper motor and second electric cylinder according to the model simulation data.

[0032] Preferably, the data acquisition module is further used for:

[0033] The patient's historical handwriting is determined by a preset coordinate system, and the patient's writing path is determined according to the determination results;

[0034] Setting multiple sampling points based on the writing path and the character distribution parameters of the handwritten characters, and determining the stacking parameters of the pen traces at each sampling point;

[0035] Determine the handwriting pressure value configuration parameters of the patient at each sampling point according to the handwriting trace stacking parameters, and select key sampling points according to the handwriting pressure value configuration parameters;

[0036] Obtain historical handwriting data of key sampling points, determine stroke layout features based on the historical handwriting data, and determine the patient's handwriting focus areas based on the stroke layout features;

[0037] Determine the stroke type of the patient's handwriting in each key area according to the weight of the handwriting key area and the stroke order statistical parameters of each key area, wherein the stroke type includes linear strokes and nonlinear strokes;

[0038] Determine the patient's stroke statistics sequence in each key area according to the patient's stroke type in each key area, and generate a template sequence for each key area according to the stroke statistics sequence;

[0039] Generate handwriting judgment rules for each key area of ​​the patient based on the stroke statistical sequence, and determine the digital dot matrix description parameters of each key area based on the handwriting judgment rules;

[0040] Constructing a dot matrix exclusive code image for each key area according to the digital dot matrix description parameters, and determining the exclusive dot matrix code for each key area according to the dot matrix exclusive code image;

[0041] Collect the patient's handwriting image in each key area, process the handwriting image of each key area using the exclusive dot matrix code of each key area, and obtain a dot matrix image;

[0042] The dot matrix image is digitized to obtain the current handwriting sequence of each key area, and the current handwriting sequence is compared with the template sequence to determine the similarity;

[0043] Based on the similarity, determine whether the handwriting image of the patient in each key area is a mistaken stroke. If so, issue a mistaken stroke reminder and ignore the handwriting image. If not, confirm that the handwriting image is qualified.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. The hand dynamics collector of the present invention adopts a combined intelligent collection structure for detection. By setting a movable digital handwriting board on the upper end of the support table, the digital handwriting board uses a transmitting coil grid and a receiving circuit inside to receive the activity information of the active capacitive pen. The active capacitive pen collects the patient's handwriting behavior information through a piezoelectric ceramic sensor, a three-axis accelerometer, a high-frequency vibration capture sensor and a gyroscope, and cooperates with the infrared camera capture module of the adjustable activity detection board to collect the patient's hand movement information, make judgments, and make intelligent adjustments to improve the diagnostic effect.

[0046] 2. The digital handwriting board and the examination chair of the present invention can both be electrically adjusted. The angle of the digital handwriting board is adjusted by the extension and retraction of the first electric cylinder. After the angle of the digital handwriting board is adjusted, it can meet the diagnosis needs of patients with different heights and body shapes. It can also gradually judge the patient's handwriting based on the step adjustment work, and detect the patient's trunk activity amplitude and leg activity amplitude through the torso activity monitoring module and the leg activity monitor, thereby further improving the diagnostic detection effect and having better adaptability to diversified diagnostic work. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is an axonometric view of the present invention from the front;

[0048] Figure 2 For the present invention Figure 1 A partial enlarged view of area A in the middle;

[0049] Figure 3 is an axonometric view of the present invention from the side;

[0050] Figure 4 This is an axonometric view of the present invention viewed from above;

[0051] Figure 5 For the present invention Figure 4 A partial enlarged view of area B in the middle;

[0052] Figure 6 This is a system block diagram of the present invention.

[0053] Figure: 1. Digital writing board; 101. First sensing layer; 102. Second sensing layer; 103. Rotating shaft; 104. Intelligent integrated circuit box; 105. First electric cylinder; 106. Data acquisition module; 107. Handwriting pre-processing module; 2. Support table; 201. Fixed sleeve; 202. Lifting frame; 203. Positioning frame; 204. Support leg; 205. Servo motor; 206. Movable slot; 207. First motor; 208. Movable arm; 209. Step Intake motor; 210, lead screw; 3, activity detection board; 301, second motor; 302, infrared camera capture module; 303, infrared camera; 4, detection chair; 401, seat cushion; 402, moving plate; 403, rolling wheel; 404, positioning block; 405, leg activity monitor; 406, visual sensor; 5, torso activity monitoring module; 501, laser positioning sensor; 502, second electric cylinder; 6, active capacitive pen; 7, core processor module. DETAILED DESCRIPTION

[0054] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0055] In order to solve the problem that the existing handwriting behavior feature recognition system has no reliable structure to perform multi-faceted behavior feature recognition in actual use, which affects the diagnosis effect, please refer to Figure 1 - Figure 5 , this embodiment provides the following technical solutions:

[0056] The handwriting behavior and hand dynamics acquisition device for Parkinson's disease diagnosis includes a digital handwriting tablet 1, a support table 2, an activity detection board 3, a detection chair 4, a trunk activity monitoring module 5, and an active capacitive stylus 6. The trunk activity monitoring module 5 is arranged inside the support table 2, and a laser positioning sensor 501 is provided at the front end of the trunk activity monitoring module 5.

[0057] The testing chair 4 is arranged at the front end of the digital handwriting board 1, and a leg activity monitor 405 is installed at the lower end of the testing chair 4;

[0058] The activity detection board 3 is installed above the digital handwriting board 1, and the lower end surface of the activity detection board 3 is installed with an infrared camera capture module 302 for tracking the movement trajectory of the wrist joint;

[0059] The support table 2 is installed at the lower end of the digital handwriting tablet 1;

[0060] The active capacitive pen 6 includes a piezoelectric ceramic sensor, a three-axis accelerometer, a high-frequency vibration capture sensor and a gyroscope. The hand dynamic collector adopts a combined intelligent acquisition structure for detection. A movable digital handwriting tablet 1 is set at the upper end of the support table 2. The interior of the digital handwriting tablet 1 uses a transmitting coil grid and a receiving circuit to receive the activity information of the active capacitive pen 6.

[0061] A second motor 301 is installed between the activity detection plate 3 and the movable arm 208. The motor shaft of the second motor 301 is fixedly connected to the activity detection plate 3, and the second motor 301 is fixedly connected to the upper end of the movable arm 208. The infrared camera capture module 302 includes an infrared camera 303, which collects the patient's handwriting behavior information through a piezoelectric ceramic sensor, a three-axis accelerometer, a high-frequency vibration capture sensor and a gyroscope, and cooperates with the infrared camera capture module 302 of the adjustable activity detection plate 3 to collect the patient's hand movement information, make judgments, and make intelligent adjustments to improve the diagnostic effect.

[0062] Specifically, the hand dynamics collector adopts a combined intelligent collection structure for detection. A movable digital handwriting board 1 is set at the upper end of the support table 2. The interior of the digital handwriting board 1 adopts a transmitting coil grid and a receiving circuit to receive the activity information of the active capacitive pen 6. The active capacitive pen 6 collects the patient's handwriting behavior information through a piezoelectric ceramic sensor, a three-axis accelerometer, a high-frequency vibration capture sensor and a gyroscope, and cooperates with the infrared camera capture module 302 of the adjustable activity detection board 3 to collect the patient's hand movement information, make judgments, and make intelligent adjustments to improve the diagnostic effect.

[0063] In order to solve the problem of single diagnostic and recognition structure and poor adaptability in the actual use of existing handwriting behavior feature recognition systems, please refer to Figure 1 - Figure 5 , this embodiment provides the following technical solutions:

[0064] A rotating shaft 103 is installed at one end of the digital handwriting tablet 1, and the digital handwriting tablet 1 is rotatably connected to the support table 2 through the rotating shaft 103. A first electric cylinder 105 is installed at the other end of the support table 2 and the digital handwriting tablet 1. Both ends of the first electric cylinder 105 are rotatably connected to the digital handwriting tablet 1 and the support table 2 through connecting ears. The digital handwriting tablet 1 and the testing chair 4 can both be electrically adjusted. The angle of the digital handwriting tablet 1 is adjusted by the extension and retraction of the first electric cylinder 105. After the angle of the digital handwriting tablet 1 is adjusted, it can meet the diagnosis work of patients with different heights and body shapes, and can gradually judge the patient's handwriting according to the step adjustment work.

[0065] A fixed sleeve 201 is installed on both sides of the support table 2, and a lifting frame 202 is installed inside the fixed sleeve 201. A positioning frame 203 is installed at the lower end of the lifting frame 202. A servo motor 205 is fixedly connected to the lower end of the positioning frame 203. A screw rod is provided between the servo motor 205 and the lifting frame 202. A movable arm 208 is installed at the upper end of the lifting frame 202, and a first motor 207 is installed on the outside of the movable arm 208. The motor shaft of the first motor 207 is fixedly connected to the movable arm 208, and the first motor 207 is fixedly connected to the lifting frame 202.

[0066] A support leg 204 is installed at the bottom of the support table 2, and a stepper motor 209 is installed in the middle of the support leg 204. A threaded screw 210 is fixedly installed on the motor shaft of the stepper motor 209. A movable plate 402 is fixedly installed at the bottom of the detection chair 4. A positioning block 404 is welded to one end of the movable plate 402. The threaded screw 210 passes through the positioning block 404 and is threadedly connected to the positioning block 404. A rolling wheel 403 is installed at the lower end of the movable plate 402. An integrally formed movable groove 206 is provided inside the support table 2, and a second electric cylinder 502 is installed inside the movable groove 206. The two ends of the second electric cylinder 502 are respectively fixedly connected to the trunk activity monitoring module 5 and the support table 2. It can gradually judge the patient's handwriting according to the step adjustment work, and detect the patient's trunk activity amplitude and leg activity amplitude through the trunk activity monitoring module 5 and the leg activity monitor 405, further improving the diagnostic detection effect, and having better adaptability to diversified diagnostic work.

[0067] Specifically, the digital handwriting tablet 1 and the examination chair 4 can both be electrically adjusted. The angle of the digital handwriting tablet 1 is adjusted by the extension and retraction of the first electric cylinder 105. After the angle of the digital handwriting tablet 1 is adjusted, it can meet the diagnosis work of patients with different heights and body shapes, and can gradually judge the patient's handwriting according to the step adjustment work, and detect the patient's trunk activity range and leg activity range through the torso activity monitoring module 5 and the leg activity monitor 405, further improving the diagnostic detection effect, and having better adaptability to diversified diagnostic work.

[0068] In order to solve the problem that the existing handwriting behavior feature recognition system has insufficient diagnostic accuracy and is not conducive to timely treatment in actual use, please refer to Figure 1 、 Figure 4 - Figure 6 , this embodiment provides the following technical solutions:

[0069] An intelligent integrated circuit box 104 is installed at the lower end of the digital handwriting tablet 1. The intelligent integrated circuit box 104 includes a data acquisition module 106 and a handwriting pre-processing module 107. The interior of the digital handwriting tablet 1 also includes a temperature sensor.

[0070] A seat cushion 401 is provided in the middle of the detection chair 4, and a leg activity monitor 405 is fixedly installed at the lower end of the seat cushion 401. A visual sensor 406 is installed at the lower end of the leg activity monitor 405. The infrared camera 303, the laser positioning sensor 501 and the visual sensor 406 are all used for identifying the patient's behavioral movements.

[0071] The handwriting behavior and hand dynamics acquisition system for Parkinson's disease diagnosis includes:

[0072] Core processor module 7, used for receiving and processing data information;

[0073] The data acquisition module 106 is used to collect data information detected by the handwriting pre-processing module 107, the digital handwriting tablet 1, the infrared camera capture module 302, the leg activity monitor 405, the torso activity monitoring module 5 and the active capacitive stylus 6, and send the collected data information to the core processor module 7;

[0074] The core processor module 7 is also used to organize the data information transmitted by the data acquisition module 106, perform real-time signal processing, build a lightweight LSTM model, control the first electric cylinder 105, servo motor 205, first electric motor 207, stepper motor 209 and second electric cylinder 502 according to the model simulation data, integrate multiple data to improve diagnostic accuracy, and finally build a model through the core processor module 7 to improve diagnostic efficiency.

[0075] A first sensing layer 101 is provided on both sides of the upper end surface of the digitizer tablet 1 , and a second sensing layer 102 is provided in the middle of the upper end surface of the digitizer tablet 1 . Both the first sensing layer 101 and the second sensing layer 102 are used to detect the signal of the active capacitive stylus 6 .

[0076] The first sensing layer 101 is configured as an anti-glare coating of frosted glass, and the second sensing layer 102 is configured as an anti-scratch coating. A transmitting coil grid for generating a magnetic field is provided inside the first sensing layer 101 and the second sensing layer 102. A receiving circuit for detecting a pen tip resonance signal is also installed inside the first sensing layer 101 and the second sensing layer 102. The handwriting movement of the same active capacitive pen 6 is respectively received through the first sensing layer 101 and the second sensing layer 102 with different coatings, thereby improving the judgment effect.

[0077] Specifically, the handwriting movements of the same active capacitive pen 6 are respectively received through the first sensing layer 101 and the second sensing layer 102 with different coatings to improve the judgment effect, and the data information detected by the handwriting preprocessing module 107, the digital handwriting board 1, the infrared camera capture module 302, the leg activity monitor 405, the torso activity monitoring module 5 and the active capacitive pen 6 are collected, and multiple data are integrated to improve the diagnosis accuracy. Finally, the model is constructed through the core processor module 7 to improve the diagnosis efficiency.

[0078] Working principle: When in use, the patient sits on the top of the examination chair 4, and the digital handwriting board 1 and the examination chair 4 are electrically adjusted according to the patient's body characteristics to meet the diagnosis work of patients with different heights and body shapes, and the patient's handwriting can be gradually judged according to the step adjustment work, and the trunk activity monitoring module 5 and the leg activity monitor 405 are used to detect the patient's trunk activity range and leg activity range, so as to further improve the diagnosis and detection effect, and the diversified diagnosis work has better adaptability. In the process of the patient using the active capacitive pen 6 to write on the digital handwriting board 1, the first sensing layer 101 and the second sensing layer 102 with different coatings respectively receive the handwriting action of the same active capacitive pen 6, so as to improve the judgment effect, and collect the handwriting preprocessing module 107, the digital handwriting board 1, the infrared camera capture module 302, the leg The data information detected by the hand activity monitor 405, the trunk activity monitoring module 5 and the active capacitive pen 6 are integrated to improve the diagnosis accuracy. Finally, the model is constructed through the core processor module 7 to improve the diagnosis efficiency. The hand dynamic collector adopts a combined intelligent acquisition structure for detection. A movable digital handwriting board 1 is set at the upper end of the support table 2. The internal part of the digital handwriting board 1 adopts a transmitting coil grid and a receiving circuit to receive the activity information of the active capacitive pen 6. The active capacitive pen 6 collects the patient's handwriting behavior movement information through a piezoelectric ceramic sensor, a three-axis accelerometer, a high-frequency vibration capture sensor and a gyroscope, and cooperates with the infrared camera capture module 302 of the adjustable activity detection board 3 to collect the patient's hand movement information, make judgments, and make intelligent adjustments to improve the diagnosis effect.

[0079] Specifically, the center spacing of adjacent coils corresponding to the transmitting coil grid is set according to the tip diameter of the active capacitive stylus, including:

[0080] Extract the tip diameter of the active capacitive stylus;

[0081] Retrieve the thickness of the first sensing layer and the second sensing layer;

[0082] comparing the thickness of the first sensing layer and the second sensing layer;

[0083] When the thickness of the first sensing layer and the second sensing layer are the same, the diameter of the tip of the active capacitive stylus is Set the center distance between adjacent coils of the transmitting coil grid to the corresponding size;

[0084] When the thicknesses of the first induction layer and the second induction layer are different, the magnetic permeabilities of the induction layer materials corresponding to the first induction layer and the second induction layer are retrieved;

[0085] Retrieve the operating frequencies corresponding to the transmitting coil networks corresponding to the first induction layer and the second induction layer;

[0086] Obtaining a difference in magnetic permeability between the first and second induction layers using the magnetic permeability of the induction layer materials corresponding to the first and second induction layers and the operating frequency of the transmitting coil network;

[0087] The difference in magnetic permeability between the first induction layer and the second induction layer is obtained by the following formula:

[0088]

[0089] Where k represents the difference in magnetic permeability between the first and second induction layers; σ 01 and σ 02 f represents the magnetic permeability of the induction layer materials of the first induction layer and the second induction layer respectively; 01 and f 02 Respectively represent the operating frequencies of the first sensing layer and the second sensing layer; specifically, the numerator in the formula calculates the two sensing layers and The absolute value of the difference between the two, with the larger value of the denominator, is a normalization operation. This quantizes the difference in the electromagnetic properties of the two sensing layers to the range [0, 1] (the range of the exponential function), facilitating subsequent unified processing and analysis. The exponential function exp is introduced to perform a nonlinear transformation on the normalized difference, further highlighting or adjusting the impact of the degree of difference on the final result k. The transmitting coil generates an alternating magnetic field through an alternating current, and the sensing layer responds by generating an induced current due to electromagnetic induction. The difference in permeability k reflects the difference in the electromagnetic properties of the two sensing layers. The center-to-center spacing of adjacent coils affects the magnetic field distribution and inter-coil coupling. If the electromagnetic properties of the sensing layers differ significantly (as reflected by the k value), to ensure consistent and accurate sensing, the coil spacing needs to be adjusted to optimize the magnetic field distribution so that the two sensing layers can best respond to the transmitting coil's magnetic field. For example, if the difference is large, the spacing needs to be reduced to enhance magnetic field coupling and ensure that both sensing layers can effectively sense the magnetic field. Conversely, if the difference is small, the spacing can be appropriately increased. Differences in the electromagnetic properties of different sensing layers can affect the sensing signal, and inappropriate coil spacing can lead to signal interference or unevenness. By adjusting the spacing according to the k value, the magnetic field signals received by the two sensing layers can be made more uniform and stable, reducing signal interference, ensuring the consistency of the sensing signals, and improving the performance of the entire sensing system.

[0090] The center spacing of adjacent coils corresponding to the transmitting coil grid is set using the total thickness of the first and second sensing layers combined with the diameter of the active capacitive stylus tip and the difference in magnetic permeability between the first and second sensing layers. The center spacing of adjacent coils specifically refers to the straight-line distance between the geometric center points of two adjacent transmitting coils in the transmitting coil grid arranged in the first and second sensing layers.

[0091] The center spacing between adjacent coils corresponding to the transmitting coil grid is obtained by the following formula:

[0092]

[0093] Where P represents the distance between the centers of adjacent coils corresponding to the transmitting coil grid; k represents the difference in magnetic permeability between the first and second induction layers; D represents the diameter of the active capacitive stylus tip; T c represents the thickness difference between the first sensing layer and the second sensing layer; T represents the total thickness of the first sensing layer and the second sensing layer; x represents the vibration frequency coupling factor, ranging from 0.6 to 1.2; y represents the biomechanical adjustment coefficient, ranging from 0.8 to 1.5; f0 represents the reference resonant frequency of the pen tip (250kHz); f represents the average real-time working frequency of the pen tip; S represents the minimum signal-to-noise ratio threshold, ranging from (6dB, 10dB); Q represents the pen pressure-frequency modulation factor, where Q∈[0.5, 3.0]. This step is a preliminary scaling based on the geometric dimensions of the capacitive stylus. The scaling factor is determined by comprehensively considering factors such as the propagation characteristics of the capacitive stylus signal in the sensing layer and the spatial distribution of electromagnetic induction. It converts the stylus tip diameter into a fundamental quantity compatible with subsequent physical processes, laying the foundation for further calculation of the center-to-center spacing of adjacent coils in combination with other physical parameters. In the figure, x (vibration frequency coupling factor) ranges from 0.6 to 1.2, reflecting the degree to which vibrations generated by an active capacitive stylus affect frequency coupling in actual use. Vibration can cause changes in the electromagnetic coupling between the stylus and the sensing layer. x is used to quantify this effect and is an abstract representation of the complex factors in actual use scenarios. c is the thickness difference between the first and second sensing layers, and T is the total thickness. This index term reflects the influence of the sensing layer structure on the electromagnetic properties. c The larger the total thickness T, the smaller the exponential term, indicating that differences in the sensing layer structure have a greater inhibitory effect on electromagnetic coupling. In practice, differences in sensing layer thickness can alter the distribution of electric and magnetic fields, thus affecting signal sensing. This exponential term mathematically describes this influence. In , y is used to quantify the influence of this biomechanical factor. This demonstrates the relationship between the stylus tip's base frequency, the frequency modulation effect of pen pressure, the real-time operating frequency, and signal quality requirements. A logarithmic function further translates this relationship into an adjustment for the center-to-center spacing of adjacent coils, describing the impact of capacitive stylus signal characteristics on spacing settings from an electrical perspective. Adding the results of the previous two parts and then taking the cube root is a normalization and readjustment process for the combined influence of multiple physical factors. The cube root operation can make the value range and change trend of the comprehensive influencing factor more consistent with the mapping relationship between the center spacing of adjacent coils and various physical factors in actual physical scenarios, making the final calculated spacing value more reasonable and accurate. The formula comprehensively considers the geometric size (D), electrical characteristics (f, f0, Q), and sensing layer structural characteristics (T, T c ) as well as tremor and biomechanical factors (x, y) experienced in actual use. This comprehensive calculation of multiple factors allows the obtained center-to-center spacing of adjacent coils to be better adapted to different types of active capacitive styluses and sensing layer structures, significantly improving the comprehensiveness and accuracy of adaptation. Because the formula includes parameters reflecting real-time operating conditions (such as f and Q), the center-to-center spacing of adjacent coils can be adjusted in real time based on dynamic changes in the pen pressure, frequency, and other factors during actual operation. This ensures that the system maintains good adaptability in different usage scenarios and operations, enhancing the system's dynamic adaptability. By introducing a minimum signal-to-noise ratio threshold, S, signal quality requirements are taken into account when calculating the center-to-center spacing of adjacent coils. This ensures that the set spacing meets certain signal sensing quality standards, avoiding problems such as signal interference and low signal-to-noise ratio caused by improper spacing settings, thereby ensuring the adaptability of the spacing acquisition from a signal quality perspective.

[0094] The working principle and technical effect of the above technical solution are as follows: First, the diameter of the active capacitive stylus tip is extracted, and the corresponding thicknesses of the first and second sensing layers are retrieved and compared. This step is to determine the structural characteristics of the sensing layers, as the thickness relationship of the sensing layers will affect the subsequent setting of the center-to-center spacing of adjacent coils. If the two layers have the same thickness, the spacing can be set based on a relatively simple rule (a certain ratio of the active capacitive stylus tip diameter); if they are different, a more complex method combining the total thickness and other parameters is required. In the above technical solution, the difference in magnetic permeability coefficient k reflects the degree of difference in the electromagnetic properties of the first and second sensing layers. During electromagnetic induction, different magnetic permeabilities and operating frequencies affect the propagation of the magnetic field in the sensing layers and the induction effect. A large k value indicates significant differences in the electromagnetic properties of the two sensing layers. In this case, the center-to-center spacing P of adjacent coils will be affected according to the calculation formula. Generally, the spacing needs to be reduced to ensure that the magnetic field effectively stimulates the induction signal in the two sensing layers and avoid signal deviation due to characteristic differences. Conversely, when the k value is small, the spacing can be appropriately increased. The active capacitive stylus tip diameter D determines the range of the sensing area. The larger the diameter, the wider and more uniform the magnetic field generated by the transmitting coil needs to be to ensure accurate sensing of the capacitive stylus. Therefore, D will be involved in the calculation of the spacing P so that the spacing can adapt to the physical size of the capacitive stylus. The total thickness T of the first sensing layer and the second sensing layer and the thickness difference T cIt will also affect the propagation and attenuation of the magnetic field. The larger the total thickness T, the more obvious the magnetic field attenuation may be. It is necessary to adjust the coil spacing to ensure the induction strength. The thickness difference T c This can cause uneven magnetic field distribution within the sensing layer. Adjusting the spacing through a formula can compensate for this unevenness in sensing. Parameters in this formula, such as the vibration frequency coupling factor x and the biomechanical adjustment coefficient y, take into account multiple factors, including pen vibration during use, biomechanical properties, and the frequency and signal-to-noise ratio of the electromagnetic signal. These parameters, combined with those related to electromagnetic properties and structural dimensions, ensure that the calculated spacing between adjacent coil centers meets the electromagnetic sensing requirements of capacitive styluses in various practical usage scenarios. By adjusting the spacing, the magnetic field distribution is optimized, ensuring the accuracy and stability of the sensing signal.

[0095] Accurate calculation based on the formula (when the thickness is different): When the thickness of the first sensing layer and the second sensing layer is different, use the given formula to calculate the center distance between adjacent coils. The parameters in the formula work together: Parameters related to the capacitive pen: D (active capacitive pen tip diameter) is the basic size parameter, which directly affects the spacing. f (average value of the real-time working frequency of the pen tip), f0 (pen tip reference resonant frequency), and Q (pen pressure-frequency modulation factor) reflect the electrical characteristics of the capacitive pen. Changes in pen pressure will affect the frequency through Q, and then affect the setting of the center distance between adjacent coils to adapt to signal sensing requirements under different pen pressures. Among them, T (total thickness) and T cThe thickness difference reflects the structural information of the sensing layer. The thickness difference Tc appears in the exponential term, while the total thickness T appears in the denominator. Together, they influence the spacing calculation, ensuring that the spacing setting matches the actual structure of the sensing layer. The adjustment factor parameters, x (tremor frequency coupling factor) and y (biomechanical adjustment coefficient), account for the possible impact of tremor on frequency coupling, while y (biomechanical adjustment coefficient) adjusts the calculation from a biomechanical perspective, ensuring that the spacing setting better reflects the combined effects of multiple factors in actual usage scenarios. S (minimum signal-to-noise ratio threshold) measures signal quality requirements and influences the calculation of the logarithmic term in the formula, ensuring that the set spacing meets certain signal sensing quality standards. By considering factors such as the tip diameter of the active capacitive stylus and the thickness of the sensing layer, this solution allows the center-to-center spacing of adjacent coils in the transmitting coil grid to better adapt to the actual characteristics of the stylus and sensing layer. Whether using styluses of different sizes or sensing layer structures, targeted spacing can be set, improving the system's adaptability to various hardware configurations. By combining the stylus's electrical characteristics (such as frequency-related parameters) with the minimum signal-to-noise ratio threshold S, which reflects signal quality, the calculated center-to-center spacing of adjacent coils helps optimize signal sensing. It can more accurately sense capacitive stylus signals under varying pen pressures and operating frequencies, reducing signal interference and misjudgment, and improving signal sensing accuracy and reliability. The introduction of the vibration frequency coupling factor x and the biomechanical adjustment coefficient y accounts for potential vibration and biomechanical factors in actual use. This allows the center-to-center spacing of adjacent coils to be more tailored to actual usage scenarios, ensuring optimal performance even in complex environments and enhancing the system's practicality and stability.

[0096] In one embodiment, the data acquisition module is further configured to:

[0097] The patient's historical handwriting is determined by a preset coordinate system, and the patient's writing path is determined according to the determination results;

[0098] Setting multiple sampling points based on the writing path and the character distribution parameters of the handwritten characters, and determining the stacking parameters of the pen traces at each sampling point;

[0099] Determine the handwriting pressure value configuration parameters of the patient at each sampling point according to the handwriting trace stacking parameters, and select key sampling points according to the handwriting pressure value configuration parameters;

[0100] Obtain historical handwriting data of key sampling points, determine stroke layout features based on the historical handwriting data, and determine the patient's handwriting focus areas based on the stroke layout features;

[0101] Determine the stroke type of the patient's handwriting in each key area according to the weight of the handwriting key area and the stroke order statistical parameters of each key area, wherein the stroke type includes linear strokes and nonlinear strokes;

[0102] Determine the patient's stroke statistics sequence in each key area according to the patient's stroke type in each key area, and generate a template sequence for each key area according to the stroke statistics sequence;

[0103] Generate handwriting judgment rules for each key area of ​​the patient based on the stroke statistical sequence, and determine the digital dot matrix description parameters of each key area based on the handwriting judgment rules;

[0104] Constructing a dot matrix exclusive code image for each key area according to the digital dot matrix description parameters, and determining the exclusive dot matrix code for each key area according to the dot matrix exclusive code image;

[0105] Collect the patient's handwriting image in each key area, process the handwriting image of each key area using the exclusive dot matrix code of each key area, and obtain a dot matrix image;

[0106] The dot matrix image is digitized to obtain the current handwriting sequence of each key area, and the current handwriting sequence is compared with the template sequence to determine the similarity;

[0107] Based on the similarity, determine whether the handwriting image of the patient in each key area is a mistaken stroke. If so, issue a mistaken stroke reminder and ignore the handwriting image. If not, confirm that the handwriting image is qualified.

[0108] The beneficial effects of the above technical solution are: by accurately locating the patient's frequently written area based on the patient's historical handwriting habits, and then using the statistical attributes of the frequently written content in the frequently written area as a reference basis for evaluating the subsequent handwriting content, it is possible to accurately determine whether the patient's handwriting is deliberate, avoid missed judgments or misjudgments, improve stability and reliability, lay the foundation for subsequent behavioral evaluations, and improve practicality.

[0109] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0110] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A handwriting behavior and hand dynamics acquisition device for Parkinson's disease diagnosis, comprising a digital handwriting tablet (1), a support table (2), an activity detection board (3), a detection chair (4), a trunk activity monitoring module (5) and an active capacitive pen (6), characterized in that: The trunk activity monitoring module (5) is arranged inside the support table (2), and a laser positioning sensor (501) is arranged at the front end of the trunk activity monitoring module (5); The detection chair (4) is arranged at the front end of the digital handwriting board (1), and a leg activity monitor (405) is installed at the lower end of the detection chair (4); The activity detection plate (3) is installed above the digital handwriting board (1), and the lower end surface of the activity detection plate (3) is installed with an infrared camera capture module (302) for tracking the movement trajectory of the wrist joint; The support table (2) is installed at the lower end of the digital handwriting board (1); The active capacitive stylus (6) includes a piezoelectric ceramic sensor, a three-axis accelerometer, a high-frequency vibration capture sensor, and a gyroscope; A first sensing layer (101) is provided on both sides of the upper end surface of the digital handwriting board (1), and a second sensing layer (102) is provided in the middle of the upper end surface of the digital handwriting board (1), and the first sensing layer (101) and the second sensing layer (102) are both used to detect the signal of the active capacitive pen (6), the first sensing layer (101) is provided as an anti-glare coating of frosted glass, and the second sensing layer (102) is provided as an anti-scratch coating, and a transmitting coil grid for generating a magnetic field is provided inside the first sensing layer (101) and the second sensing layer (102), and a receiving circuit for detecting the pen tip resonance signal is also installed inside the first sensing layer (101) and the second sensing layer (102); The center spacing between adjacent coils in the transmitting coil grid is set based on the tip diameter of the active capacitive stylus, including: Extract the tip diameter of the active capacitive stylus; Retrieve the thickness of the first sensing layer and the second sensing layer; comparing the thickness of the first sensing layer and the second sensing layer; When the thickness of the first sensing layer and the second sensing layer are the same, the diameter of the tip of the active capacitive stylus is Set the center distance between adjacent coils of the transmitting coil grid to the corresponding size; When the thicknesses of the first induction layer and the second induction layer are different, the magnetic permeabilities of the induction layer materials corresponding to the first induction layer and the second induction layer are retrieved; The difference in magnetic permeability between the first induction layer and the second induction layer is obtained by the following formula: Where k represents the difference in magnetic permeability between the first and second induction layers; σ 01 and σ 02 f represents the magnetic permeability of the induction layer materials of the first induction layer and the second induction layer respectively; 01 and f 02 Respectively represent the operating frequencies corresponding to the first sensing layer and the second sensing layer; Retrieve the operating frequencies corresponding to the transmitting coil networks corresponding to the first induction layer and the second induction layer; Obtaining a difference in magnetic permeability between the first and second induction layers using the magnetic permeability of the induction layer materials corresponding to the first and second induction layers and the operating frequency of the transmitting coil network; The center spacing between adjacent coils corresponding to the transmitting coil grid is obtained by the following formula: Where P represents the distance between the centers of adjacent coils corresponding to the transmitting coil grid; k represents the difference in magnetic permeability between the first and second induction layers; D represents the diameter of the active capacitive stylus tip; T c represents the thickness difference between the first and second sensing layers; T represents the total thickness of the first and second sensing layers; x represents the vibration frequency coupling factor, ranging from 0.6 to 1.2; y represents the biomechanical adjustment coefficient, ranging from 0.8 to 1.5; f0 represents the reference resonant frequency of the pen tip (250kHz); f represents the average real-time operating frequency of the pen tip; S represents the minimum signal-to-noise ratio threshold, ranging from (6dB to 10dB); Q represents the pen pressure-frequency modulation factor, where Q∈[0.5, 3.0]; The center spacing of adjacent coils corresponding to the transmitting coil grid is set using the total thickness of the first and second sensing layers combined with the diameter of the active capacitive stylus tip and the difference in magnetic permeability between the first and second sensing layers. The center spacing of adjacent coils specifically refers to the straight-line distance between the geometric center points of two adjacent transmitting coils in the transmitting coil grid arranged in the first and second sensing layers.

2. The handwriting behavior and hand dynamics acquisition device for Parkinson's disease diagnosis according to claim 1, characterized in that: An intelligent integrated circuit box (104) is installed at the lower end of the digital handwriting board (1), and the intelligent integrated circuit box (104) includes a data acquisition module (106) and a handwriting preprocessing module (107). The interior of the digital handwriting board (1) also includes a temperature sensor. A rotating shaft (103) is installed at one end of the digital handwriting board (1), and the digital handwriting board (1) is rotatably connected to the support table (2) via the rotating shaft (103). A first electric cylinder (105) is installed at the other end of the support table (2) and the digital handwriting board (1), and both ends of the first electric cylinder (105) are rotatably connected to the digital handwriting board (1) and the support table (2) respectively through connecting ears.

3. The hand gesture collection device for Parkinson's disease diagnosis according to claim 2, characterized in that: Both sides of the support table (2) are installed with fixed sleeves (201), a lifting frame (202) is installed inside the fixed sleeve (201), a positioning frame (203) is installed at the lower end of the lifting frame (202), a servo motor (205) is fixedly connected to the lower end of the positioning frame (203), a screw rod is provided between the servo motor (205) and the lifting frame (202), a movable arm (208) is installed at the upper end of the lifting frame (202), a first motor (207) is installed outside the movable arm (208), a motor shaft of the first motor (207) is fixedly connected to the movable arm (208), and the first motor (207) is fixedly connected to the lifting frame (202).

4. The hand gesture collection device for Parkinson's disease diagnosis according to claim 3, characterized in that: A second motor (301) is installed between the movable detection plate (3) and the movable arm (208); a motor shaft of the second motor (301) is fixedly connected to the movable detection plate (3), and the second motor (301) is fixedly connected to the upper end of the movable arm (208); and the infrared camera capture module (302) includes an infrared camera (303).

5. The hand gesture collection device for Parkinson's disease diagnosis according to claim 4, characterized in that: A support leg (204) is installed below the support table (2), a stepper motor (209) is installed in the middle of the support leg (204), a threaded screw (210) is fixedly installed on the motor shaft of the stepper motor (209), a movable plate (402) is fixedly installed below the detection chair (4), one end of the movable plate (402) is welded to a positioning block (404), the threaded screw (210) passes through the positioning block (404) and is threadedly connected to the positioning block (404), a rolling wheel (403) is installed at the lower end of the movable plate (402), an integrally formed movable groove (206) is provided inside the support table (2), a second electric cylinder (502) is installed inside the movable groove (206), and both ends of the second electric cylinder (502) are fixedly connected to the trunk activity monitoring module (5) and the support table (2), respectively.

6. The handwriting behavior and hand dynamics acquisition device for Parkinson's disease diagnosis according to claim 5, characterized in that: A seat cushion (401) is provided in the middle of the detection chair (4), the leg activity monitor (405) is fixedly mounted on the lower end of the seat cushion (401), and a visual sensor (406) is mounted on the lower end of the leg activity monitor (405). The infrared camera (303), the laser positioning sensor (501) and the visual sensor (406) are all used for identifying the patient's behavior and movements.

7. The handwriting behavior and hand dynamics acquisition device for Parkinson's disease diagnosis according to claim 1, characterized in that: Also includes: A core processor module (7), configured to receive and process data information; a data acquisition module (106) for collecting data information detected by the handwriting pre-processing module (107), the digital handwriting tablet (1), the infrared camera capture module (302), the leg activity monitor (405), the trunk activity monitoring module (5) and the active capacitive stylus (6), and sending the collected data information to the core processor module (7); The core processor module (7) is further used to organize the data information transmitted by the data acquisition module (106), perform real-time signal processing, construct a lightweight LSTM model, and control the first electric cylinder (105), the servo motor (205), the first electric motor (207), the stepper motor (209), and the second electric cylinder (502) according to the model simulation data.

8. The handwriting behavior and hand dynamics acquisition device for Parkinson's disease diagnosis according to claim 7, characterized in that: The data acquisition module is also used for: The patient's historical handwriting is determined by a preset coordinate system, and the patient's writing path is determined according to the determination results; Setting multiple sampling points based on the writing path and the character distribution parameters of the handwritten characters, and determining the stacking parameters of the pen traces at each sampling point; Determine the handwriting pressure value configuration parameters of the patient at each sampling point according to the handwriting trace stacking parameters, and select key sampling points according to the handwriting pressure value configuration parameters; Obtain historical handwriting data of key sampling points, determine stroke layout features based on the historical handwriting data, and determine the patient's handwriting focus areas based on the stroke layout features; Determine the stroke type of the patient's handwriting in each key area according to the weight of the handwriting key area and the stroke order statistical parameters of each key area, wherein the stroke type includes linear strokes and nonlinear strokes; Determine the patient's stroke statistics sequence in each key area according to the patient's stroke type in each key area, and generate a template sequence for each key area according to the stroke statistics sequence; Generate handwriting judgment rules for each key area of ​​the patient based on the stroke statistical sequence, and determine the digital dot matrix description parameters of each key area based on the handwriting judgment rules; Constructing a dot matrix exclusive code image for each key area according to the digital dot matrix description parameters, and determining the exclusive dot matrix code for each key area according to the dot matrix exclusive code image; Collect the patient's handwriting image in each key area, process the handwriting image of each key area using the exclusive dot matrix code of each key area, and obtain a dot matrix image; The dot matrix image is digitized to obtain the current handwriting sequence of each key area, and the current handwriting sequence is compared with the template sequence to determine the similarity; Based on the similarity, determine whether the handwriting image of the patient in each key area is a mistaken stroke. If so, issue a mistaken stroke reminder and ignore the handwriting image. If not, confirm that the handwriting image is qualified.

Images