Balance ability detection training system and control method thereof

Abstract

The invention discloses a balance ability detection training system and a control method thereof, and belongs to the technical field of human body balance ability detection and training. The system comprises a pressure detection unit which comprises four pressure detection areas which are respectively corresponding to a left front area, a left rear area, a right front area and a right rear area; the data acquisition unit is used for acquiring pressure data of each area; the auxiliary pressure detection unit is arranged at the auxiliary supporting part and is used for detecting the contact pressure; and the processing unit is used for generating training related information according to the pressure data and judging an independent standing state according to the contact pressure. Standing posture errors are eliminated through four-area design, and eight-area pressure analysis and gravity center calculation are achieved; and functions of configurable training targets, intelligent collection and the like are supported. The device can be widely applied to various scenes such as medical rehabilitation, physical training and elderly health.

Description

[Technical Field] This invention relates to the field of human balance ability detection and training technology, specifically to a system and method for assessing and training human balance ability. Background Technology

[0001] Balance is fundamental to daily human activities. In health management, balance dysfunction can impact quality of life; in sports training, balance is a key indicator of an athlete's performance; and in geriatric health, balance is closely linked to fall risk. Therefore, balance assessment and training have broad application value.

[0002] Currently, commonly used balance assessment and training equipment mainly suffers from the following technical shortcomings: 1. Measurement accuracy is affected by standing posture. Existing devices mostly use an integrated force plate, which detects pressure distribution by placing sensors at multiple locations on the force plate. This design has inherent flaws: changes in the tester's standing position, foot opening, and foot size directly affect the measurement results, leading to large data deviations for the same user in different measurements, and a lack of objectivity and comparability in the evaluation.

[0003] 2. The assessment uses only one dimension, making it impossible to accurately pinpoint the problem area. Existing equipment typically only provides macroscopic indicators such as overall center of gravity position or pressure center trajectory, failing to precisely quantify the pressure distribution across different body parts. In particular, current technology cannot distinguish the pressure differences between the left forefoot and left hindfoot, or the right forefoot and right hindfoot. However, in balance training, the independent control capabilities of these areas have different functional significance: the left forefoot reflects left-side forward control capability, and the left hindfoot reflects left-side backward support capability. The inability to distinguish these areas makes it difficult for assessors to accurately determine which specific part of the user's body has insufficient control, hindering targeted training.

[0004] 3. The training model is rigid and lacks personalization. Existing equipment typically offers only a few preset training modes, limiting users to a limited selection and preventing the design of personalized training programs to meet specific needs. Training objectives are often vague, lacking precise quantitative indicators, making accurate rehabilitation difficult.

[0005] 4. Lack of assessment or subjective assessment of independent standing ability In balance training, whether a user uses external force to complete the training is a key indicator for assessing their true balance ability. However, existing devices generally have the following shortcomings: most devices do not monitor whether the user uses external force at all, assuming that all training is completed in an independent state, resulting in inflated evaluation results; a few devices rely on manual observation, which is highly subjective and cannot be quantified.

[0006] 5. The feedback mechanism is crude, resulting in the loss of process information. Most devices use a simple "success / failure" binary judgment, only recording the final result, losing a lot of process information, and failing to accurately quantify the user's control capabilities and progress. Summary of the Invention

[0007] I. Purpose of the Invention This invention aims to overcome the aforementioned deficiencies of the prior art and provides a balance ability testing and training system and its control method based on four-zone pressure detection. By designing four independent pressure zones, it fundamentally eliminates measurement errors caused by differences in standing posture. On this basis, it realizes multi-dimensional pressure analysis, real-time center of gravity calculation, configurable training modes, quantitative assessment of independent standing ability, and intelligent training management, providing accurate, flexible, and intelligent technical solutions for various scenarios that require balance ability assessment and training. Technical solution

[0008] 2.1 Hardware Components A balance ability testing and training system, comprising: The pressure detection unit includes four pressure detection zones, which correspond to the left front, left rear, right front, and right rear regions when a person is standing. Each pressure detection zone includes at least one pressure sensor. The four pressure detection zones are independent of each other and each outputs an independent pressure signal. A data acquisition unit is connected to the four pressure detection zones and is used to acquire pressure data from each pressure detection zone. At least one auxiliary pressure detection unit is deployed at an auxiliary support part that the user may use for leverage, for detecting the contact pressure between the user and the auxiliary support part; A processing unit, connected to the data acquisition unit and the auxiliary pressure detection unit, is used to receive and process the pressure data and the contact pressure data; An output unit, connected to the processing unit, is used to output information to the user.

[0009] 2.2 Core Algorithm The processing unit is configured to perform the following operations: 2.2.1 Calculation of pressure characteristic value Based on the pressure data from the four pressure detection zones, calculate the pressure ratio for the following zones: Left anterior region pressure ratio = P_left anterior / total pressure × 100% Left rear pressure ratio = P_left rear / total pressure × 100% Right anterior region pressure ratio = P_right anterior / total pressure × 100% Right rear pressure ratio = P_right rear / total pressure × 100% Anterior pressure ratio = (P_left anterior + P_right anterior) / total pressure × 100% Rear pressure ratio = (P_left rear + P_right rear) / Total pressure × 100% Pressure ratio in the left side region = (P_front left + P_rear left) / total pressure × 100% Right side pressure ratio = (P_Right Anterior + P_Right Rear) / Total Pressure × 100% Wherein, total pressure = P_left front + P_left rear + P_right front + P_right rear.

[0010] 2.2.2 Determining the position of the center of gravity Establish a two-dimensional coordinate system, where the X-axis represents the left-right direction and the Y-axis represents the front-back direction; The position of the center of gravity on the X-axis is determined by the pressure ratio between the left and right regions. The position of the center of gravity on the Y-axis is determined by the pressure ratio between the front and rear regions.

[0011] In one implementation, the coordinate system is defined as a range of 0-100. Based on clinical standards (left-right balance 50:50, anterior-posterior balance 40:60), the ideal center of gravity coordinates are (50, 40). The formula for calculating the center of gravity coordinates is: X = Pressure ratio of the right-hand region • Y = Pressure ratio of the rear region 2.2.3 Determination of Independent Standing Status Acquire the real-time contact pressure data of the auxiliary pressure detection unit; The real-time contact pressure data is compared with a preset threshold. The comparison results determine whether the user is in an independent standing position.

[0012] In one implementation, multiple preset thresholds are set to distinguish different levels of leverage.

[0013] 2.3 Training Control Methods The processing unit is also configured to perform at least one of the following training control methods: 2.3.1 Training method based on center of gravity position Obtain at least one target location; Obtain the real-time position of the human body's center of gravity; Training feedback information is generated based on the relationship between the real-time location and the target location.

[0014] 2.3.2 Training Method Based on Regional Pressure Characteristics Obtain the target pressure characteristics for at least one pressure detection zone; Obtain the real-time pressure characteristics of the pressure detection area; Training feedback information is generated based on the relationship between the real-time pressure features and the target pressure features.

[0015] 2.3.3 Configurability of Training Objectives The target location and target pressure characteristics can be freely set by the user.

[0016] In one implementation, the target location includes: At least one fixed position; A movement trajectory formed by connecting multiple location points.

[0017] In another embodiment, the target pressure characteristic includes: A fixed pressure value or pressure ratio; The changing pressure curve.

[0018] 2.4 Intelligent Collection Mechanism The processing unit is further configured to: Based on the evaluation results of each training session, automatically identify training items with scores below a preset threshold; The training items with scores below a preset threshold are stored in the collection library; Receive manual collection instructions from users and save the training items specified by users into the collection library; In subsequent training, the training items in the collection will be used first for users to practice. Beneficial effects

[0019] Compared with the prior art, the present invention has the following beneficial effects: 1. High measurement accuracy – eliminating the influence of standing posture. With its four independent pressure detection zones, the system can accurately acquire the pressure distribution in four areas of the sole: left front, left back, right front, and right back. Regardless of whether the user's feet are open or closed, or whether their feet are large or small, the system can accurately measure the actual pressure in each area, fundamentally eliminating measurement errors caused by differences in standing posture.

[0020] 2. Comprehensive Assessment Dimensions – Stress Analysis in Eight Regions Based on data from four zones, this invention can calculate the pressure ratio of eight zones, achieving a multi-dimensional assessment from overall to local perspectives. For left-sided hemiplegic users, traditional devices can only show a rightward shift in the center of gravity, while this invention can accurately pinpoint the weakness in the left forefoot and the relatively good support in the left hindfoot, providing precise evidence for targeted training.

[0021] 3. Flexible and configurable training methods – from fixed modes to programmable platforms It supports multiple training modes, and all training goals can be freely defined by the user. Therapists can design an unlimited number of personalized training programs based on the user's specific functional impairments.

[0022] 4. Objective quantification of independent standing ability The auxiliary pressure detection unit enables objective monitoring of the user's leverage. Leverage data is integrated with training performance data to generate objective indicators such as independent standing time and independent completion rate.

[0023] 5. Intelligent bookmarking mechanism – a personalized "error notebook" The system automatically identifies training items that users perform poorly on and saves them to a favorites library, creating a personalized "weakness reinforcement set." Users can also manually save training items they deem valuable. In subsequent training sessions, the system prioritizes using items from the favorites library, allowing users to specifically strengthen their weaknesses.

[0024] 6. Wide range of application scenarios This system can be widely used in various scenarios such as medical rehabilitation, sports training, elderly health, fitness activities, scientific research and education, and home health. Attached Figure Description

[0025] Figure 1 The hardware composition block diagram and connection method of the present invention. In the picture: 1: Input unit (such as touch screen operation) 2: Output unit (such as LCD touch screen / mobile terminal) 3: Auxiliary pressure detection unit (such as armrests / countertops, etc.) 4: Data acquisition unit (multi-channel analog-to-digital converter) 5: Processing Unit (Embedded Microcontroller / Single-Board Computer) 6: Pressure detection unit (four zones) 601: Left anterior zone (LF) 602: Left Rear Zone (LH) 603: Right anterior region (RF) 604: Right Rear Zone (RH) Figure 2 Schematic diagram of four pressure testing zones and eight area divisions In the picture: 101: Pressure Zone (As shown in the figure, L indicates that the left side is the training target zone) 102: Number of targets (number of training coordinate points) 201: Number of training sessions 301: Target pressure (target pressure value for pressure zone 101) 302: Lateral pressure (target pressure value on the right side of the training coordinate point on the x-axis) 303: Longitudinal pressure (target pressure value on the back side of the training coordinate point on the Y-axis) 401: Duration of Hold (The duration of time the body's center of gravity is at the target pressure value / point) 501: Rest Duration Figure 3 Schematic diagram of the static center of gravity control training mode interface Figure 4 : Interface diagram of dynamic center of gravity tracking training mode In the picture: 601: Movement speed (used to set the speed at which the training coordinates / cursor moves along the trajectory) Figure 5 Intelligent collection mechanism workflow diagram In the picture: Node 10: Training End Node 12: Calculate the training score for this session. Node 14: Determine if the score is below the preset threshold. Node 16: If the value is not lower than the threshold, the process ends. Node 18: If the value is below the threshold, automatically save it to the collection library. Node 20: Users can manually save training projects. Node 22: Collections Management (Sorting, Display) Node 24: Subsequent training will prioritize using the bookmarked items. Node 26: Start the next training session Yes: Yes NO: No Detailed Implementation

[0026] Example 1: Hardware System Implementation like Figure 1 Figure 2 As shown, in this embodiment, the pressure detection unit 6 employs four independent thin-film pressure sensors, respectively positioned on the left anterior zone 601, left posterior zone 602, right anterior zone 603, and right posterior zone 604 of the sole of the foot. Each sensor area measures 15cm × 10cm, covering the main pressure-bearing areas of the adult foot. The four sensors operate independently, each outputting an independent voltage signal, the magnitude of which is proportional to the applied pressure.

[0027] The data acquisition unit 4 uses a multi-channel analog-to-digital converter module to synchronously acquire the voltage signals of the four sensors at a sampling frequency of not less than 50Hz, convert them into digital pressure values, and then send them to the processing unit 5.

[0028] Processing unit 5 employs an embedded microcontroller, built into the pressure detection board. After receiving pressure data, processing unit 5 calculates the pressure ratio of the eight regions and the center of gravity coordinates in real time, and generates feedback information based on the training mode.

[0029] The auxiliary pressure detection unit 3 adopts different deployment methods depending on the product form: In the conventional training device configuration, thin-film pressure sensors are deployed on the left and right armrests to detect hand grip pressure. In the standing frame configuration, a pressure sensor array is deployed in the support area of ​​the platform to detect the pressure exerted on the upper body.

[0030] Output unit 2 uses an LCD touch screen, which is connected to processing unit 5 to display information such as center of gravity cursor, training target, and pressure value in real time.

[0031] Power supply options include external power supply or built-in battery power supply, supporting mobile usage scenarios.

[0032] Example 2: Calculation of the pressure ratio between four-zone hardware and eight-zone hardware like Figure 1 Figure 2 As shown in the figure, this embodiment details how the four-zone hardware implements the calculation of the pressure ratio of the eight zones.

[0033] When the user stands on pressure detection unit 6, the real-time pressure values ​​output by the four pressure detection zones are as follows: P_LF: Pressure value of 601 in the left anterior zone P_LH: Pressure value of 602 in the left posterior zone P_RF: Right anterior zone pressure value 603 P_RH: Right rear zone 604 pressure value Total pressure P_total = P_LF + P_LH + P_RF + P_RH Processing unit 5 calculates the following pressure ratios at a fixed frequency (e.g., 50 Hz): Physical meaning of the regional calculation formula Left front region LF_ratio = P_LF / P_total × 100% Left frontal load-bearing ratio Left rear region LH_ratio = P_LH / P_total × 100% Left rearward load-bearing ratio Right front region RF_ratio = P_RF / P_total × 100% Right front load-bearing ratio Right rear region RH_ratio = P_RH / P_total × 100% Right rearward load-bearing ratio Front region F_ratio = (P_LF + P_RF) / P_total × 100% Overall forward load-bearing ratio Rear region H_ratio = (P_LH + P_RH) / P_total × 100% Overall rearward load-bearing ratio Left side region L_ratio = (P_LF + P_LH) / P_total × 100% (Overall load-bearing ratio of the left side) R_ratio of the right-side region = (P_RF + P_RH) / P_total × 100% (Overall load-bearing ratio of the right side) Simultaneously, based on clinical standards (left-right balance 50:50, front-back balance 40:60), the center of gravity coordinates are defined as follows: • X-axis (left-right direction): 0-100, where 0 represents the far left and 100 represents the far right. The ideal balance point is 50. X = R_ratio • Y-axis (front-to-back direction): 0-100, where 0 represents the foremost point and 100 represents the last point. The ideal equilibrium point is 40. Y = H_ratio The processing unit 5 displays the calculated pressure ratios of the eight zones and the coordinates of the center of gravity on the output unit 2 in real time and stores them in the local memory.

[0034] Example 3: Static control training based on center of gravity position like Figure 1 Figure 2 Figure 3 As shown in the figure, this embodiment details the specific process of the static center of gravity control training mode.

[0035] Training goal setting: The therapist accesses the training settings interface through output unit 2 and selects a target point in a two-dimensional coordinate system. For example, for a user whose center of gravity is shifted to the right, this can be achieved through... Figure 2 The parameters, such as the target lateral pressure value shown in 302 and the target longitudinal pressure value shown in 303, are finely set, and the target point (60, 40) is selected. Settings Figure 2 The hold duration shown in section 401 (e.g., 10 seconds) is set. Figure 2 The number of training iterations shown in Figure 201 (e.g., 3 times) is set. Figure 2 The rest duration shown in section 501 (e.g., 5 seconds).

[0036] Training execution: 1. The user stands on the pressure detection unit 6 and looks at the output unit 2. Two cursors are displayed on the output unit 2: one cursor represents the user's real-time center of gravity position, and the other cursor represents the target point.

[0037] 2. The system begins monitoring the user's center of gravity position. When the real-time center of gravity cursor enters the error range centered on the target point and within a preset radius, the system starts timing and provides a prompt message.

[0038] 3. During the holding process, the system calculates the instantaneous deviation between the real-time center of gravity cursor and the target point in real time. If the cursor deviates out of the error range, the timer pauses and displays a deviation message, resuming timing only after the cursor re-enters the error range.

[0039] 4. After successfully holding the preset time, the system will indicate that the session is complete and record the completion status of this training session.

[0040] 5. Repeat the above process to complete the preset number of training iterations. The system will then generate a training report.

[0041] Example 4: Dynamic tracking training based on center of gravity position like Figure 1 Figure 2 Figure 4 As shown in the figure, this embodiment details the specific process of the center of gravity dynamic tracking training mode.

[0042] Training goal setting: The therapist accesses the trajectory drawing interface through output unit 2 and sequentially selects multiple points in the two-dimensional coordinate system. For example... Figure 2 As shown in Figure 102, set the number of target points (e.g., 3 target points). The system will automatically connect these points in sequence to form a continuous movement trajectory. Through... Figure 4 The movement speed setting shown in section 601 adjusts the cursor's movement speed within the trajectory, and as shown in... Figure 2 The parameters, such as the number of training iterations, are shown in Figure 201.

[0043] Training execution: 1. The user stands on the pressure detection unit 6 and looks at the output unit 2. Two cursors are displayed on the output unit 2: one cursor represents the user's real-time center of gravity position, and the other cursor represents the target point to be moved along a preset trajectory.

[0044] 2. The target point begins to move along the preset trajectory. The user needs to adjust their body posture to make the real-time center of gravity cursor follow the target cursor as closely as possible.

[0045] 3. The system calculates the distance between the two cursors in real time and displays the current tracking deviation.

[0046] 4. After completing the preset number of loops, the system generates a dynamic tracking report.

[0047] Example 5: Static Control Training Based on Regional Pressure Characteristics like Figure 1 Figure 2 As shown in the figure, this embodiment details the specific process of the regional static control training mode.

[0048] Training goal setting: The therapist selects one of eight areas as the target area and sets a target pressure ratio for that area (e.g., ...). Figure 2 As shown in 301), the duration of holding (as shown in 301) Figure 2 As shown in Figure 401), the allowable error range ε, and the number of training iterations (as shown in Figure 401). Figure 2 As shown in Figure 201), rest duration (such as...) Figure 2 (As shown in 501).

[0049] Training execution: 1. The user stands on the pressure detection unit 6 and looks at the output unit 2. The output unit 2 displays the current pressure ratio of the target area in real time.

[0050] 2. The system begins monitoring the pressure in the target area. When the pressure ratio falls within the range of ±ε of the target value, the system starts timing and provides a prompt message.

[0051] 3. During the holding period, the system calculates the instantaneous deviation between the actual pressure ratio and the target value in real time. If the pressure ratio deviates from the allowable range, the timing pauses and resumes upon re-entry.

[0052] 4. After successfully holding the preset time, the system will indicate that the session is complete and record the completion status of this training session.

[0053] 5. Repeat the above process to complete the preset number of training sessions.

[0054] Example 6: Dynamic Tracking Training Based on Regional Pressure Features (Optional Implementation) In an extended implementation, the user can set a dynamically changing target pressure curve for a selected area. The system displays the target curve and the user's real-time pressure curve in real time and calculates the goodness of fit between the two curves to evaluate the user's dynamic pressure regulation capability. Alternatively, the user can set two or more target pressure values ​​for the area, forming a trajectory connecting the target pressure values. By controlling the pressure changes in the area, the real-time pressure ratio cursor follows the target pressure value cursor along the trajectory formed by the target pressure values.

[0055] Training execution: 1. The patient stands on the pressure detection unit 6 and looks at the output unit 2. The output unit 2 displays two curves or two cursors simultaneously: the target pressure curve / cursor and the real-time pressure curve / cursor.

[0056] 2. The target curve begins to change over time. The patient needs to adjust the pressure in this area to make the real-time pressure curve fit the target curve as closely as possible.

[0057] 3. The system calculates the instantaneous deviation between the two in real time and displays the current degree of fit.

[0058] 4. After completing the preset number of iterations, the system generates a curve fitting report.

[0059] Example 7: Specific Implementation of the Intelligent Collection Mechanism Reference Figure 5 This embodiment details the workflow of the intelligent collection mechanism.

[0060] Automatic Favorites: The system sets a scoring threshold (e.g., 80 points) for each training item. After each training session, the system calculates the evaluation result for that session. If the score is below the threshold, the system automatically saves the training item to the favorites library and indicates the reason for saving it (e.g., "Left forefoot static control - score below threshold - needs reinforcement").

[0061] Manually add to favorites: Users can manually save the current training project to their favorites library through the interface during or after training, and can add text notes (such as "This training is effective and can be used again").

[0062] Collections Management: The system sorts items in the collection by collection time or rating, making it easier for users to find what they need.

[0063] Training call: In subsequent training sessions, users can directly select items from their favorites library for training. The system supports batch selection of multiple items, automatically assembling a training course, and allows users to drag and drop to adjust the order.

Claims

1. A balance ability testing and training system, characterized in that, include: The pressure detection unit includes four pressure detection zones, which correspond to the left front, left rear, right front, and right rear regions when a person is standing; each pressure detection zone includes at least one pressure sensor. A data acquisition unit is connected to the four pressure detection zones and is used to acquire pressure data from each pressure detection zone. The processing unit, connected to the data acquisition unit, is configured to generate training-related information based on the stress data; An output unit, connected to the processing unit, is used to output the training-related information to the user.

2. The system according to claim 1, characterized in that, The processing unit is further configured to: Based on the pressure data from the four pressure detection zones, calculate at least one of the following pressure ratios: • Pressure ratio of the left anterior region, pressure ratio of the left posterior region, pressure ratio of the right anterior region, and pressure ratio of the right posterior region; • Pressure ratio of the anterior region and pressure ratio of the posterior region; • Pressure ratio of the left region and pressure ratio of the right region.

3. The system according to claim 1, characterized in that, The processing unit is further configured to: Based on the pressure data from the four pressure detection zones, the location of the human body's center of gravity is determined.

4. The system according to claim 3, characterized in that, The processing unit is further configured to: Obtain at least one target location; Training feedback information is generated based on the relationship between the center of gravity position and the target position.

5. The system according to claim 2, characterized in that, The processing unit is further configured to: Obtain the target pressure characteristics for at least one pressure detection zone; Training feedback information is generated based on the relationship between the pressure feature value and the target pressure feature.

6. The system according to claim 4, characterized in that, The target location includes any of the following: • At least one fixed location; • A movement trajectory formed by connecting multiple location points.

7. The system according to claim 5, characterized in that, The target pressure characteristic includes any one of the following: • Fixed pressure value or pressure ratio; • The changing pressure curve.

8. The system according to claim 1, characterized in that, Also includes: At least one auxiliary pressure detection unit is deployed at an auxiliary support part that the user may use for leverage, for detecting the contact pressure between the user and the auxiliary support part; The processing unit is also configured to determine whether the user is in an independent standing state based on the comparison result of the contact pressure and a preset threshold.

9. The system according to claim 8, characterized in that, The processing unit is further configured to: The system simultaneously records the determination results of the independent standing state and the stress data during the training process to generate an evaluation index that reflects the user's ability to stand independently.

10. The system according to claim 4 or 5, characterized in that, The processing unit is further configured to: Based on the evaluation results of each training session, automatically identify training items with scores below a preset threshold; The training items with scores below a preset threshold are stored in the collection library; In subsequent training, the training items in the collection will be used first for users to practice.

11. The system according to claim 10, characterized in that, The processing unit is further configured to: Receive manual collection instructions from users and save the training items specified by users into the collection library.

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