Rehabilitation exercise detection system and method

Abstract

The invention discloses a rehabilitation exercise detection system and method. A rehabilitation exercise detection system comprises a base, a supporting frame, a seat, sensors corresponding to detection parts and data processing equipment. Pressure sensors are respectively arranged on the base and the seat; the data processing equipment is in communication connection with the sensors and the pressure sensors corresponding to the detection parts; the data processing equipment is used for acquiring first detection data of each detection part of the target object in a balance detection posture and keeping duration of the target object in the balance detection posture; determining a balance evaluation result of the target object in the balance detection attitude based on the first detection data and the retention duration; the data processing equipment is further used for acquiring second detection data of each detection part and pressure data of the pressure sensor on the base or the seat in the balance training process of the target object; and generating balance training result data based on the second detection data and the pressure data, thereby realizing balance detection and training of the target object.

Description

Technical Field

[0001] The present invention relates to the technical field of rehabilitation exercise, and particularly relates to a rehabilitation exercise detection system and method. Background Art

[0002] With the development and improvement of computer hardware technology and software systems, rehabilitation exercise detection plays an increasingly important role in the field of rehabilitation medicine. In the prior art, through the evaluation and training of the limb functions of patients, the rehabilitation training of the limb functions of patients is realized, but the detection and evaluation of the balance ability of patients are lacking. Summary of the Invention

[0003] The present invention provides a rehabilitation exercise detection system and method to solve the lack of detection and evaluation of the balance ability of patients in the prior art.

[0004] According to one aspect of the present invention, a rehabilitation exercise detection system is provided, including: a base, a support frame, a seat, sensors corresponding to each detection part, and a data processing device; pressure sensors are respectively arranged on the base and the seat; the data processing device is respectively communicatively connected with the sensors corresponding to each detection part and the pressure sensors;

[0005] The data processing device is configured to: obtain first detection data of each detection part of a target object in a balance detection posture, and the holding duration of the target object in the balance detection posture, wherein the balance detection posture includes one or more of a sitting posture and a standing posture; each balance detection posture corresponds to multiple detection parts, and sensors are respectively worn on each detection part of the target object;

[0006] Determine a balance evaluation result of the target object in the balance detection posture based on the first detection data and the holding duration of each detection part in the balance detection posture; the balance evaluation result is used to indicate the balance training strategy of the target object;

[0007] And, the data processing device is further configured to: during the balance training process of the target object, obtain second detection data of each detection part and pressure data of the pressure sensor on the base or the seat;

[0008] Generate balance training result data based on the second detection data of each detection part and the pressure data, and the balance training result data includes one or more of the following: the trajectory line of each detection part, the pressure center of gravity trajectory line, and the left - right pressure ratio change curve.

[0009] According to another aspect of the present invention, a rehabilitation exercise detection method is provided, including:

[0010] Obtain the first detection data of each detection part of the target object in the balance detection posture, and the holding duration of the target object in the balance detection posture, where the balance detection posture includes one or more of the sitting posture and the standing posture; each balance detection posture corresponds to multiple detection parts, and sensors are respectively worn on each detection part of the target object;

[0011] Determine the balance evaluation result of the target object in the balance detection posture based on the first detection data of each detection part and the holding duration in the balance detection posture; the balance evaluation result is used to indicate the balance training strategy of the target object;

[0012] The method further includes:

[0013] During the balance training process of the target object, obtain the second detection data of each detection part and the pressure data of the pressure sensor on the base or the seat;

[0014] Generate balance training result data based on the second detection data of each detection part and the pressure data, and the balance training result data includes one or more of the following: the trajectory line of each detection part, the pressure center of gravity trajectory line, and the left - right pressure ratio change curve.

[0015] The technical solution of the embodiment of the present invention, by obtaining the first detection data of each detection part of the target object in the balance detection posture, and the holding duration of the target object in the balance detection posture, where the balance detection posture includes one or more of the sitting posture and the standing posture, each balance detection posture corresponds to multiple detection parts, and sensors are respectively worn on each detection part of the target object, provides data support for subsequent detection and analysis; by determining the balance evaluation result of the target object in the balance detection posture based on the first detection data of each detection part and the holding duration in the balance detection posture, and the balance evaluation result is used to indicate the balance training strategy of the target object, improves the accuracy of the balance evaluation result; during the balance training process of the target object, obtain the second detection data of each detection part and the pressure data of the pressure sensor on the base or the seat, provides data support for subsequent training and analysis; by generating balance training result data based on the second detection data of each detection part and the pressure data, and the balance training result data includes one or more of the following: the trajectory line of each detection part, the pressure center of gravity trajectory line, and the left - right pressure ratio change curve, realizes the balance detection and training of the target object.

[0016] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Brief Description of the Drawings

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0018] Figure 1 It is a schematic structural diagram of a rehabilitation exercise detection system provided in Embodiment 1 of the present invention;

[0019] Figure 2 It is a schematic diagram of a safety harness provided in the embodiments of the present invention;

[0020] Figure 3 It is a schematic diagram of a balance training report provided in the embodiments of the present invention;

[0021] Figure 4 It is a schematic diagram of a limb training strategy provided in the embodiments of the present invention;

[0022] Figure 5 It is a schematic diagram of an upper limb training report provided in the embodiments of the present invention;

[0023] Figure 6 It is a schematic diagram of a lower limb training report provided in the embodiments of the present invention;

[0024] Figure 7 It is a schematic structural diagram of a rehabilitation exercise detection system provided in Embodiment 2 of the present invention;

[0025] Figure 8 It is a flowchart of a rehabilitation exercise detection method provided in Embodiment 3 of the present invention. Detailed implementation manners

[0026] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment 1

[0029] Figure 1 It is a schematic structural diagram of a rehabilitation exercise detection system provided by Embodiment 1 of the present invention. This embodiment is applicable to the situation of balance detection and training of a target object. As Figure 1 shown, the rehabilitation exercise detection system includes: a base 110, a support frame 120, a seat 130, sensors 140 corresponding to each detection part, and a data processing device 150; pressure sensors are respectively arranged on the base 110 and the seat 120; the data processing device 150 is respectively communicatively connected with the sensors 140 corresponding to each detection part and the pressure sensors; the data processing device 150 is configured to: obtain first detection data of each detection part of the target object in the balance detection posture, and the holding duration of the target object in the balance detection posture, wherein the balance detection posture includes one or more of a sitting posture and a standing posture; each balance detection posture corresponds to multiple detection parts, and sensors are respectively worn on each detection part of the target object; determine the balance evaluation result of the target object in the balance detection posture based on the first detection data and the holding duration of each detection part in the balance detection posture; the balance evaluation result is used to indicate the balance training strategy of the target object; and, the data processing device 150 is further configured to: during the balance training process of the target object, obtain second detection data of each detection part and the pressure data of the pressure sensor on the base or the seat; generate balance training result data based on the second detection data and the pressure data of each detection part, and the balance training result data includes one or more of the following: the trajectory line of each detection part, the pressure center of gravity trajectory line, and the left and right pressure ratio change curve.

[0030] In this embodiment, the connection relationship between the base 110, the support frame 120, and the seat 130 is not limited. The support frame 120 can be provided on the base 110 or can be independent of the base 110. The seat 130 can be fixed to the support frame 120 or can be independent of the support frame 120. The connection relationship between the base 110, the support frame 120, and the seat 130 can be set according to requirements and is not limited here.

[0031] In this embodiment, the data processing device 150 is communicatively connected to the sensors 140 corresponding to each detection part and the pressure sensor respectively, for example, through Ethernet communication. During the balance detection of the target object, the balance detection postures include one or more of the sitting posture and the standing posture. The detection parts corresponding to the standing posture include the hip, knee, hand, and head. The detection parts corresponding to the sitting posture include the hip, chest, hand, and head. The base 110 is a device for assisting the target object to achieve the standing posture. A pressure sensor is provided on the base 110, which can collect the pressure data of the target object in the standing posture. Optionally, two pressure sensors are provided on the base 110, which are respectively used to collect the first pressure data corresponding to the left foot and the right foot of the target object in the standing posture. Among them, the first pressure data may include the pressure value. The support frame 120 is a device for assisting the target object to perform rehabilitation movement detection. The target object can complete the corresponding actions with the help of the support frame 120. For example, the target object can complete the standing action corresponding to the standing posture and the sitting and standing action corresponding to the sitting posture with the help of the support frame 120. The seat 130 is a device for assisting the target object to achieve the sitting posture. A pressure sensor is provided on the seat 130, which can collect the pressure data of the target object in the sitting posture. Optionally, two pressure sensors are provided on the seat 130, which are respectively used to collect the second pressure data corresponding to the left side and the right side of the target object's body in the sitting posture. The seat 130 can be fixed on the support frame 120 or independent of the support frame 120, which can be set according to requirements and is not limited here. Among them, the second pressure data may include the pressure value. The sensors 140 corresponding to each detection part include but are not limited to pressure sensors and position sensors. The position sensor includes a 6 Degrees of Freedom (DoF) sensor. The 6 DoF sensor can accurately measure the omnidirectional motion state of the target object in space, covering three translational degrees of freedom (movement along the X, Y, and Z axes) and three rotational degrees of freedom (rotation around the X, Y, and Z axes). The 6 DoF sensor integrates an accelerometer and a gyroscope. Among them, the accelerometer can measure the acceleration in the X, Y, and Z axis directions. These acceleration data can be deduced through operations such as integration to obtain the speed change of the target object in space, and then the position information can be obtained. The gyroscope obtains the angle data of each detection part of the target object by integrating the angular velocities of the X, Y, and Z axes. The data processing device 150 is a device for processing the data collected by the sensors 140 corresponding to each detection part, including but not limited to computers, mobile phones, processors, and servers.

[0032] In the process of balance assessment of the target object, it includes standing balance assessment and sitting balance assessment. The first detection data is the data reflecting the motion states of each detection part when the target object is in the balance detection posture. For example, it can include the position information and angle information of each detection part, and can be collected by the sensors 140 corresponding to each detection part. The holding duration of the target object in the balance detection posture is the time when the target object maintains the corresponding action during the balance detection process, including one or more of the holding duration of the target object in the sitting posture and the holding duration of the target object in the standing posture. The holding duration can be obtained by timing with a timer. Taking the holding duration in the standing posture as an example, the moment when the target object starts to stand is taken as the starting moment, and the moment when the target object completes the standing action is taken as the ending moment. By calculating the difference between the starting moment and the ending moment, the holding duration of the target object in the standing posture can be obtained.

[0033] The balance assessment result is information used to characterize the balance level of the target object in the balance detection posture, including but not limited to forms such as scores and grades. The balance assessment result includes one or more of the balance assessment results in the standing posture and the balance assessment results in the sitting posture. The balance assessment result can be determined by the first detection data and the holding duration of each detection part in the balance detection posture. Taking the balance assessment result in the sitting posture as an example, the balance assessment result in the sitting posture can be based on the position information and angle information in the first detection data, as well as the holding duration in the sitting posture, to find the corresponding score in the balance assessment result database in the sitting posture, and use the corresponding score as the balance assessment result of the target object in the sitting posture. It is also possible to input the first detection data and the holding duration into a pre-trained balance assessment model to obtain the balance assessment result. The balance assessment model includes but is not limited to a neural network model, which can be set according to requirements and is not restricted here. The balance training strategy is a targeted training plan formulated for the target object based on the balance assessment result to improve the balance ability of the target object. The balance training strategy includes the balance training strategy in the sitting posture and the balance training strategy in the standing posture. The balance training strategy can be determined according to the balance assessment result. Taking the balance training strategy in the standing posture as an example, the balance assessment result in the standing posture can be determined based on whether the target object can stand alone, whether the hand touches the support frame 120, whether the hand and head can perform actions, whether the head shakes and the shaking amplitude. According to the balance assessment result in the standing posture, the balance training strategy in the standing posture is determined. For example, the target object can stand alone, the hand does not touch the support frame 120, can move to stand on an uneven ground, the hand and head cannot complete the execution of actions, the head shakes, the balance assessment result in the standing posture is level 5, and the balance training strategy is to enhance the balance training of the hand and head, including increasing the training duration and training difficulty. During the process of balance assessment of the target object, by obtaining the first detection data of each detection part of the target object in the balance detection posture and the holding duration of the target object in the balance detection posture, it provides data support for subsequent balance assessment; determining the balance assessment result of the target object in the balance detection posture according to the first detection data and the holding duration of each detection part in the balance detection posture improves the accuracy of the balance assessment result and realizes the balance training of the target object.

[0034] During the balance training of the target object, the second detection data reflects the real-time motion state of each detected part during the balance training, including but not limited to the displacement of each detected part, and can be collected by the corresponding sensors 140 of each detected part. The pressure data includes the pressure data of the pressure sensors on the base and the pressure data of the pressure sensors on the seat. Among them, the pressure data of the pressure sensors on the base reflects the pressure situation between the target object and the base when the target object completes the standing balance training action, and can include the magnitude of the pressure; the pressure data of the pressure sensors on the seat reflects the pressure situation between the target object and the seat when the target object completes the sitting and standing balance training action, and can include the magnitude of the pressure.

[0035] The balance training result data is data that reflects the balance ability of each detected part of the target object during the balance training process, and can be generated based on the second detection data and pressure data of each detected part. The balance training result data includes one or more of the trajectory lines of each detected part, the pressure center of gravity trajectory line, and the left and right pressure ratio change curves. Among them, the trajectory line is a curve formed by the change of the moving trajectory corresponding to each detected part over time, and can be determined according to the position information of each detected part. For example, it can be obtained by fitting the position information of each detected part according to time. The trajectory line includes one or more of the head trajectory line and the hip trajectory line. The pressure center of gravity trajectory line is the moving trajectory of the pressure center of gravity of each detected part and can be calculated based on the pressure data. The left and right pressure ratio change curve is data characterizing the balance of the forces on the left and right sides of the target object's body, and can be obtained by calculating the ratio of the pressure data corresponding to the left foot and the right foot of the target object in the standing position, and can also be obtained by calculating the ratio of the pressure data corresponding to the left side and the right side of the target object's body in the sitting position. It can also be obtained by calculating the ratio of the pressure data corresponding to the left foot and the right foot of the target object in the standing position and the ratio of the pressure data corresponding to the left side and the right side of the target object's body in the sitting position respectively, and determining the weights of the ratio of the pressure data corresponding to the left foot and the right foot of the target object in the standing position and the weights of the ratio of the pressure data corresponding to the left side and the right side of the target object's body in the sitting position according to experience, and performing weighted summation.

[0036] During the balance training of the target object, obtaining the second detection data of each detected part and the pressure data of the pressure sensors on the base or the seat provides a data basis for subsequent balance training analysis; generating the balance training result data based on the second detection data and pressure data of each detected part improves the accuracy of generating the balance training result data and realizes the balance training of the target object.

[0037] To ensure the safety of the target object during the balance detection process and the balance training process, the rehabilitation exercise detection system is provided with a safety harness.

[0038] Optionally, the system further includes: a safety harness; the top of the support frame 120 further includes a hook, and the safety harness is hooked on the hook; the safety harness is worn by the target object during the standing posture training to maintain the safety of the target object during the training. Exemplarily, see Figure 2 , Figure 2 which is a schematic diagram of a safety harness provided by an embodiment of the present invention. The target object wears the safety harness to complete corresponding actions during the standing posture training. The safety harness can be hooked on the hook of the support frame 120, maintaining the safety of the target object during the training.

[0039] In some embodiments, the data processing device 150 is further configured to: determine whether the target object needs support and the support intensity in the sitting posture according to the first detection data of the hand; determine whether there is head shaking and the shaking amplitude of the target object in the sitting posture according to the first detection data of the head; determine whether there is trunk shaking and the shaking amplitude of the target object in the sitting posture according to the first detection data of the hip and chest; determine the balance evaluation result of the target object in the sitting posture based on the holding duration, the support intensity of the hand, the shaking amplitude of the head, and the shaking amplitude of the trunk.

[0040] Specifically, during the sitting balance assessment, the detection sites include the hip, chest, hand, and head. Different types of sensors are worn at different detection sites. For example, the sensors worn on the hip and chest can be position sensors, the sensor worn on the hand can be a pressure sensor, and the sensor worn on the head can be an acceleration sensor, which can be set according to requirements and are not limited here. The support strength is the magnitude of the external support force required by the hand to maintain body balance when the target object is in a sitting posture. The larger the value of the support strength, the weaker the ability of the target object's hand to maintain balance, and the stronger the external support is needed to keep the body stable. The support strength can be determined based on the pressure sensor worn on the hand. For example, it is measured by the pressure sensor worn on the hand. The sway amplitude is the degree to which the head of the target object deviates from its normal stable position in the sitting posture. The smaller the sway amplitude, the better the stability of the head, and vice versa. The sway amplitude can be determined based on the acceleration sensor worn on the head. For example, the acceleration information of the head is collected by the acceleration sensor worn on the head, and the Kalman filtering method is used to process the acceleration information of the head to obtain the sway amplitude of the head. The jitter amplitude is the degree of rapid vibration or slight swing of the trunk of the target object in the sitting posture. The smaller the jitter amplitude, the higher the stability of the trunk, and vice versa. The jitter amplitude can be determined based on the position sensors worn on the hip and chest. For example, the corresponding position information is collected by the position sensors worn on the hip and chest, and the jitter amplitude can be obtained by analyzing the position information. For example, the displacement information within a preset time of the position information can be calculated. The larger the displacement value, the larger the jitter amplitude.

[0041] Based on the first detection data of the hand, it can be determined whether the target object needs support and the support strength in the sitting posture. For example, the first detection data of the hand is input into a pre-trained hand balance detection posture model to determine whether the target object needs support and the support strength in the sitting posture. The hand balance detection posture model includes, but is not limited to, a deep learning model, which can be set according to requirements and is not limited here. It can also be determined whether the target object needs support and the support strength in the sitting posture through the pressure sensor worn on the hand. For example, different support strengths corresponding to different pressure values can be preset, and the pressure data of the target object's hand is compared with the preset pressure values, and the support strength corresponding to the pressure value is used as the support strength of the target object in the sitting posture.

[0042] Based on the first detection data of the head, determine whether there is head shaking and the shaking amplitude of the target object in the sitting posture. For example, input the first detection data of the head into a pre-trained head balance detection posture model to determine whether there is head shaking and the shaking amplitude of the target object in the sitting posture. The head balance detection posture model includes, but is not limited to, a deep learning model, which can be set according to requirements and is not restricted here. It is also possible to determine whether there is head shaking and the shaking amplitude of the target object in the sitting posture through an acceleration sensor worn on the head. For example, by collecting the acceleration information of the head and processing the acceleration information using the Kalman filtering method to obtain the head shaking information, and pre-setting the shaking amplitudes corresponding to different shaking information, taking the shaking amplitude corresponding to the shaking information as the shaking amplitude of the target object's head in the sitting posture.

[0043] Based on the first detection data of the hip and chest, it can be determined whether there is trunk shaking and the shaking amplitude of the target object in the sitting posture. For example, input the first detection data of the hip and chest into a pre-trained trunk balance detection posture model to determine whether there is trunk shaking and the shaking amplitude of the target object in the sitting posture. The trunk balance detection posture model includes, but is not limited to, a deep learning model, which can be set according to requirements and is not restricted here. It is also possible to determine whether there is trunk shaking and the shaking amplitude of the target object in the sitting posture through position sensors worn on the hip and chest. For example, collect the position information of the hip and chest, determine the moving displacement of the hip and chest within a preset time, pre-set the shaking amplitudes corresponding to different moving displacements, and take the shaking amplitude corresponding to the moving displacement of the hip and chest within the preset time as the shaking amplitude of the target object's trunk in the sitting posture.

[0044] The balance assessment result of the target object in the sitting posture can be determined based on the holding duration, the support strength of the hand, the head shaking amplitude, and the trunk shaking amplitude. For example, query the scores and corresponding weights corresponding to the holding duration, the support strength of the hand, the head shaking amplitude, and the trunk shaking amplitude, and perform weighted summation to obtain the final score, taking the final score as the balance assessment result of the target object in the sitting posture. Optionally, the balance assessment result of the target object in the sitting posture may also include one or more of the holding duration, the support strength of the hand and the corresponding score, the head shaking amplitude and the corresponding score, the trunk shaking amplitude and the corresponding score, and the final score.

[0045] Determining whether the target object needs support and the support intensity in the sitting posture based on the first detection data of the hand, whether there is head shaking and the shaking amplitude of the head in the sitting posture based on the first detection data of the head, and whether there is trunk shaking and the shaking amplitude of the trunk in the sitting posture based on the first detection data of the hip and chest provide data support for the determination of the balance assessment result; determining the balance assessment result of the target object in the sitting posture according to the holding duration, the support intensity of the hand, the shaking amplitude of the head, and the shaking amplitude of the trunk improves the accuracy of the balance assessment result of the target object in the sitting posture.

[0046] Optionally, the balance assessment result includes a balance assessment level. Among them, the balance assessment level represents the balance ability of the target object in different postures. For example, it can include scores and corresponding levels. The balance assessment level can be set with different levels according to requirements. The balance assessment level can include the sitting balance assessment level and the standing balance assessment level.

[0047] In some embodiments, in the sitting posture, the sitting balance assessment criteria and the corresponding standard sitting balance assessment levels can be preset, the execution of the sitting posture of the target object is collected, the execution of the sitting posture of the target object is compared with the sitting balance assessment criteria, the sitting balance assessment criteria corresponding to the execution of the sitting posture of the target object are determined, and the standard sitting balance assessment level corresponding to the sitting balance assessment criteria is determined as the sitting balance assessment level of the target object. Among them, the execution of the sitting posture includes one or more of the ability to complete the sitting and standing actions, the duration of sitting steadily, and whether the hand touches the support frame 120. Exemplarily, in the sitting posture, the sitting balance assessment level can be divided into the following levels: when the target object cannot complete the sitting and standing actions, the corresponding sitting balance assessment level is level 0; when the target object can sit and stand, but the sitting steady time does not exceed 30 seconds and the hand touches the support frame 120, the corresponding sitting balance assessment level is level 1; when the target object can maintain a stable sitting posture for more than 30 seconds to within 1 minute and the hand touches the support frame 120, the corresponding sitting balance assessment level is level 2; when the target object can sit and stand for more than 1 minute, the hand touches the support frame 120, and there is obvious head shaking, the corresponding sitting balance assessment level is level 3; when the target object can sit and stand for more than 1 minute, the hand does not touch the support frame 120, there is slight head shaking, and there is slight trunk shaking, the corresponding sitting balance assessment level is level 4; when the target object can sit and stand for more than 1 minute, the hand does not touch the support frame 120, there is no head shaking, and there is no trunk shaking, the corresponding sitting balance assessment level is level 5.

[0048] In some embodiments, the data processing device 150 is further configured to: in the standing evaluation stage, determine whether the target object needs support and the support intensity in the standing posture according to the first detection data of the hand; and determine whether there is head shaking and the shaking amplitude of the target object in the sitting posture according to the first detection data of the head; determine the execution information of the set action according to the first detection data of the detection parts during the execution of the set action by each detection part, where the execution information includes one or more of whether the set action is completed, the smoothness of the set action, and the completion speed; and determine the balance evaluation result of the target object in the standing posture based on the hand support intensity and the head shaking amplitude in the standing evaluation stage, and the execution information of each set action in the standing evaluation stage.

[0049] Specifically, the execution information is information representing the execution of the set action by the target object, reflecting the body control ability and the motor function state of the target object during the balance evaluation. The execution information may include one or more of whether the set action is completed, the smoothness of the set action, and the completion speed. Among them, whether the set action is completed can be determined according to the angle change of each detection part. For example, a standard angle data range corresponding to the completion of the corresponding action is preset in advance, the angle data of each detection part is collected by a position sensor, and the angle data of each detection part is compared with the standard angle data. When the angle data of each detection part is within the standard angle data range, it indicates that the target object has completed the set action, otherwise the set action has not been completed. The smoothness of the set action can be determined according to the angle change of each detection part. For example, the angle data of each detection part is collected by a position sensor, and the angle change data of each detection part within a preset time is calculated. When the angle change data changes evenly, it indicates that the smoothness of the set action is good. The completion speed of the set action can be determined according to the completion time of the set action. For example, a standard time range for executing the action is preset in advance, the completion time of the set action of the target object is collected, and the completion time of the set action is compared with the standard time range. When the completion time of the set action is greater than or equal to the standard time range, it indicates that the target object is slow in completing the execution action.

[0050] The balance assessment result of the target object in the standing position can be determined based on the hand support strength, head sway amplitude, and execution information of each set action during the standing assessment stage. For example, query the scores and corresponding weights corresponding to the hand support strength, head sway amplitude, and execution information of each set action, and perform weighted summation to obtain the final score, and use the final score as the balance assessment result of the target object in the sitting position. Optionally, the balance assessment result of the target object in the sitting position may further include one or more of the hand support strength and the corresponding score, the head sway amplitude and the corresponding score, the execution information of each set action and the corresponding score, and the final score. Determining whether the target object needs support and the support strength in the sitting position through the first detection data of the hand, the first detection data of the head to determine whether there is head sway and the sway amplitude in the sitting position of the target object, and the execution information of each set action in the standing assessment stage provides data support for determining the balance assessment result of the target object in the standing position; determining the balance assessment result of the target object in the standing position based on the hand support strength, head sway amplitude, and execution information of each set action in the standing assessment stage improves the accuracy of the balance assessment result of the target object in the sitting position.

[0051] In some embodiments, in the standing position, the standing balance assessment criteria and the corresponding standard standing balance assessment levels can be preset. The execution of the standing posture of the target object is collected, and the execution of the standing posture of the target object is compared with the standing balance assessment criteria to determine the standing balance assessment criteria corresponding to the execution of the standing posture of the target object. The standard standing balance assessment level corresponding to the standing balance assessment criteria is determined as the standing balance assessment level of the target object. Among them, the execution of the standing posture includes one or more of whether the target object can stand alone, whether the hand touches the support frame 120, whether the hand and the head can perform actions, whether the head shakes, and the shaking amplitude. Exemplarily, the standing balance assessment levels can be divided into the following levels: when the target object cannot stand, the corresponding standing balance assessment level is level 1; when the target object cannot stand alone, the hand touches the support frame 120, and the head shakes, the corresponding standing balance assessment level is level 2; when the target object can stand alone, the hand does not touch the support frame 120, cannot move to stand on an uneven ground, the head shakes, and the trunk shakes, the corresponding standing balance assessment level is level 3; when the target object can stand alone, the hand does not touch the support frame 120, can move to stand on an uneven ground, and the hand and the head cannot complete the execution actions, the corresponding standing balance assessment level is level 4; when the target object can stand alone, the hand does not touch the support frame 120, can move to stand on an uneven ground, the hand and the head cannot complete the execution actions, and the head shakes, the corresponding standing balance assessment level is level 5; when the target object can stand alone, the hand does not touch the support frame 120, can move to stand on an uneven ground, the hand and the head cannot complete the execution actions, and the head has a slight shake or no shake, the corresponding standing balance assessment level is level 6.

[0052] Optionally, the data processing device 150 is further configured to: generate a balance training strategy in the balance detection posture according to the balance assessment level in the balance detection posture.

[0053] Specifically, the training parts, training intensity, training items, and training time are determined according to the balance assessment level. For example, matching can be performed in the database according to the balance assessment level. The database stores the training parts, training intensity, training items, and training time corresponding to different balance assessment levels. The training items include single-leg standing, balance beam training, and straight-line walking training. A balance training strategy is generated according to the training parts, training intensity, training items, and training time. Generating a balance training strategy in the balance detection posture according to the balance assessment level in the balance detection posture improves the accuracy of the balance training strategy in the balance detection posture.

[0054] Exemplarily, refer to Figure 3 , Figure 3It is a schematic diagram of a balance training report provided by an embodiment of the present invention. Among them, the balance training report is the balance training result data, including the sitting / standing balance assessment level, head trajectory line, hip trajectory line, center of pressure trajectory, left and right pressure ratio change data, and training data. The training data includes training items, training postures, total number of training times, number of failures, training modes, duration, number of successes, and hit rate.

[0055] In some embodiments, the data processing device 150 is further configured to: determine the center of pressure position at each moment in the standing posture based on the first pressure data corresponding to the left foot and the right foot, and form a center of pressure trajectory line based on the center of pressure position at each moment in the standing posture.

[0056] Specifically, the first pressure data is the pressure data corresponding to the left foot and the right foot of the target object in the standing posture, including the magnitude and position of the pressure. The center of pressure position at each moment in the standing posture can be determined based on the first pressure data corresponding to the left foot and the right foot. For example, compare the magnitudes of the first pressure data corresponding to the left foot and the right foot at the current moment, determine the larger value of the pressure value in the first pressure data at the current moment and the position corresponding to the larger value, and use the position corresponding to the larger value as the center of pressure position at the current moment, and so on, to calculate the center of pressure position at each moment. The center of pressure trajectory line can be obtained based on the center of pressure position at each moment in the standing posture. For example, the center of pressure position at each moment in the standing posture can be fitted to obtain the center of pressure trajectory line in the standing posture. For example, the center of pressure position at each moment in the standing posture can be fitted through drawing software and drawing tools. By determining the center of pressure position at each moment in the standing posture based on the first pressure data corresponding to the left foot and the right foot, the accuracy of determining the center of pressure position at each moment in the standing posture is improved, providing a data basis for subsequent analysis; by forming a center of pressure trajectory line based on the center of pressure position at each moment in the standing posture, the accuracy of the center of pressure trajectory line is improved, and thus the accuracy of the balance training result data is improved.

[0057] In some embodiments, the data processing device 150 is further configured to: determine the center of pressure position at each moment in the sitting posture based on the second pressure data corresponding to the left side of the body and the right side of the body, and form a center of pressure trajectory line based on the center of pressure position at each moment in the sitting posture.

[0058] Specifically, the second pressure data are the pressure data corresponding to the left and right sides of the body of the target object in the sitting position, including the magnitude and position of the pressure. The position of the pressure center of gravity at each moment in the sitting position can be determined according to the second pressure data corresponding to the left and right sides of the body. For example, compare the magnitudes of the second pressure data corresponding to the left side of the body and the second pressure data corresponding to the right side of the body at the current moment, and take the larger value of the second pressure data and the position corresponding to the larger value at the current moment, and use the position corresponding to the larger value as the position of the pressure center of gravity at the current moment, and so on, to calculate the position of the pressure center of gravity at each moment. The pressure center of gravity trajectory line can also be determined according to the positions of the pressure center of gravity at each moment in the sitting position. For example, fitting the positions of the pressure center of gravity at each moment in the sitting position can obtain the pressure center of gravity trajectory line in the sitting position. For example, the positions of the pressure center of gravity at each moment in the sitting position can be fitted by using drawing software and drawing tools. Determining the positions of the pressure center of gravity at each moment in the sitting position according to the second pressure data corresponding to the left and right sides of the body provides a data basis for the subsequent analysis of balance training; forming the pressure center of gravity trajectory line according to the positions of the pressure center of gravity at each moment in the sitting position improves the accuracy of the pressure center of gravity trajectory line, and further improves the accuracy of the balance training result data.

[0059] In order to perform balance training on the limbs of the target object, the limb function of the target object can be evaluated to obtain a targeted limb training strategy.

[0060] Optionally, the data processing device 150 is further configured to: obtain third detection data of each detection part of the target object in the limb detection scenario, determine the limb movement angle based on the third detection data; determine the limb function evaluation result based on the limb movement angle; and the limb function evaluation result is used to indicate the limb training strategy of the target object.

[0061] Specifically, the limb detection scenario includes an upper limb detection scenario and a lower limb detection scenario. Among them, the detection parts in the upper limb detection scenario include the shoulder, elbow, and wrist, and the detection parts in the lower limb detection scenario include the knee and ankle. The third detection data is the position information of each detection part of the target object in the limb detection scenario, which can be collected by a position sensor. The position sensor includes a 6-degree-of-freedom sensor. The limb movement angle reflects the angle change of each detection part of the target object's limb during the rehabilitation movement detection in the limb detection scenario. It can be obtained from the third detection data and can be calculated from the third detection data through a data fusion algorithm. For example, the complementary filter algorithm and the Kalman filter algorithm can be used to calculate the third detection data to obtain the limb movement angle. The limb function evaluation result represents the level of the target object's limb function and can include one or more of the upper limb function evaluation result and the lower limb function evaluation result. For example, it can include the upper limb motor ability and the lower limb motor ability. The limb function evaluation result of the target object can be determined according to the limb movement angle. For example, the method of looking up a table can be used to determine the motor ability level corresponding to the limb movement angle, and the motor ability level is used as the limb function evaluation result. The limb training strategy is a series of targeted training plans and methods formulated to improve and enhance the limb function of the target object, including but not limited to the training part, training intensity, training item, and training time. The limb training strategy can be determined according to the limb function evaluation result and can be determined according to the motor ability level of the limb in the limb function evaluation result. Different levels correspond to different limb training strategies. Since the detection parts of the upper limb and the lower limb are different, even if the motor ability levels are the same, the limb training strategies for the upper limb and the lower limb are also different. Taking the limb training strategy of the upper limb as an example, the limb function evaluation result of the upper limb is level 3, and the limb training strategy of the upper limb is obtained by looking up a preset table. Among them, the limb training strategy of the upper limb includes the training part, training intensity, training item, and training time. The training part is the left hand, the training intensity is medium, the training item is air raid, and the training time is 20 minutes. By evaluating the limb function of the target object in the limb detection scenario, accurate data support is provided for the limb training strategy. Exemplarily, see Figure 4 , Figure 4 is a schematic diagram of a limb training strategy provided by an embodiment of the present invention. Among them, the training item is beach volleyball, the training part is the left hand, single-handed training of the left hand, the training time is 5 minutes, and the training angle can be selected.

[0062] In some embodiments, the upper limb motor ability and the lower limb motor ability can be evaluated according to the joint movement angle ratio, and the specific evaluation is as follows: when the maximum joint movement angle is 0, the corresponding joint movement ability is no movement ability; when the maximum joint movement angle ratio is 1%-10%, the corresponding joint movement ability is weak movement ability; when the maximum joint movement angle ratio is 11%-25%, the corresponding joint movement ability is poor movement ability; when the maximum joint movement angle ratio is 26%-50%, the corresponding joint movement ability is fair movement ability; when the maximum joint movement angle ratio is 51%-75%, the corresponding joint movement ability is good movement ability; when the maximum joint movement angle ratio is 75%-100%, the corresponding joint movement ability is normal movement ability.

[0063] Optionally, the data processing device 150 is further configured to: during the limb training of the target object, obtain the fourth detection data of each detection part, determine the movement angle of the limb joint at each moment based on the fourth detection data; generate limb training result data based on the movement angle of the limb joint at each moment, and the limb training result data includes the movement angle change curve of each limb joint.

[0064] Specifically, the fourth detection data is the position information of each detection part of the target object during the limb training process, which can be collected by a position sensor, and the position sensor includes a 6-degree-of-freedom sensor. The limb training result data is used to characterize the completion of the limb movement of the target object during the limb training process, and may include upper limb training result data and lower limb training result data. The upper limb training result data may include the movement angle change curve of the upper limb joint, and the lower limb training result data may include the movement angle change curve of the lower limb joint. The limb training result data can be generated according to the movement angle of the limb joint at each moment. Exemplarily, the movement angle of each limb joint of the target object at each moment is fitted to obtain the movement angle change curve of each limb joint, and the movement angle change curve of each limb joint is used as the limb training result data. For example, the movement angle change curve of each limb joint can be obtained by fitting the movement angle of each limb joint of the target object at each moment through a drawing software and a drawing tool. By performing limb training on the target object, the limb training result data is obtained, the limb training of the target object is realized, and a basis is provided for adjusting the limb training strategy.

[0065] Exemplarily, refer to Figure 5 and Figure 6 。 Figure 5It is a schematic diagram of an upper limb training report provided by an embodiment of the present invention. The upper limb training report is the upper limb training result data, and the upper limb training report includes the activity ability corresponding to each joint, the change in the shoulder joint flexion angle, the change in the shoulder joint abduction / adduction angle, the change in the elbow joint flexion / extension, the change in the wrist joint flexion / extension, the pressure center of gravity trajectory, the left-right pressure ratio change curve, and the training data. Among them, the training data includes the training item, the training part, the training angle, the number of training times, the training mode, the staying time, the number of failures, the time-consuming, and the angle changes of shoulder flexion, shoulder abduction / adduction, and elbow flexion / extension. Figure 6 It is a schematic diagram of a lower limb training report provided by an embodiment of the present invention. The lower limb training report is the lower limb training result data, and the lower limb training report includes the activity ability corresponding to each joint, the change in the knee joint flexion, the change in the ankle joint flexion, the pressure center of gravity trajectory, the left-right pressure ratio change curve, and the training data. Among them, the training data includes the training item, the training part, the total number of training times, the number of failures, the time-consuming, the training mode, the duration, the number of successes, and the hit rate.

[0066] The technical solution of this embodiment provides data support for subsequent detection and analysis by obtaining the first detection data of each detection part of the target object in the balance detection posture and the holding duration of the target object in the balance detection posture. Among them, the balance detection posture includes one or more of the sitting posture and the standing posture, and each balance detection posture corresponds to multiple detection parts. Sensors are respectively worn on each detection part of the target object; the balance evaluation result of the target object in the balance detection posture is determined by the first detection data and the holding duration of each detection part in the balance detection posture, and the balance evaluation result is used to indicate the balance training strategy of the target object, improving the accuracy of the balance evaluation result; during the balance training process of the target object, the second detection data of each detection part and the pressure data of the pressure sensor on the base or the seat are obtained, providing data support for subsequent training and analysis; the balance training result data is generated by the second detection data and the pressure data of each detection part. The balance training result data includes one or more of the following: the trajectory line of each detection part, the pressure center of gravity trajectory line, and the left-right pressure ratio change curve, realizing the balance detection and training of the target object.

[0067] Embodiment 2

[0068] Figure 7 It is a schematic diagram of the structure of a rehabilitation exercise detection system provided by Embodiment 2 of the present invention. This embodiment is an optimization of the above embodiment. As Figure 7As shown in the figure, the rehabilitation exercise detection system includes: a base 210, a support frame 220, a seat 230, sensors 240 corresponding to each detection part, a data processing device 250, and a VR device 260; pressure sensors are respectively arranged on the base 210 and the seat 220; the data processing device 250 is respectively communicatively connected to the sensors 240 corresponding to each detection part and the pressure sensors; the VR device 260 is communicatively connected to the data processing device 250; the data processing device 250 is configured to: obtain first detection data of each detection part of the target object in the balance detection posture and the holding duration of the target object in the balance detection posture, wherein the balance detection posture includes one or more of the sitting posture and the standing posture; each balance detection posture corresponds to multiple detection parts, and sensors are respectively worn on each detection part of the target object; determine the balance evaluation result of the target object in the balance detection posture based on the first detection data of each detection part and the holding duration in the balance detection posture; the balance evaluation result is used to indicate the balance training strategy of the target object; the data processing device 250 is further configured to: during the balance training process of the target object, obtain second detection data of each detection part and the pressure data of the pressure sensor on the base or the seat; generate balance training result data based on the second detection data of each detection part and the pressure data, and the balance training result data includes one or more of the following: the trajectory line of each detection part, the pressure center of gravity trajectory line, and the left and right pressure ratio change curve; the VR device 260 is configured to: read the balance training strategy from the data processing device 250, and the balance training strategy includes at least one training item; display the VR scene corresponding to at least one training item, and the VR scene includes a first example image, and the first example image performs the actions corresponding to the training item to prompt the target object to perform the actions.

[0069] In this embodiment, a virtual reality (VR) device is a device that uses computer technology to generate a simulated environment and enables a target object to immerse in this environment. The VR device 260 is communicatively connected to the data processing device 250, for example, it can be communicatively connected via Ethernet. In some embodiments, the VR device 260 includes a display device and an input device. Among them, the display device presents a virtual image through a high-definition display screen, and is equipped with sensors such as a gyroscope and an accelerometer to track the movement of the target object and adjust the view angle of the image in real time. The display device includes a head-mounted display device. The input device includes a handle and a joystick, which can accurately capture the hand movements of the target object, realize the natural interaction between the target object and the virtual environment, and realize grasping and manipulating virtual objects. The first example image is the virtual image presented when the VR device 260 shows the VR scene during the balance training process. It can accurately execute the standard actions corresponding to various balance training items, provide action demonstrations for the target object, and the target object can complete the actions corresponding to the training items according to the prompts of the first example image. The first example image can be pre-stored in the VR device 260. The VR device 260 includes a data reading module, which reads the balance training strategy from the data processing device 250, generates a corresponding VR scene according to the training items in the balance training strategy and displays it. The first example image can execute corresponding actions according to the training items to prompt the target object. By showing the VR scene corresponding to the training item through the VR device 260, the target object can execute the actions corresponding to the corresponding training item according to the prompts of the first example image, improving the accuracy of the target object's actions.

[0070] Optionally, the data processing device 150 is further configured to: determine the motion state of the target object at each moment according to the first detection data of each detection part; send the motion state of the target object at each moment to the VR device 260; the VR device 260 shows a second example image in the VR scene, and the second example image shows the motion state of the target object.

[0071] Specifically, the motion state of the target object at each moment reflects the true motion conditions of each detected part of the target object, including but not limited to the position, posture, and motion direction of each detected part. The second example image is the virtual image of the VR device 260 reflecting the motion state of the target object at each moment in the VR scene. The data processing device 150 includes a data sending module, which sends the motion state of the target object at each moment to the VR device 260 through communication. The VR device 260 reads the motion state of the target object at each moment through the data reading module and displays the motion state of the target object through the second example image, realizing the visualization of the motion state of the target object. By displaying the motion state of the target object through the second example image of the VR device 260 in the VR scene, the target object can master the execution of the actions corresponding to the training items according to the second example image, which is beneficial for the target object to adjust the corresponding actions to improve the training effect.

[0072] In the technical solution of this embodiment, by obtaining the first detection data of each detected part of the target object in the balance detection posture and the holding duration of the target object in the balance detection posture, where the balance detection posture includes one or more of the sitting posture and the standing posture, and each balance detection posture corresponds to multiple detected parts, and sensors are respectively worn on each detected part of the target object, data support is provided for subsequent detection and analysis; by determining the balance evaluation result of the target object in the balance detection posture based on the first detection data of each detected part and the holding duration in the balance detection posture, and the balance evaluation result is used to indicate the balance training strategy of the target object, the accuracy of the balance evaluation result is improved; during the balance training process of the target object, the second detection data of each detected part and the pressure data of the pressure sensor on the base or the seat are obtained, providing data support for subsequent training and analysis; by generating balance training result data based on the second detection data of each detected part and the pressure data, the balance training result data includes one or more of the following: the trajectory line of each detected part, the pressure center of gravity trajectory line, and the left-right pressure ratio change curve, realizing the balance detection and training of the target object; by reading the balance training strategy, the balance training strategy includes at least one training item, and a VR scene corresponding to at least one training item is displayed. The VR scene includes a first example image, and the first example image executes the actions corresponding to the training item to prompt the target object to execute the actions, providing an action demonstration for the target object, which is beneficial to improving the accuracy of the target object's execution of the actions.

[0073] Embodiment III

[0074] Figure 8 It is a flowchart of a rehabilitation motion detection method provided by Embodiment III of the present invention. This embodiment is applicable to the situation of balance detection and training of a target object. As Figure 8 shown, the method includes:

[0075] S310. During the balance assessment of the target object, obtain the first detection data of each detection part of the target object in the balance detection posture and the holding duration in the balance detection posture of the target object, where the balance detection posture includes one or more of the sitting posture and the standing posture; each balance detection posture corresponds to multiple detection parts, and sensors are respectively worn on each detection part of the target object.

[0076] Optionally, the detection parts corresponding to the sitting posture include the hip, chest, hand, and head.

[0077] Optionally, the detection parts corresponding to the standing posture include the hip, knee, hand, and head.

[0078] S320. Determine the balance assessment result of the target object in the balance detection posture based on the first detection data and the holding duration of each detection part in the balance detection posture; the balance assessment result is used to indicate the balance training strategy of the target object.

[0079] Optionally, the balance assessment result includes a balance assessment level.

[0080] Optionally, the method further includes: generating a balance training strategy in the balance detection posture according to the balance assessment level in the balance detection posture.

[0081] S330. During the balance training of the target object, obtain the second detection data of each detection part and the pressure data of the pressure sensor on the base or the seat.

[0082] S340. Generate balance training result data based on the second detection data of each detection part and the pressure data, and the balance training result data includes one or more of the following: the trajectory line of each detection part, the pressure center of gravity trajectory line, and the left - right pressure ratio change curve.

[0083] Optionally, the method further includes: collecting the first pressure data corresponding to the left foot and the right foot of the target object in the standing posture and the second pressure data corresponding to the left side and the right side of the body of the target object in the sitting posture; the first pressure data and the second pressure data respectively carry time stamps; determining the pressure center of gravity position at each moment in the standing posture based on the first pressure data corresponding to the left foot and the right foot; or determining the pressure center of gravity position at each moment in the sitting posture based on the second pressure data corresponding to the left side and the right side of the body, and forming a pressure center of gravity trajectory line based on the pressure center of gravity position at each moment in the standing posture or the pressure center of gravity position at each moment in the sitting posture.

[0084] Optionally, the method further includes: determining, according to the first detection data of the hand, whether the target object needs support and the support intensity in the sitting posture; determining, according to the first detection data of the head, whether there is head shaking and the shaking amplitude of the target object in the sitting posture; determining, according to the first detection data of the hip and chest, whether there is trunk shaking and the shaking amplitude of the target object in the sitting posture; and determining the balance evaluation result of the target object in the sitting posture based on the holding duration, the support intensity of the hand, the head shaking amplitude, and the trunk shaking amplitude.

[0085] Optionally, the method further includes: in the standing evaluation stage, determining, according to the first detection data of the hand, whether the target object needs support and the support intensity in the standing posture; and determining, according to the first detection data of the head, whether there is head shaking and the shaking amplitude of the target object in the sitting posture; in the standing evaluation stage, determining the execution information of the set action according to the first detection data of the detection part during the execution of the set action by each detection part, where the execution information includes one or more of whether the set action is completed, the fluency of the set action, and the completion speed; and determining the balance evaluation result of the target object in the standing posture based on the hand support intensity and the head shaking amplitude in the standing evaluation stage, and the execution information of each set action in the standing evaluation stage.

[0086] Optionally, the method further includes: reading a balance training strategy, where the balance training strategy includes at least one training item; and displaying a VR scene corresponding to at least one training item, where the VR scene includes a first example image that performs the action corresponding to the training item to prompt the target object to perform the action.

[0087] Optionally, the method further includes: determining the motion state of the target object at each moment according to the first detection data of each detection part; sending the motion state of the target object at each moment to the VR device; and the VR device displays a second example image in the VR scene, and the second example image displays the motion state of the target object, the motion state of the target object.

[0088] Optionally, the method further includes: obtaining the third detection data of each detection part of the target object in the limb detection scene, and determining the limb activity angle based on the third detection data; determining the limb function evaluation result based on the limb activity angle; and the limb function evaluation result is used to indicate the limb training strategy of the target object.

[0089] Optionally, the method further includes: during the limb training process of the target object, obtaining the fourth detection data of each detection part, and determining the activity angle of the limb joint at each moment based on the fourth detection data; and generating limb training result data based on the activity angle of the limb joint at each moment, where the limb training result data includes the activity angle change curve of each limb joint.

[0090] In the technical solution of this embodiment, by obtaining the first detection data of each detection part of the target object in the balance detection posture and the holding duration of the target object in the balance detection posture, where the balance detection posture includes one or more of the sitting posture and the standing posture, and each balance detection posture corresponds to multiple detection parts, and sensors are respectively worn on each detection part of the target object, it provides data support for subsequent detection and analysis; by determining the balance evaluation result of the target object in the balance detection posture through the first detection data and the holding duration of each detection part in the balance detection posture, the balance evaluation result is used to indicate the balance training strategy of the target object, improving the accuracy of the balance evaluation result; during the balance training process of the target object, obtain the second detection data of each detection part and the pressure data of the pressure sensor on the base or the seat, providing data support for subsequent training and analysis; generate balance training result data through the second detection data of each detection part and the pressure data, and the balance training result data includes one or more of the following: the trajectory line of each detection part, the pressure center of gravity trajectory line, and the left and right pressure ratio change curve, realizing the balance detection and training of the target object.

[0091] It should be understood that various forms of the processes shown above can be used, reordering, adding or deleting steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0092] The above specific embodiments do not constitute a limitation to the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub - combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A rehabilitation movement detection system, characterized in that: include: Base, support frame, seat, sensors and data processing equipment corresponding to each detection part; The base and the seat are respectively provided with pressure sensors; the data processing device is respectively connected to the sensors corresponding to the detection parts and the pressure sensors for communication; The data processing device is used to: during the balance assessment of the target object, obtain first detection data of each detection part of the target object in a balance detection posture, and the holding time of the target object in the balance detection posture, wherein the balance detection posture includes one or more of a sitting posture and a standing posture; each balance detection posture corresponds to multiple detection parts, and each detection part of the target object is respectively equipped with a sensor; Determine a balance evaluation result of the target object in the balance detection posture based on the first detection data of each detection part in the balance detection posture and the holding time; the balance evaluation result is used to indicate a balance training strategy for the target object; Furthermore, the data processing device is further used to: obtain the second detection data of each detection part and the pressure data of the pressure sensor on the base or the seat during the balance training of the target object; Based on the second detection data of each detection part and the pressure data, balance training result data is generated, and the balance training result data includes one or more of the following: trajectory lines of each detection part, pressure center of gravity trajectory lines and left and right pressure ratio change curves.

2. The rehabilitation movement detection system according to claim 1, characterized in that: Two pressure sensors are provided on the base, respectively used to collect first pressure data corresponding to the left foot and the right foot of the target object in the standing posture; The seat is provided with two pressure sensors, which are respectively used to collect second pressure data corresponding to the left side and the right side of the body of the target object in the sitting posture; the first pressure data and the second pressure data respectively carry a time stamp; The data processing device is also used to: determine the pressure center of gravity position at each moment in the standing posture based on the first pressure data corresponding to the left foot and the right foot; or determine the pressure center of gravity position at each moment in the sitting posture based on the second pressure data corresponding to the left side of the torso and the right side of the torso, and form a pressure center of gravity trajectory line based on the pressure center of gravity position at each moment in the standing posture or the pressure center of gravity position at each moment in the sitting posture.

3. The rehabilitation movement detection system according to claim 1, characterized in that: The detection parts corresponding to the sitting posture include hips, chest, hands, and head; The determining, based on the first detection data of each detection part in the balance detection posture and the holding time, a balance evaluation result of the target object in the balance detection posture comprises: determining, according to the first detection data of the hand, whether the target object needs support in the sitting posture and the support strength; Determine, based on the first detection data of the head, whether the head of the target object is shaking in the sitting posture and the shaking amplitude; Determining whether there is shaking of the torso of the target object in the sitting posture and the shaking amplitude according to the first detection data of the hip and the chest; A balance assessment result of the target object in the sitting posture is determined based on the holding time, the support strength of the hands, the shaking amplitude of the head, and the shaking amplitude of the torso.

4. The rehabilitation movement detection system according to claim 1, characterized in that: The detection parts corresponding to the standing posture include hips, knees, hands, and head; The determining, based on the first detection data of each detection part in the balance detection posture and the holding time, a balance evaluation result of the target object in the balance detection posture comprises: In the standing assessment stage, whether the target object needs support and the strength of support in the standing posture is determined based on the first detection data of the hand; and whether the head of the target object shakes and the amplitude of the shaking in the sitting posture is determined based on the first detection data of the head; Determine, according to the first detection data of the detection part during the process of each detection part performing the set action, the execution information of the set action, wherein the execution information includes one or more of whether the set action is completed, the smoothness of the set action and the completion speed; The balance assessment result of the target object in the standing posture is determined based on the hand support strength and the head shaking amplitude in the standing assessment stage, and the execution information of each of the set actions in the standing assessment stage.

5. The rehabilitation movement detection system according to claim 1, characterized in that: The balance assessment result includes a balance assessment grade; The data processing device is further used for generating a balance training strategy under the balance detection posture according to the balance evaluation level under the balance detection posture.

6. The rehabilitation movement detection system according to claim 1, characterized in that: The system further includes a VR device; the VR device is in communication with the data processing device, and reads the balance training strategy from the data processing device, wherein the balance training strategy includes at least one training item; The VR device displays a VR scene corresponding to the at least one training item, wherein the VR scene includes a first example image, and the first example image performs an action corresponding to the training item to prompt the target object to perform the action.

7. The rehabilitation movement detection system according to claim 6, characterized in that: The data processing device is also used for: Determine the motion state of the target object at each moment according to the first detection data of each detection part; send the motion state of the target object at each moment to the VR device; The VR device displays a second example image in the VR scene, and the second example image displays the motion state of the target object.

8. The rehabilitation movement detection system according to claim 1, characterized in that: The system further includes: a safety harness; the top of the support frame further includes a hook, and the safety harness is hung on the hook; The safety harness is worn by the target object during the training of the standing posture to maintain the safety of the target object during the training.

9. The rehabilitation movement detection system according to claim 1, characterized in that: The data processing device is also used for: Acquire third detection data of each detection part of the target object in a limb detection scenario, and determine the limb activity angle based on the third detection data; Determine a limb function assessment result of the target object based on the limb activity angle; the limb function assessment result is used to indicate a limb training strategy for the target object; Furthermore, the data processing device is further used to: during the limb training of the target object, obtain fourth detection data of each detection part, and determine the activity angle of the limb joint at each moment based on the fourth detection data; Limb training result data is generated based on the activity angles of the limb joints at each moment, and the limb training result data includes activity angle change curves of each limb joint.

10. A rehabilitation exercise detection method, characterized in that: include: During the balance assessment of the target object, first detection data of each detection part of the target object in a balance detection posture and the holding time of the target object in the balance detection posture are obtained, wherein the balance detection posture includes one or more of a sitting posture and a standing posture; each balance detection posture corresponds to multiple detection parts, and each detection part of the target object is respectively equipped with a sensor; Determine a balance evaluation result of the target object in the balance detection posture based on the first detection data of each detection part in the balance detection posture and the holding time; the balance evaluation result is used to indicate a balance training strategy for the target object; The method further comprises: During the balance training of the target object, second detection data of each detection part and pressure data of a pressure sensor on a base or a seat are obtained; Based on the second detection data of each detection part and the pressure data, balance training result data is generated, and the balance training result data includes one or more of the following: trajectory lines of each detection part, pressure center of gravity trajectory lines and left and right pressure ratio change curves.

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