A method and system for assisting stroke patients to get up based on multi-scenario body position changes

Abstract

The application provides a stroke elderly person helping-up control method and system based on multi-scene body position conversion. In the application, firstly, a helping-up device is controlled to obtain fusion perception results composed of body pressure distribution data and joint motion data, and multi-feature coupling analysis is performed to generate joint identification conclusions containing body position states and scene types. Then, dynamic mode mapping is performed according to the joint identification conclusions, and a target control mode matched with the current scene type and body position is output. Finally, power instruction synthesis is performed by using the target control mode, and a cooperative control signal for driving the helping-up device to perform body position conversion is generated. The technical scheme provided by the application not only realizes accurate conversion from standardized control to individualized control, improves the adaptability and user experience of the helping-up process, but also overcomes the uncertainty and individual differences in actual operation, and improves the stability, safety and adaptability of the body position conversion process.

Description

Technical Field

[0001] This application relates to the field of medical assistive device control technology, and in particular to a method and system for assisting stroke patients to get up based on multi-scenario body position changes. Background Technology

[0002] Because stroke patients often experience hemiplegia, abnormal muscle tone, and decreased postural control due to damage to the central nervous system, the process of changing positions from lying down to sitting and from sitting to standing is extremely difficult. There is an urgent need for an intelligent assistive technology that can replace or assist manual care.

[0003] Currently, a targeted solution is a lifting aid device based on mechanical transmission and a flip-up plate structure. It uses a servo motor to drive a transmission wheel assembly, causing the flip-up plate to rotate upwards to provide back support. Simultaneously, a retractable support belt provides auxiliary pulling force for the patient to stand up. This device allows patients to actively exert force to complete the standing motion within a certain range, or rely entirely on mechanical support for passive lifting, aiming to balance standing assistance with light motor function training.

[0004] However, these mechanical assisted-up devices still have significant drawbacks. Their control process relies heavily on preset mechanical programs and lacks the ability to dynamically perceive and intelligently decide on the patient's real-time position, muscle tone changes, movement intentions, and the surrounding environment (such as a bed or wheelchair). This results in rigid standing patterns that are difficult to flexibly adjust according to individual differences and actual needs, limiting the patient's active participation and training benefits, and failing to effectively avoid the risk of secondary injury caused by improper posture or discomfort from assistance. Summary of the Invention

[0005] This application provides a method and system for assisting stroke patients to get up based on multi-scenario position changes, in order to solve the problems of rigid control modes and lack of dynamic adaptation to the real-time status and multi-scenario changes of existing assisting devices.

[0006] Firstly, this application provides a method for assisted standing control in stroke patients based on multi-scenario positional transitions, including:

[0007] Multiple sensors pre-positioned on the contact surface of the assistive device for the target person are controlled to simultaneously perceive and obtain a fusion perception result composed of body pressure distribution data and joint motion data.

[0008] Based on the fusion perception results, multi-feature coupling analysis is performed to generate a joint recognition conclusion that includes body position and scene type;

[0009] Based on the joint recognition conclusion, dynamic pattern mapping is performed, and a target control mode matching the current scene type and body position is output.

[0010] The target control mode is used to synthesize power commands to generate a coordinated control signal that drives the lifting device to perform body position conversion.

[0011] Optionally, multiple sensors pre-positioned on the contact surface of the assistive device on the target person are controlled to simultaneously perceive, in order to obtain a fused perception result consisting of body pressure distribution data and joint motion data, including:

[0012] Raw pressure distribution data is collected by a pressure sensor array that is evenly distributed on the support surface of the lifting device.

[0013] Raw joint motion data is collected by motion sensing units fixed to the joints of the target person's limbs.

[0014] The raw pressure distribution data is converted into continuous trajectory information reflecting the displacement of the pressure center;

[0015] The raw joint motion data is converted into a sequence of information describing the changes in joint flexion and extension angles.

[0016] The continuous trajectory information and the changing sequence information are integrated by temporal correlation to obtain a fusion perception result that represents the user's body posture characteristics.

[0017] Optionally, based on the fused perception results, multi-feature coupling analysis is performed to generate a joint recognition conclusion including body position and scene type, including:

[0018] Pressure distribution features and motion pattern features are extracted from the fused perception results;

[0019] The pressure distribution characteristics are matched with predefined pressure distribution patterns to obtain preliminary body position judgment results;

[0020] The motion pattern features are matched with a predefined motion pattern sequence to obtain the auxiliary posture judgment result;

[0021] Based on the combination of the preliminary body position judgment results and the auxiliary body position judgment results, the final body position state is determined;

[0022] Based on the contact characteristics between the final body position and the equipment support structure of the assistive device, the current scene type of the target person is identified;

[0023] The final body position and the current scene type of the target person are combined to form a joint identification conclusion.

[0024] Optionally, dynamic pattern mapping is performed based on the joint recognition conclusion to output a target control pattern that matches the current scene type and body position, including:

[0025] The current scene type and the current body position of the target person are extracted from the joint identification results;

[0026] The current scene type is matched with the scene classification in the pre-stored start-up mode library to determine the candidate control mode set;

[0027] The current body position is matched and filtered with the body position requirements in the candidate control mode set, and an adaptive control mode is generated based on the matching and filtering results.

[0028] Based on the adaptive control mode and the user's historical operation records, the target control mode is determined.

[0029] Optionally, based on the adaptive control mode and the user's historical operation records, a target control mode is determined, including:

[0030] Extract preference data, including assisted start speed, angle adjustment, and pause interval, from the user's historical operation records;

[0031] The preference data is converted into a control parameter adjustment vector;

[0032] The basic parameters of the adaptive control mode are combined with the control parameter adjustment vector for calculation.

[0033] Based on the combined calculation results, a target control mode that meets the user's personalized needs is generated.

[0034] Optionally, the target control mode is used to synthesize power commands to generate coordinated control signals that drive the assisted walking device to perform body position changes, including:

[0035] The action sequence parameters of each power execution unit are parsed from the target control mode;

[0036] Generate a basic drive instruction set based on the action sequence parameters;

[0037] The basic driver instruction set is converted into multi-execution unit cooperative instructions with timing correlation through a motion coordination algorithm;

[0038] The multi-execution unit collaborative instructions are adjusted in real time based on the current status feedback of the device to obtain the adjusted multi-execution unit collaborative instructions.

[0039] Based on the adjusted multi-execution unit cooperative instructions, the cooperative control signal containing speed control, angle control, and force control is generated.

[0040] Optionally, the multi-execution unit collaborative instructions are adjusted in real time based on the current status feedback of the device to obtain adjusted multi-execution unit collaborative instructions, including:

[0041] The operating parameters and load parameters of each power actuator in the lifting device are obtained in real time through the status monitoring module on the lifting device.

[0042] The operating parameters and the load parameters are compared with the expected parameters in the multi-execution unit cooperative instructions to obtain the difference comparison results.

[0043] The parameter adjustment amount for each power actuator is determined based on the difference comparison results;

[0044] The parameter adjustment amount is applied to the multi-execution unit cooperative instruction to generate the adjusted multi-execution unit cooperative instruction.

[0045] Secondly, this application provides a multi-scenario posture transition-based assistive lifting control system for stroke patients, including:

[0046] The control module is used to control multiple sensors pre-placed on the contact surface of the assistive device for the target person to synchronously sense and obtain a fusion sensing result composed of body pressure distribution data and joint motion data.

[0047] The coupling analysis module is used to perform multi-feature coupling analysis based on the fused perception results and generate a joint recognition conclusion that includes body position and scene type.

[0048] The pattern mapping module is used to perform dynamic pattern mapping based on the joint recognition conclusion and output a target control pattern that matches the current scene type and body position.

[0049] The instruction synthesis module is used to synthesize power instructions using the target control mode to generate a coordinated control signal that drives the lifting device to perform body position conversion.

[0050] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are to be invoked and executed by the processing component to implement the stroke-assisted standing control method based on multi-scenario body position transformation as described in the first aspect above.

[0051] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a stroke-assisted standing control method based on multi-scenario body position transformation as described in the first aspect.

[0052] This application acquires fused data through simultaneous sensing by multiple sensors, accurately identifies body position and scene type by combining multi-feature coupling analysis, generates a control mode highly matched to the current scene and body position using dynamic pattern mapping, and finally achieves coordinated control through power command synthesis. This solution can adapt to various assisted standing scenarios such as beds, wheelchairs, and chairs, and provides personalized body position transition assistance based on the real-time physical condition of stroke patients, effectively improving the safety and adaptability of the assisted standing process.

[0053] Furthermore, by monitoring the operating status of each power actuator in real time, the actual parameters are compared with the expected commands, and the control parameters are dynamically adjusted to ensure that the assisted lifting device maintains coordinated operation of each actuator throughout the position transition process. This real-time adjustment mechanism based on status feedback can effectively cope with changes in equipment load and fluctuations in patient position, preventing overload or under-assistance situations and significantly improving the stability and safety of the assisted lifting process.

[0054] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 A flowchart of a stroke-assisted standing control method based on multi-scenario body position transformation provided in this application is shown;

[0057] Figure 2 This paper presents a schematic diagram of a stroke-assisted standing control system based on multi-scenario body position transformation provided in this application.

[0058] Figure 3 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0059] To enable those skilled in the art to better understand the present application, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0060] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0061] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0062] Figure 1 This application provides a flowchart of a method for controlling assisted standing in stroke patients based on multi-scenario positional transitions, such as... Figure 1 As shown, the method includes:

[0063] Step 101: Control multiple sensors pre-positioned on the contact surface of the assistive device on the target person to synchronously sense, so as to obtain a fusion sensing result composed of body pressure distribution data and joint motion data.

[0064] Optionally, step 101 may specifically include the following steps:

[0065] Step 1011: Collect raw pressure distribution data by a pressure sensing array that is evenly distributed on the supporting surface of the lifting device;

[0066] Step 1012: Collect raw joint motion data through motion sensing units fixed to the joints of the target person's limbs;

[0067] Step 1013: Convert the raw pressure distribution data into continuous trajectory information reflecting the displacement of the pressure center;

[0068] Step 1014: Convert the raw joint motion data into sequence information describing the changes in joint flexion and extension angles;

[0069] Step 1015: Integrate the continuous trajectory information and the change sequence information through temporal correlation to obtain a fusion perception result that represents the user's body posture characteristics.

[0070] In the above scheme, the contact surface of the assistive device refers to the part of the device that directly contacts the user's body to provide support. Its surface is uniformly covered with a pressure sensor array to collect raw pressure distribution data. This data reflects the magnitude and distribution of pressure exerted on the contact surface by various parts of the user's body. Body pressure distribution data is obtained by analyzing the raw pressure distribution data and describes the overall pressure distribution characteristics of the user's body on the assistive device. Joint motion data describes the motion state of the user's limb joints, obtained by converting raw joint motion data collected by motion sensing units fixed at the limb joints. The fusion perception result is a comprehensive set of data characterizing the user's postural features, formed by integrating continuous trajectory information reflecting the displacement of the pressure center and sequence information describing changes in joint flexion and extension angles through a temporal correlation method.

[0071] In this scheme, firstly, step 1011 collects raw pressure distribution data through a pressure sensing array uniformly distributed on the support surface of the assistive device. This array continuously measures the pressure value applied by the user's body to the device surface. Secondly, step 1012 collects raw joint motion data through motion sensing units fixed to the joints of the target person's limbs. These units track the joint's angle and direction of movement. Next, step 1013 converts the raw pressure distribution data into continuous trajectory information reflecting the displacement of the pressure center. This process describes the dynamic changes of the pressure center by analyzing the movement path of the pressure points. Then, step 1014 converts the raw joint motion data into a sequence of changes in joint flexion and extension angles by parsing continuous data points of joint movement to form an angle change sequence. Finally, step 1015 integrates the continuous trajectory information and the change sequence information through temporal correlation, aligning the movement of the pressure center and the changes in joint angles on the time axis, thereby forming a fusion perception result that comprehensively characterizes the user's body posture.

[0072] For example, in the application scenario of the assisted standing device in Rehabilitation Center A, a pressure-sensing array is laid on the device's support surface. When a stroke-stricken elderly person uses the device, the array continuously collects raw data on the pressure distribution on their back and buttocks. Simultaneously, motion sensing units installed at the elderly person's knee and hip joints synchronously collect raw data on joint movement. After processing, the pressure distribution data is converted into continuous trajectory information reflecting the movement path of the pressure center from a sitting posture to standing, while the joint movement data is converted into a sequence of changes recording the flexion and extension angles of the knee and hip joints. After integrating the two types of information through a temporal correlation algorithm, the system generates a fusion perception result that accurately reflects the changes in the elderly person's posture during standing, providing data support for subsequent assisted standing control.

[0073] This solution achieves comprehensive perception of the user's body posture characteristics by collaboratively collecting pressure and joint motion data and converting it into time-correlated trajectory and sequence information. This provides a high-precision and synchronous multimodal data foundation for assistive walking control, ensuring the accuracy of user status judgment and the effectiveness of control strategies in subsequent stages.

[0074] Step 102: Based on the fusion perception results, perform multi-feature coupling analysis to generate a joint recognition conclusion that includes body position and scene type.

[0075] Optionally, step 102 may specifically include the following steps:

[0076] Step 1021: Extract pressure distribution features and motion pattern features from the fused perception results;

[0077] Step 1022: Match the pressure distribution characteristics with a predefined pressure distribution pattern to obtain a preliminary body position judgment result;

[0078] Step 1023: Match the motion pattern features with a predefined motion pattern sequence to obtain the auxiliary posture judgment result;

[0079] Step 1024: Determine the final body position state based on the combination relationship between the preliminary body position judgment result and the auxiliary body position judgment result;

[0080] Step 1025: Based on the contact characteristics between the final body position and the equipment support structure of the assistive device, identify the current scene type of the target person;

[0081] Step 1026: Combine the final body position and the current scene type of the target person to form a joint identification conclusion.

[0082] In the above scheme, multi-feature coupling analysis refers to the joint processing of pressure distribution features and motion pattern features from the fused perception results to identify the user's postural state and scene type. Postural state describes the user's body posture category (e.g., lying, sitting, or standing), and scene type refers to the support environment in which the assistive device is currently located (e.g., bed, wheelchair, or chair). The joint identification conclusion is a judgment result formed by combining the final postural state and the current scene type. Pressure distribution features are data extracted from the fused perception results that reflect the body's pressure distribution pattern, while motion pattern features are data that describe the laws of joint movement. The preliminary postural judgment result is an initial postural classification obtained by matching pressure distribution features with predefined pressure distribution patterns. The predefined pressure distribution patterns are a pre-stored set of pressure distribution patterns, and the predefined motion pattern sequences are a pre-stored set of typical joint movement patterns. The auxiliary postural judgment result is an auxiliary classification obtained by matching motion pattern features with predefined motion pattern sequences. The final postural state is the precise postural classification determined by combining the preliminary postural judgment result and the auxiliary postural judgment result. The contact characteristics of the equipment support structure describe the contact method between the lifting device and the support surface (such as a mattress or wheelchair cushion), and are used to distinguish the scene type.

[0083] In this scheme, firstly, pressure distribution features and motion pattern features are separated from the fused perception results via step 1021. Pressure distribution features reflect the pressure intensity distribution of various parts of the body, while motion pattern features describe the temporal pattern of joint angle changes. Secondly, step 1022 compares the pressure distribution features with a pre-stored pressure distribution pattern library, and obtains a preliminary posture judgment result (e.g., identified as "sitting") by calculating similarity. Next, step 1023 matches the motion pattern features with a predefined motion pattern sequence, and obtains an auxiliary posture judgment result (e.g., identified as "transitioning to standing") through a sequence alignment algorithm. Then, step 1024 performs a weighted fusion of the preliminary posture judgment result and the auxiliary posture judgment result, and determines the final posture state (e.g., "transitioning from sitting to standing") according to predefined decision rules (e.g., majority voting or confidence weighting). Subsequently, step 1025 analyzes the contact features of the equipment support structure (e.g., pressure center location and support surface shape) based on the final posture state, matches it with a predefined scene template (e.g., bed scenes typically have a large area of ​​uniform pressure distribution), and identifies the current scene type (e.g., "wheelchair scene"). Finally, the final body position and the current scene type are combined using 1026 to form a joint recognition conclusion (such as "sitting position in a wheelchair scene").

[0084] Following the specific implementation of the previous solution, in the application scenario of the assisted standing device in Rehabilitation Center A, the system extracts pressure distribution features (showing pressure concentrated in the buttocks and thighs) and movement pattern features (showing slow extension patterns of the knee and hip joints) from the fused perception results. The pressure distribution features highly match the pre-stored "sitting" pattern, generating a preliminary posture judgment result of "sitting". The movement pattern features match the predefined "transition from sitting to standing" sequence, generating an assisted posture judgment result of "standing transition". The system integrates the two judgment results and determines the final posture state as "transitioning from sitting to standing". By analyzing the contact features of the device support structure (finding that the pressure distribution is rectangular and has movement wheel markings), the current scene type is identified as a "wheelchair scene". Finally, a joint recognition conclusion of "sitting to standing transition state in wheelchair scene" is generated, providing accurate input for subsequent assisted standing control.

[0085] This solution achieves high-precision identification of body position and scene type through multi-feature coupling analysis, reduces the possibility of misjudgment through a dual verification mechanism of pressure and motion features, and enhances scene differentiation ability through contact feature analysis of equipment support structure. The final joint identification conclusion provides a comprehensive and accurate environmental perception basis for subsequent assisted walking control.

[0086] Step 103: Perform dynamic pattern mapping based on the joint identification conclusion, and output a target control mode that matches the current scene type and body position.

[0087] Optionally, step 103 may specifically include the following steps:

[0088] Step 1031: Extract the current scene type and the current body position of the target person from the joint identification conclusion;

[0089] Step 1032: Match the current scene type with the scene classification in the pre-stored start-up mode library to determine the candidate control mode set;

[0090] Step 1033: Match and filter the current body position state with the body position requirements in the candidate control mode set, and generate an adaptive control mode based on the matching and filtering results;

[0091] Step 1034: Determine the target control mode based on the adaptive control mode and the user's historical operation records and their preference characteristics.

[0092] Step 1034 may specifically include the following steps:

[0093] Extract preference data containing start-up speed, angle adjustment, and pause interval from the user's historical operation records; convert the preference data into a control parameter adjustment vector; perform a combination operation on the basic parameters of the adaptive control mode and the control parameter adjustment vector; and generate a target control mode that meets the user's personalized needs based on the combination operation result.

[0094] In the above scheme, dynamic pattern mapping refers to the process of matching and generating a control strategy suitable for the current situation from a pre-stored assisted-up pattern library based on the scene type and body position status in the joint identification conclusion. The target control pattern is the final set of specific control instructions used to drive the assisted-up device to perform body position changes. The pre-stored assisted-up pattern library contains standardized control pattern templates for different scenes (such as beds, wheelchairs, chairs) and body positions (such as lying, sitting, standing). The candidate control pattern set consists of multiple potentially applicable control pattern groups initially screened through scene classification matching. The adaptive control pattern is a control pattern that is closer to the current body position obtained after further matching and screening from the candidate set according to body position requirements. The user's historical operation record preference characteristics reflect the user's personalized usage habits for parameters such as assisted-up speed, angle adjustment, and pause interval. Assisted-up speed refers to the range of standing speeds preferred by the user in historical operations; angle adjustment refers to the user's preference for adjusting the angles of back support, leg lifting, etc.; and pause interval refers to the user's preferred pause length during body position changes. The control parameter adjustment vector is a set of parameter adjustment instructions formed by quantifying these preference data. The base parameters are the preset standard control parameters in the adaptive control mode.

[0095] In this scheme, firstly, step 1031 separates the current scene type (e.g., wheelchair scene) and the target person's current posture (e.g., transitioning from sitting to standing) from the joint identification results. Secondly, step 1032 matches the current scene type with scene categories in a pre-stored assisted-up mode library (e.g., matching the wheelchair scene with wheelchair-related scene patterns in the library), filtering out all potentially applicable modes to form a candidate control mode set. Next, step 1033 matches the current posture with the posture requirements of each mode in the candidate control mode set (e.g., matching the state transitioning from sitting to standing with the posture transition requirements in the candidate modes), eliminating mismatched modes to generate an adapted control mode. Finally, step 1034 extracts preference data for assisted-up speed, angle adjustment, and pause intervals from the user's historical operation records, converts this preference data into control parameter adjustment vectors (e.g., quantifying preference data into speed increase coefficients, angle adjustment coefficients, etc.), combines the basic parameters of the adapted control mode with the control parameter adjustment vectors (e.g., multiplying the basic speed parameter by the speed adjustment coefficient), and generates a target control mode that meets the user's personalized needs based on the calculation results.

[0096] Following the specific implementation of the previous solution, in the application scenario of the assisted standing device in Rehabilitation Center A, the system parses the current scenario type as a wheelchair scenario from the joint identification results, and the current body position is transitioning from sitting to standing. The system matches the wheelchair scenario with the scenario categories in the pre-stored assisted standing mode library, and filters out all control modes suitable for the wheelchair scenario to form a candidate control mode set. Then, it matches the transition from sitting to standing state with the body position requirements of each mode in the candidate set, and filters out the adaptive control mode specifically designed for the transition from sitting to standing in the wheelchair scenario. The system then extracts user B's preference data from the historical operation records, showing a preference for faster assisted standing speed, smaller back angle adjustment, and shorter pauses, and converts this data into control parameter adjustment vectors (e.g., speed coefficient 1.2, angle coefficient 0.9, pause time 0.5 seconds). This vector is combined with the basic parameters of the adaptive control mode to generate the target control mode that meets user B's personalized needs.

[0097] This solution achieves a precise transition from standardized to personalized control through dynamic pattern mapping. It ensures the basic applicability of the control mode through dual matching of scenarios and body positions. By introducing user historical preference data, the control parameters are further refined, so that the generated target control mode not only meets the requirements of the current environment but also satisfies the user's personalized needs, significantly improving the adaptability of the assisted lifting process and the user experience.

[0098] Step 104: Use the target control mode to synthesize power commands and generate a coordinated control signal to drive the lifting device to perform body position conversion.

[0099] Optionally, step 104 may specifically include the following steps:

[0100] Step 1041: Parse the action sequence parameters of each power execution unit from the target control mode;

[0101] Step 1042: Generate a basic drive instruction set based on the action sequence parameters;

[0102] Step 1043: The basic driving instruction set is converted into multi-execution unit cooperative instructions with timing correlation through a motion coordination algorithm;

[0103] Step 1044: Adjust the multi-execution unit collaborative instructions in real time according to the current status feedback of the device to obtain the adjusted multi-execution unit collaborative instructions;

[0104] Step 1044 may specifically include the following steps:

[0105] The operating parameters and load parameters of each power actuator in the lifting device are acquired in real time by the status monitoring module of the lifting device; the operating parameters and load parameters are compared with the expected parameters in the multi-execution unit collaborative instruction to obtain the difference comparison result; the parameter adjustment amount of each power actuator is determined according to the difference comparison result; the parameter adjustment amount is applied to the multi-execution unit collaborative instruction to generate the adjusted multi-execution unit collaborative instruction.

[0106] Step 1045: Generate the coordinated control signal, which includes speed control, angle control, and force control, based on the adjusted multi-execution unit coordinated instruction.

[0107] In the above scheme, power command synthesis refers to the process of generating specific drive commands based on the target control mode. The coordinated control signal is the final output set of control commands used to coordinate multiple execution units to complete body position conversion. Each power execution unit refers to an independent component (such as a motor, hydraulic cylinder, etc.) in the assistive device responsible for generating assistive motion. The motion sequence parameters describe the sequence, amplitude, and time requirements of each execution unit's actions. The basic drive command set is a preliminary control command group converted from the motion sequence parameters. The motion coordination algorithm is a processing logic used to convert basic commands into commands that coordinate multiple execution units in time. The multi-execution unit coordinated command is a set of control commands with precise timing relationships obtained after processing by the motion coordination algorithm. The adjusted multi-execution unit coordinated command is the final command after correcting the coordinated command based on real-time status feedback. The status monitoring module is a sensing unit used to collect the operating status of the equipment. The operating parameters and load parameters of each power actuator reflect the actual motion state and the force / torque data borne by the actuator, respectively. The expected parameters are the ideal operating values ​​preset in the cooperative instructions. The difference comparison results are the deviation analysis results between the actual parameters and the expected values. The parameter adjustment amount is the numerical amount that needs to be corrected based on the difference.

[0108] In this scheme, firstly, step 1041 extracts the motion sequence parameters of each power actuator from the target control mode. These parameters specify the motion sequence, travel range, and timing requirements of each actuator. Secondly, step 1042 converts the motion sequence parameters into a specific basic drive instruction set. This conversion process transforms abstract motion parameters into electrical signals or digital commands that can directly drive the actuators. Next, step 1043 uses a motion coordination algorithm to process the basic drive instruction set. The algorithm establishes a spatiotemporal relationship model between actuators, integrating independent instructions into multi-actuator collaborative instructions with precise timing coordination, ensuring that the actions of each unit are synchronized and conflict-free. Then, step 1044 utilizes a status monitoring module to collect the operating parameters (such as speed and position) and load parameters (such as pressure and torque) of each power actuator in real time. These actual parameters are compared with the expected parameters in the multi-actuator collaborative instructions to obtain the difference comparison results. Based on the difference results, the parameter adjustment amount required for each actuator is calculated, and these adjustment amounts are applied to the collaborative instructions to generate the adjusted multi-actuator collaborative instructions. Finally, the 1045 generates a coordinated control signal containing speed control (adjusting the speed of movement), angle control (adjusting the joint angle), and force control (adjusting the output force / torque) based on the adjusted multi-execution unit coordinated instructions. This signal can directly drive the assistive device to perform body position conversion.

[0109] Following on from the previous specific implementation, in the application scenario of the assisted standing device in Rehabilitation Center A, the system parses the motion sequence parameters (including extension angle, movement speed, and force requirements) of the back support unit and leg lifting unit from the target control mode. These parameters are then converted into a basic drive instruction set to control the motor speed and stroke. Through motion coordination algorithms, multi-execution unit coordinated instructions are generated to ensure synchronized back and leg movements (e.g., when the back is raised 30 degrees, the legs are simultaneously raised 15 degrees). The status monitoring module collects real-time data showing that the actual pressure value of the back support unit is lower than expected, while the movement speed of the leg unit is higher than expected. The system calculates the parameter adjustments needed to increase the back output force and decrease the leg speed. After applying these adjustments, adjusted multi-execution unit coordinated instructions are generated, ultimately outputting a coordinated control signal containing appropriate speed, angle, and force control, enabling the device to smoothly complete the posture transition from sitting to standing.

[0110] This solution transforms abstract control modes into precise and executable drive signals through a power command synthesis and real-time adjustment mechanism. It ensures the synchronization and coordination of the actions of multiple execution units through a motion coordination algorithm and overcomes the uncertainties and individual differences in actual operation through state feedback adjustment. The resulting collaborative control signal has multiple control dimensions of speed, angle and force, which significantly improves the smoothness, safety and adaptability of the body position transition process.

[0111] Figure 2This application provides a structural schematic diagram of a multi-scenario posture transition-based assisted-up control system for stroke patients, as shown below. Figure 2 As shown, the system includes:

[0112] Control module 21 is used to control multiple sensors pre-arranged on the contact surface of the assistive device for the target person to synchronously sense, so as to obtain a fusion sensing result composed of body pressure distribution data and joint motion data.

[0113] The coupling analysis module 22 is used to perform multi-feature coupling analysis based on the fused perception results to generate a joint recognition conclusion that includes body position and scene type;

[0114] The pattern mapping module 23 is used to perform dynamic pattern mapping based on the joint recognition conclusion and output a target control pattern that matches the current scene type and body position.

[0115] The instruction synthesis module 24 is used to synthesize power instructions using the target control mode to generate a coordinated control signal that drives the lifting device to perform body position conversion.

[0116] Figure 2 The aforementioned multi-scenario posture transition-based stroke patient assisted standing control system can perform... Figure 1 The implementation principle and technical effects of the stroke-assisted standing control method based on multi-scenario body position transformation described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the stroke-assisted standing control system based on multi-scenario body position transformation in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0117] In one possible design, Figure 2 The stroke-assisted lifting control system for elderly patients based on multi-scenario posture transformation, as shown in the embodiment, can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0118] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0119] The processing component 32 is used for the above Figure 1 The embodiment describes a method for assisting stroke patients to get up based on multi-scenario positional changes.

[0120] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0121] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0122] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0123] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0124] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0125] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0126] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown is a method for assisting stroke patients to get up based on multi-scenario body position transformation.

[0127] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0128] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0129] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A stroke elderly person assist rise control method based on multi-scene body position conversion, characterized by, The method comprises the following steps: controlling a plurality of sensors pre-arranged on the contact surface of the assistive device of the target person to synchronously perceive to obtain a fusion perception result composed of body pressure distribution data and joint motion data, including: collecting pressure distribution raw data through a pressure sensing array uniformly distributed on the support surface of the assistive device; collecting joint motion raw data through a motion sensing unit fixed at the joint of the limb of the target person; converting the pressure distribution raw data into continuous trajectory information reflecting the displacement of the pressure center; converting the joint motion raw data into change sequence information describing the angle of joint flexion and extension; integrating the continuous trajectory information and the change sequence information through time sequence correlation to obtain a fusion perception result representing the body posture characteristics of the user; performing multi-feature coupling analysis based on the fusion perception result to generate a joint recognition conclusion containing body position state and scene type, including: extracting pressure distribution features and motion pattern features from the fusion perception result; matching the pressure distribution features with predefined pressure distribution patterns to obtain a preliminary body position judgment result; matching the motion pattern features with predefined motion pattern sequences to obtain an auxiliary body position judgment result; determining the final body position state according to the combination relationship of the preliminary body position judgment result and the auxiliary body position judgment result; identifying the current scene type in which the target person is located based on the final body position state and the contact features of the device support structure of the assistive device; combining the final body position state and the current scene type in which the target person is located to form a joint recognition conclusion; performing dynamic mode mapping according to the joint recognition conclusion to output a target control mode matching the current scene type and body position, including: analyzing the current scene type and the current body position state of the target person from the joint recognition conclusion; matching the current scene type with the scene classification in the pre-stored assistive mode library to determine a candidate control mode set; matching the current body position state with the body position requirements in the candidate control mode set for screening, and generating an adaptive control mode according to the screening result; determining the target control mode based on the adaptive control mode and the preference features of the user historical operation record; performing power instruction synthesis using the target control mode to generate a cooperative control signal for driving the assistive device to perform body position conversion.

2. The method of claim 1, wherein, Determining the target control mode based on the adaptive control mode and the preference features of the user historical operation record, including: extracting preference data containing assistive speed, angle adjustment and pause interval from the user historical operation record; converting the preference data into a control parameter adjustment vector; combining the basic parameters of the adaptive control mode with the control parameter adjustment vector for combined operation; generating a target control mode meeting the individual needs of the user according to the combined operation result.

3. The method of claim 1, wherein, Performing power instruction synthesis using the target control mode to generate a cooperative control signal for driving the assistive device to perform body position conversion, including: analyzing the action sequence parameters of each power execution unit from the target control mode; generating a basic driving instruction set according to the action sequence parameters; The basic driving instruction set is converted into a multi-execution unit cooperative instruction with timing correlation through a motion coordination algorithm; The multi-execution unit cooperative instruction is adjusted in real time according to the current state feedback of the device to obtain an adjusted multi-execution unit cooperative instruction; The cooperative control signal containing speed control, angle control and force control is generated according to the adjusted multi-execution unit cooperative instruction.

4. The method of claim 3, wherein, The multi-execution unit cooperative instruction is adjusted in real time according to the current state feedback of the device to obtain an adjusted multi-execution unit cooperative instruction, including: The running parameters and load parameters of each power execution mechanism in the assistive device are obtained in real time through a state monitoring module on the assistive device; The running parameters and load parameters are compared with the expected parameters in the multi-execution unit cooperative instruction to obtain a difference comparison result; The parameter adjustment amount of each power execution mechanism is determined according to the difference comparison result; The parameter adjustment amount is applied to the multi-execution unit cooperative instruction to generate an adjusted multi-execution unit cooperative instruction.

5. A stroke elderly person assist rise control system based on multi-scene body position conversion, applied to the stroke elderly person assist rise control method based on multi-scene body position conversion in any one of claims 1-4, characterized in that, It includes: The control module is used for controlling a plurality of sensors arranged in advance on the contact surface of the assistive device of the target person to perform synchronous sensing to obtain a fusion sensing result composed of body pressure distribution data and joint motion data; The coupling analysis module is used for performing multi-feature coupling analysis based on the fusion sensing result to generate a joint identification conclusion containing body position state and scene type; The mode mapping module is used for dynamically mapping the target control mode according to the joint identification conclusion to output a target control mode matched with the current scene type and body position; The instruction synthesis module is used for synthesizing dynamic instructions using the target control mode to generate a cooperative control signal for driving the assistive device to perform body position conversion.

6. A computing device, comprising: It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to realize the stroke old people assistive control method based on multi-scene body position conversion according to any one of claims 1-4.

7. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the stroke old people assistive control method based on multi-scene body position conversion according to any one of claims 1-4 is realized.

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