Non-walking type gait simulation evaluation system based on virtual reality interaction

Abstract

The invention relates to the technical field of medical data processing, and discloses a non-walking type gait simulation evaluation system based on virtual reality interaction. A virtual reality interaction module is utilized to enable a to-be-tested person to complete a walking simulation gait task cooperatively generated with any one or any combination of a cognitive load task, an emotional load task and a spatial obstacle task in a virtual reality test scene so as to simulate real-time gait changes of the to-be-tested person in different scenes. Efficient and accurate gait evaluation is achieved by combining the data acquisition module and the processor, the safety of a to-be-tested person in the gait evaluation process can be guaranteed, the objectivity of the evaluation process can also be guaranteed, especially under the condition that the to-be-tested person often has frozen gaits, the time and frequency of footstep stagnation or change can be accurately recorded, and the accuracy of the gait evaluation is improved. A doctor is helped to accurately know the occurrence mode of the frozen gait of the Parkinson's patient, and effective data guidance is provided for personalized treatment of the patient.

Description

Technical Field

[0001] This invention relates to the field of medical data processing technology, and in particular to a non-walking gait simulation and evaluation system based on virtual reality interaction. Background Technology

[0002] Parkinson's disease is a neurodegenerative disease with a high incidence and high disability rate. Statistics show that there are over 5 million Parkinson's patients in China, accounting for a significant proportion of the global total, and this number continues to grow rapidly with the aging population. This not only severely impacts patients' quality of life but also poses a significant challenge to the healthcare and social security systems. Therefore, early diagnosis, accurate assessment, and targeted treatment have become core requirements for the clinical management of Parkinson's disease.

[0003] Freezing of gait (FOG) is a typical symptom of Parkinson's disease, characterized by a sudden or persistent difficulty in initiating gait, accompanied by gait arrest and loss of balance. This symptom significantly increases the risk of falls and is a major cause of disability and even death in patients. However, the pathological mechanism of FOG is not fully understood, and its inducing factors and pathogenesis require further investigation. Although the Movement Disorders Society International (MDS) has included FOG in its Parkinson's disease assessment scale, current clinical assessment methods still have significant limitations. Highly subjective and lacking objective standards: Currently, it mainly relies on manual observation and questionnaires, which are easily affected by the experience of the assessors, the subjective descriptions of patients and environmental interference, resulting in poor consistency of results and difficulty in accurate quantification.

[0004] Significant safety risks: Actual walking tests may induce patients to fall, especially for patients with moderate to severe FOG. The testing process carries a high risk, limiting its clinical application.

[0005] Limited assessment coverage: subtle gait abnormalities in patients with mild FOG are difficult to detect using traditional methods, which can delay intervention; while severe patients are limited in their mobility and can only be assessed generally through descriptive records, making it impossible to analyze specific gait characteristics.

[0006] The impact of cognition and emotion cannot be quantified: FOG is often closely related to cognitive load (such as performing dual tasks) and emotional state (such as anxiety), but existing methods make it difficult to introduce specific tasks into the assessment to quantify the role of these factors, which restricts the development of personalized treatment.

[0007] Therefore, there is an urgent need for a new non-walking gait simulation assessment system based on virtual reality interaction to assist doctors in objectively, comprehensively, and accurately assessing Parkinson's disease-induced gait freeze, while improving patient safety during the assessment process. Summary of the Invention

[0008] This invention provides a non-walking gait simulation assessment system based on virtual reality interaction to address the shortcomings of existing Parkinson's disease frozen gait assessment methods, which are not objective enough, have low accuracy, and cannot guarantee the safety of patients during the assessment process.

[0009] This invention provides a non-walking gait simulation and evaluation system based on virtual reality interaction, comprising: A virtual reality interaction module is configured to present a dynamically adjustable virtual reality test scene to the test subject, the virtual reality test scene including gait tasks of different difficulty simulating walking. A static postural support unit, comprising a rigid load-bearing structure and a footplate, is configured to support the patient in a non-walking static posture. The data acquisition module includes one or more motion sensors fixedly mounted on the pedal, configured to acquire non-walking simulated gait data of the subject in real time when the subject responds to the simulated walking gait task and send it to the data processing unit. The data processing unit, which is communicatively connected to the data acquisition module and the virtual reality interaction module, includes a processor and a memory. The processor is configured to execute a computer program stored in the memory. When the processor executes the computer program, it performs the following steps: receiving non-walking simulated gait data of the subject sent by the data acquisition module, and obtaining the gait evaluation result of the subject based on the non-walking simulated gait data of the subject.

[0010] According to the present invention, a non-walking gait simulation assessment system based on virtual reality interaction is provided. The subject is a Parkinson's disease patient, and the gait assessment result is a Parkinson's disease frozen gait assessment result. The gait tasks of different difficulties include gait tasks that occur in conjunction with any one or any combination of the following: cognitive load tasks, emotional load tasks, and spatial obstacle tasks.

[0011] In one embodiment, the gait task may involve having the test subject walk in a virtual reality testing scenario with prompts to simulate a real walking scenario.

[0012] In one embodiment, the gait task in conjunction with the cognitive load task can be to have the test subject perform a gait task in a virtual reality testing scenario while simultaneously engaging in cognitive responses and decision-making, in order to simulate the impact of complex cognitive load on the test subject's gait.

[0013] In one embodiment, the cognitive load task adopts a multi-level cognitive task paradigm, designing progressively more difficult tasks, from a baseline walking task to a complex cognitive-motor integration task.

[0014] In one embodiment, the gait task in conjunction with the emotional load task can be to first increase the spatial height in the virtual reality test scene to simulate the cliff effect, and then have the test subject complete the gait task. Alternatively, situations that can bring emotional stress to the test subject can be added during the process of the test subject completing the gait task to simulate the test subject's gait performance under emotional stress.

[0015] In one embodiment, the emotional load task constructs a multimodal emotion induction system and utilizes virtual reality scene design, physiological signal monitoring, and task parameter regulation to achieve precise application and quantification of emotional stress.

[0016] In one embodiment, the gait task in conjunction with the spatial obstacle task can be to add spatial obstacles such as narrow gates and obstacles to the virtual reality test scene, and then have the test subject complete the gait task to simulate the test subject's gait ability in complex environments, especially in obstacle avoidance and obstacle crossing, which helps to more accurately assess the occurrence and changes of the test subject's frozen gait.

[0017] In one embodiment, the spatial obstacle task employs a parametric environment modeling method, which dynamically generates a three-dimensional spatial structure with precise size control through a virtual reality engine, focusing on two types of parameters: passage restrictions and obstacle layout.

[0018] When a gait task occurs alone, it is a gait task with a difficulty level of 0. When a gait task occurs in conjunction with any one of the cognitive load task, emotional load task, or spatial obstacle task, it is a gait task with a difficulty level of I. When a gait task occurs in conjunction with any two of the cognitive load task, emotional load task, or spatial obstacle task, it is a gait task with a difficulty level of II. When a gait task occurs in conjunction with the cognitive load task, emotional load task, or spatial obstacle task, it is a gait task with a difficulty level of III.

[0019] According to the present invention, a non-walking gait simulation evaluation system based on virtual reality interaction is provided. The non-walking simulated gait data includes any one or any combination of the following: stride time, cadence, stride length, stride speed, gait rhythm, and reaction time to prompts.

[0020] In one embodiment, stride time refers to the time interval between two consecutive foot strikes on one side, cadence refers to the number of steps completed per unit time (which is the reciprocal of stride time), pace refers to the distance traveled per unit time (cadence × stride length), gait rhythm refers to the temporal regularity of the stride sequence (such as the symmetry of left and right step times), and reaction time to cues refers to the length of time from when the cues for the subject to start walking are given until the subject begins walking.

[0021] According to the present invention, a non-walking gait simulation and evaluation system based on virtual reality interaction is provided, wherein obtaining the gait evaluation result of the subject based on the subject's non-walking simulated gait data includes: The dynamic sliding window method was used to analyze the non-walking simulated gait data of the subjects in real time, and the key indicator data of the non-walking simulated gait data in each analysis window were obtained. Based on the key indicator data of each analysis window from the non-walking simulated gait data, combined with the preset evaluation criteria, the first step gait evaluation result of the test subject is obtained.

[0022] According to the present invention, a non-walking gait simulation evaluation system based on virtual reality interaction includes key indicator data including any one of the following indicators or any combination thereof: mode of gait time and its distribution characteristics, mean and standard deviation of gait frequency, coefficient of variation of gait rhythm (coefficient of variation of gait rhythm CV = standard deviation of gait rhythm / mean of gait rhythm × 100%), average reaction time to prompts and its dispersion.

[0023] According to the present invention, a non-walking gait simulation evaluation system based on virtual reality interaction is provided, wherein the preset evaluation criteria include: A. For each analysis window, a window is determined to be a frozen event window if two or more of the following conditions are met simultaneously: The current step time is more than twice the mode of the step time within the analysis window; The coefficient of variation of gait rhythm is greater than 0.4; The current reaction time to the prompt exceeds two standard deviations of the mean reaction time in the analysis window; The current step frequency is less than 60% of the average step frequency in the analysis window; B. When two or more freeze event windows occur consecutively, or when freeze event windows occur multiple times within a preset time period, a continuous freeze gait event is determined to have occurred, and the specific time of the freeze gait event is recorded.

[0024] According to the present invention, a non-walking gait simulation and evaluation system based on virtual reality interaction is provided, wherein obtaining the gait evaluation result of the subject based on the subject's non-walking simulated gait data includes: Based on the non-walking simulated gait data of the test subjects, the dose-response relationship between the difficulty of the cognitive load task and the change in non-walking simulated gait data, the dose-response relationship between the emotional intensity of the emotional load task and the duration of freezing, and the dose-response relationship between the constraint intensity of the spatial obstacle task and the occurrence of freezing were analyzed. By analyzing the dose-response relationship between the difficulty of the cognitive load task and changes in non-walking simulated gait data, the dose-response relationship between the emotional intensity of the emotional load task and the duration of freeze, and the dose-response relationship between the constraint intensity of the spatial obstacle task and the occurrence of freeze, the synergistic effect pattern of multidimensional factors was analyzed using the random forest algorithm to obtain the second gait assessment results of the test subjects.

[0025] According to the present invention, a non-walking gait simulation assessment system based on virtual reality interaction is provided. The second gait assessment result includes any one or any combination of the following: quantitative index data on the severity of freezing events, probability distribution characteristics of freezing under different inducing factors, classification of frozen gait subtypes based on multi-dimensional data analysis, and wherein the frozen gait subtypes include cognitive load-dominated type, emotional stress-induced type, and spatial constraint-sensitive type.

[0026] In one embodiment, a user interaction terminal is further included, which is communicatively connected to the data processing unit, for providing gait assessment results to the user.

[0027] In one embodiment, the pedal may be hinged to the seat for the subject to step on. One or more of the following gait sensing components may be installed on, inside, or at the bottom of the pedal: multiple sensors, such as pressure sensors or sensor arrays, for sensing plantar pressure distribution, the magnitude and timing of vertical ground reaction forces, the trajectory of the pressure center, and the start time and duration of each phase of the gait cycle; an inertial measurement unit for measuring parameters such as the three-dimensional acceleration and angular velocity of the pedal / foot, foot posture, gait speed, stride length, and cadence (in conjunction with pressure parameters); a position / displacement sensor, such as an optical or ultrasonic sensor, for accurately measuring the relative position of the foot on the pedal (e.g., heel-to-toe), etc. In one specific embodiment, a pressure sensor array may be installed at the bottom of the pedal to synchronously acquire the timing of plantar pressure distribution and provide feedback on the pedal's tread depth. In one implementation, an embedded microcontroller or microprocessor may also be installed inside or near the pedal to acquire all sensor data in real time, perform signal preprocessing (e.g., filtering, noise reduction, baseline correction), convert the analog signals sensed by the sensors into digital signals, generate non-walking analog gait data, and send it to the processor of the data processing unit.

[0028] In one embodiment, the system of the present invention may further include a seat or a custom-designed railing, preferably a seat. This allows the subject to complete the gait assessment process while seated or while holding onto the railing.

[0029] In one embodiment, for patients with cognitively driven hyperactivity, a daily living activity (DOA) guidance plan can be provided based on the dose-response relationship between the difficulty of the cognitively driven task and changes in non-walking simulated gait data. For example, mild patients can perform low-complexity dual-task walking, such as walking while performing simple counting or naming exercises, but should avoid walking in complex environments that require rapid decision-making; moderate patients are advised to choose structured walking training, such as rhythmic walking along a fixed route, but sudden interruptions or route changes should be avoided; severe patients are only advised to walk short distances on undisturbed straight paths, and any distracting motor tasks are prohibited.

[0030] In one embodiment, for patients sensitive to spatial constraints, a progressive spatial adaptation training program can be provided based on the analysis results of parameters such as the ratio of passage width to shoulder width and obstacle density. For example, when the passage width / shoulder width ratio is <1.2, it is recommended to first conduct walking training in an open environment; when the ratio is between 1.2 and 1.5, walking in a standard-width passage can be attempted; when the ratio is >1.5, narrow space adaptation training can be conducted. At the same time, an appropriate obstacle avoidance training plan is designed according to the obstacle density threshold.

[0031] In one embodiment, for patients with stress-induced emotional states, targeted psychological intervention recommendations can be provided based on the results of emotional provocation tests. For those sensitive to highly threatening environments, systematic desensitization therapy is recommended; for those sensitive to social evaluation, social scene exposure training is suggested; and for those sensitive to time pressure, relaxation training and time management strategies are provided.

[0032] The data processing unit can communicate with the virtual reality interaction module and the user interaction terminal via wireless or wired means. When connected wirelessly, the data processing unit may include a wireless communication module for establishing a low-power data transmission channel with the data acquisition module, such as using Bluetooth or ZigBee protocols.

[0033] As a user interaction terminal, it can be a mobile terminal or an embedded tablet device, or it can be a hospital's electronic pathology system, used to provide users (doctors or other medical personnel involved in the assessment) with the frozen assessment results and reports of the Parkinson's disease of the test subject. In this invention, the provision includes, but is not limited to, delivery methods such as visual display, electronic document export, physical printing, and network transmission.

[0034] In one implementation, the data processing unit may further include a visualization module for visualizing the Parkinson's disease frozen gait assessment results of the test subject, thereby obtaining a visualized Parkinson's disease frozen gait assessment report. Further, the assessment report includes personalized guidance information based on the test subject's Parkinson's disease frozen gait assessment results, wherein the personalized guidance information may include any one or any combination of the following: gait improvement suggestions, early warning information regarding the occurrence of frozen gait, and task completion status.

[0035] It should be noted that although the visualization module can also be configured on the user interaction terminal, from the perspective of data security and the sensitivity of medical data, it is preferable to configure it in the data processing center (e.g., central server, private cloud architecture data center, etc.).

[0036] In one embodiment, the Parkinson's disease frozen gait assessment report may include any one or any combination of the following: the subject's gait performance under gait tasks of different difficulty, the occurrence of frozen gait, the potential for gait improvement, etc. The report content can provide a reference for the formulation of subsequent treatment plans.

[0037] In one embodiment, the visualization module includes multiple dimensions of data presentation. For severity visualization, a progressive color gradient from light green to dark red represents the trend of symptoms changing from mild to severe, while specific clinical score values ​​and corresponding symptom descriptions are annotated, enabling healthcare professionals to quickly grasp the patient's overall condition. For subtype classification visualization, a three-dimensional coordinate system chart can be generated, with cognitive load, spatial constraints, and emotional stress as the coordinate axes. The distribution of patient data points visually reflects the dominant subtype characteristics, supplemented by textual descriptions of typical symptoms.

[0038] In one embodiment, the visualization module includes a graphical user interface (GUI). For exercise guidance suggestions, the GUI can display dynamically simulated human movement diagrams, annotating the amplitude, frequency, and duration of recommended movements, while simultaneously marking prohibited movements with warning icons. Environmental adaptation suggestions are presented using virtual scene reconstruction technology, allowing adjustment of parameters such as channel width and obstacle density, and visually demonstrating the patient's walking performance in different environmental configurations. The training progress tracking section uses a timeline format, comparing historical assessment data with preset target values ​​and automatically calculating the recommended training intensity for the next stage.

[0039] All visualizations support multi-level information display, allowing users to access more detailed data analysis through clicks or zooming. The visualization module may also include a report generation module, which generates standardized assessment documents containing key charts and data summaries, facilitating clinical archiving and cross-departmental collaboration. The output of the visualization module can be directly linked to the hospital's electronic medical record system, providing intuitive evidence for multidisciplinary teams to develop personalized intervention plans.

[0040] The present invention also provides a gait assessment method based on the non-walking gait simulation assessment system based on virtual reality interaction as described in any one of the above claims, comprising: S1. Load a dynamically adjustable virtual reality test scenario onto the test subject through a virtual reality interaction module. The virtual reality test scenario includes gait tasks of varying difficulty. S2. The data acquisition module collects non-walking simulated gait data of the subject in real time when performing simulated walking gait tasks and transmits it to the data processing unit; S3. The data processing unit receives the non-walking simulated gait data of the test subject sent by the data acquisition module, and obtains the gait evaluation result of the test subject based on the non-walking simulated gait data.

[0041] In a preferred embodiment, the visualization module of the data processing center generates a visualized gait assessment report based on the gait assessment results, and the user interaction terminal receives and displays the assessment report to the user. This invention provides a non-walking gait simulation assessment system based on virtual reality interaction. Utilizing a virtual reality interaction module, the system allows the test subject to complete a simulated walking gait task in a virtual reality testing scenario, co-occurring with any one or any combination of cognitive load tasks, emotional load tasks, and spatial obstacle tasks. This simulates the test subject's real-time gait changes in different scenarios. Combined with a data acquisition module and processor, it achieves efficient and accurate gait assessment, ensuring both the safety of the test subject during the assessment process and the objectivity of the assessment. Especially when the test subject frequently exhibits frozen gait, it can accurately record the time and frequency of foot stagnation or changes, helping doctors accurately understand the occurrence pattern of frozen gait and providing effective data guidance for personalized treatment of patients. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in this invention 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 invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0043] Figure 1This invention provides a structural schematic diagram of a non-walking gait simulation and evaluation system based on virtual reality interaction.

[0044] Figure 2 This invention provides a schematic diagram of a non-walking gait simulation and assessment system based on virtual reality interaction, applied to the assessment of frozen gait in Parkinson's disease. Reference numerals: 1. VR glasses; 2. Static body position support unit; 3. Data acquisition module; 4. Non-walking gait simulation and assessment system based on virtual reality interaction.

[0045] Figure 3 This invention provides a schematic diagram of the process for applying a virtual reality-based interactive non-walking gait simulation assessment system to the assessment of frozen gait in Parkinson's disease. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, embodiments of this invention, and should not be construed as limiting the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. In the description of this invention, it should be understood that the terminology used is for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0047] Figure 1 This invention provides a structural schematic diagram of a non-walking gait simulation and evaluation system based on virtual reality interaction. Figure 2 and 3 The diagrams shown are a structural schematic and a flowchart of a non-walking gait simulation assessment system based on virtual reality interaction provided by the present invention, applied to the frozen gait assessment of Parkinson's disease.

[0048] See Figures 1 to 3 The present invention provides a non-walking gait simulation and evaluation system based on virtual reality interaction, which may include: A. Virtual Reality Interaction Module: The virtual reality interaction module is configured to present a dynamically adjustable virtual reality test scenario to the test subject. The virtual reality test scenario includes gait tasks of varying difficulty that simulate walking. The test subject is a Parkinson's disease patient.

[0049] In this invention, the virtual reality interaction module may include one or more of the following components: a virtual reality head-mounted device, such as a head-mounted display (HMD); a reality scene acquisition and reconstruction system (including a lidar scanner, a multi-view panoramic camera array, etc.); a reality fusion enhancement module (including a high-definition RGB camera); motion capture devices (e.g., optical motion capture systems, inertial motion capture devices, etc.); environmental sensors (e.g., light sensors, sound sensors, etc.); and voice interaction devices (including high-fidelity headphones, microphones). The virtual reality interaction module can be constructed using existing virtual interaction hardware and related software in the art as needed. Figure 2 In one embodiment, the virtual reality interaction module is implemented using VR glasses.

[0050] In one embodiment, gait tasks of varying difficulty include gait tasks that occur in conjunction with any one or any combination of the following: cognitive load tasks, emotional load tasks, and spatial obstacle tasks.

[0051] Among them, the gait task can be to have the test subject walk in a virtual reality test scenario with prompts to simulate a real walking scenario.

[0052] Gait tasks that complement cognitive load tasks can involve subjects performing gait tasks in a virtual reality testing environment while simultaneously engaging in cognitive responses and decision-making, thus simulating the impact of complex cognitive load on the subject's gait. In this embodiment, the cognitive load task adopts a multi-level cognitive task paradigm, designing progressively more challenging tasks, ranging from a baseline walking task to a complex cognitive-motor integration task.

[0053] The gait task coordinated with the emotional load task can be achieved by first increasing the spatial height of the virtual reality test scene to simulate a cliff effect, and then having the test subject complete the gait task. Alternatively, it can involve adding situations that induce emotional stress during the test subject's gait task to simulate the test subject's gait performance under emotional stress. In this embodiment, the emotional load task achieves precise application and quantification of emotional stress by constructing a multimodal emotion induction system and utilizing virtual reality scene design, physiological signal monitoring, and task parameter adjustment.

[0054] Gait tasks that complement spatial obstacle tasks can involve adding spatial obstacles such as narrow gates and obstacles to a virtual reality testing scenario, and then having the test subject complete a gait task to simulate the subject's gait ability in complex environments. This is particularly helpful in more accurately assessing the occurrence and changes of frozen gait during obstacle avoidance and traversal. In this embodiment, the spatial obstacle task employs a parametric environment modeling method, dynamically generating a precisely dimensional three-dimensional spatial structure through a virtual reality engine, focusing on two types of parameters: passage restrictions and obstacle layout.

[0055] In one embodiment, when the gait task occurs alone, the gait task is a gait task with a difficulty level of 0. When the gait task occurs in conjunction with any one of the cognitive load task, emotional load task, and spatial obstacle task, the gait task is a gait task with a difficulty level of I. When the gait task occurs in conjunction with any two of the cognitive load task, emotional load task, and spatial obstacle task, the gait task is a gait task with a difficulty level of II. When the gait task occurs in conjunction with the cognitive load task, emotional load task, and spatial obstacle task, the gait task is a gait task with a difficulty level of III.

[0056] B. Data acquisition module, which is configured to collect the non-walking simulated gait data of the subject in real time when the subject performs a gait task, and send the non-walking simulated gait data of the subject to the processor.

[0057] In one embodiment, non-walking simulated gait data includes any one or any combination of the following: stride time, cadence, stride length, gait speed, gait rhythm, and reaction time to cues. Stride time refers to the time interval between two consecutive foot strikes on one side; cadence refers to the number of steps completed per unit time (the reciprocal of stride time); gait speed refers to the distance traveled per unit time (cadence × stride length); gait rhythm refers to the temporal regularity of the stride sequence (e.g., symmetry of left and right step times); and reaction time to cues refers to the time elapsed from when the cues instructing the subject to begin walking until the subject begins walking.

[0058] C. A data processing unit, communicatively connected to the data acquisition module and the virtual reality interaction module, includes a processor and a memory. The processor is configured to execute a computer program stored in the memory. When the processor executes the computer program, it performs the following steps: receiving non-walking simulated gait data of the subject sent by the data acquisition module, and obtaining the gait assessment result of the subject based on the non-walking simulated gait data of the subject. In this embodiment, it is the gait freeze assessment result for Parkinson's disease.

[0059] In one embodiment, obtaining the gait assessment result of the test subject based on the non-walking simulated gait data of the test subject may include: The dynamic sliding window method is used to analyze the non-walking simulated gait data of the test subjects in real time, and obtain the key indicator data of the non-walking simulated gait data in each analysis window (in this embodiment, the window length is set to 30 seconds and the sliding step length is set to 5 seconds. This parameter configuration can effectively capture the dynamic characteristics of gait cycle changes and meet the requirements of real-time analysis). The key indicator data includes any one of the following indicators or any combination thereof: mode of step time and its distribution characteristics, mean and standard deviation of step frequency, coefficient of variation of gait rhythm (coefficient of variation of gait rhythm CV = standard deviation of gait rhythm / mean of gait rhythm × 100%), average reaction time to prompts and its dispersion. Based on the key indicator data of each analysis window from the non-walking simulated gait data, combined with the preset evaluation criteria, the first step gait evaluation result of the subject is obtained. The preset evaluation criteria determine that a freeze event has occurred when two or more of the following conditions are met simultaneously: The preset evaluation criteria include: I. For each analysis window, a window is determined to be a frozen event window when two or more of the following conditions are met simultaneously: The current step time is more than twice the mode of the step time within the analysis window; The coefficient of variation of gait rhythm is greater than 0.4; The current reaction time to the prompt exceeds two standard deviations of the mean reaction time in the analysis window; The current step frequency is less than 60% of the average step frequency in the analysis window; II. When two or more freeze event windows occur consecutively, or when freeze event windows occur multiple times within a preset time period (e.g., 1 minute), a continuous freeze gait event is determined to have occurred, and the specific time of the freeze gait event is recorded.

[0060] In each analysis window, the so-called "current pace time," "current reaction time to prompts," and "current cadence" values ​​actually refer to the latest data collected in real time within that analysis window. This latest data is compared with historical data within the same window: for example, the "current pace time" is compared with the mode of pace times in that window, and the "current reaction time" is compared with the mean and standard deviation of reaction times in that window. This means that each time a sliding window is analyzed, the newly entered data point is always compared with the historical statistical values ​​within the window.

[0061] For example: Assume that in analysis window 1 (0-30 seconds), the mean cadence is 100 steps / minute, the mode is 0.6 seconds / step, the gait rhythm CV is 0.3, the average response time to cues is 1 second, and the standard deviation is 0.2 seconds. If the latest cadence at the end of the analysis window is 55 steps / minute (60% below the mean) and the latest step time is 1.3 seconds (more than twice the mode), then analysis window 1 meets two freezing conditions, and is therefore marked as a "freeze event window". When the analysis window slides to the next stage (5-35 seconds), the same analysis is performed again. If several consecutive analysis windows show the "freeze event window" marker, a sustained freeze event is determined to have occurred.

[0062] To determine whether a freezing phenomenon has occurred, this embodiment establishes a multi-indicator joint decision-making mechanism. The multi-indicator cross-validation method can significantly improve the accuracy of Parkinson's disease freezing gait assessment compared with the traditional single threshold method.

[0063] In one embodiment, a two-stage analysis method can be used to establish a multi-dimensional task evaluation system: first, the independent effects of each individual dimension are quantified using a generalized linear mixture model; then, by constructing a third-order interaction term of cognition × space × emotion, a random forest algorithm is used to analyze the synergistic effect pattern of multi-dimensional factors. This method achieves three major technological innovations: standardized grading ensures the repeatability of experimental conditions; continuous variable modeling avoids information loss; and explicit analysis of multi-dimensional interactions. The resulting quantitative correlation model not only reveals the independent influence of each parameter but also discovers the synergistic effect of key parameter combinations.

[0064] Specifically, obtaining the gait assessment results of the test subject based on the non-walking simulated gait data of the test subject may include: Based on the non-walking simulated gait data of the test subjects, the dose-response relationship between the difficulty of the cognitive load task and the changes in the non-walking simulated gait data was analyzed (which can be done using machine learning methods), the dose-response relationship between the emotional intensity of the emotional load task and the duration of freeze (which can be done using time series alignment techniques and survival analysis methods), and the dose-response relationship between the constraint intensity of the spatial obstacle task and the occurrence of freeze (which can be analyzed using a mixed-effects model). By analyzing the dose-response relationship between the difficulty of the cognitive load task and changes in non-walking simulated gait data, the dose-response relationship between the emotional intensity of the emotional load task and the duration of freeze, and the dose-response relationship between the constraint intensity of the spatial obstacle task and the occurrence of freeze, the synergistic effect pattern of multidimensional factors was analyzed using the random forest algorithm to obtain the second gait assessment results of the test subjects.

[0065] In one embodiment, the assessment results of frozen gait in Parkinson's disease may include any one or any combination of the following: quantitative indicators of the severity of frozen events (including duration of frozen events (e.g., cumulative duration of a continuous window), frequency of frozen events (number of events per unit time), percentage of frozen events (percentage of frozen duration to total test duration)), probability distribution characteristics of frozen events under different triggering factors, and classification of frozen gait subtypes based on multidimensional data analysis, wherein frozen gait subtypes include cognitive load-dominated, emotional stress-induced, and spatial constraint-sensitive types. These assessment results can provide objective and quantitative scientific evidence for clinical diagnosis and treatment planning, contributing to the realization of personalized precision medicine. In particular, by identifying specific frozen gait subtypes, such as cognitive load-dominated, spatial constraint-sensitive, and emotional stress-induced types, targeted treatment recommendations can be provided to clinicians, significantly improving intervention effectiveness.

[0066] Specifically, the probability distribution characteristics of freezing under different inducing factors and the classification of frozen gait subtypes based on multi-dimensional data analysis can be obtained through the following methods.

[0067] Probability distribution characteristics of freezing under different inducing factors: (a) How to obtain: The following three tasks were tested respectively, and corresponding non-walking simulated gait data were collected in real time: Cognitive load dimension: multi-level cognitive task paradigm, with difficulty increasing step by step; Emotional load dimension: Real-time application of emotional stress through virtual reality scenarios and physiological signal monitoring; Spatial obstacle dimension: obstacles and spatial constraints are dynamically added to the virtual reality environment.

[0068] The collected non-walking simulated gait data were analyzed to determine the relationship between the freezing event and the intensity of each triggering factor.

[0069] (II) Specific Judgment Criteria and Analysis Methods: Analysis is conducted through statistical models or machine learning algorithms, specifically including: Dose-response analysis: For example, using generalized linear mixture models, mixed-effects models, or survival analysis methods, calculate the relationship between the intensity of inducing factors and the incidence of freezing events.

[0070] Analyze each task dimension separately: how the probability of a freeze event (e.g., freeze frequency, duration) changes as task difficulty / intensity increases.

[0071] Finally, the probability distribution curves of the freezing event as the intensity of different inducing factors were obtained.

[0072] Frozen gait subtype classification based on multidimensional data analysis: (a) How to obtain: Based on the completion of single-dimensional dose-response analysis, a multi-dimensional comprehensive analysis is then performed: Phase 1: Using a generalized linear mixture model (GLMM) to identify the independent effects of each triggering factor dimension (cognitive, emotional, spatial barriers); The second stage involves constructing a three-dimensional interaction term encompassing cognition, space, and emotion, and using the Random Forest algorithm to analyze the synergistic effects of multiple factors, thereby further identifying the frozen gait subtypes of patients under the combined influence of different dimensions.

[0073] (II) Specific Judgment Criteria and Analysis Methods: Random Forest Classification Model: Input multidimensional task data and train the classification model.

[0074] The input features of the model may include: cognitive task performance and its correlation with non-walking simulated gait data; emotional stress intensity and corresponding freeze duration; spatial obstacle constraint intensity and freeze frequency.

[0075] Analysis using the random forest algorithm: Variable importance analysis (such as Mean Decrease Gini) identifies which factors have the greatest impact on frozen gait; Sample clustering or classification can identify frozen gait subtypes, such as: cognitive load-dominated type: increased cognitive load significantly induces freezing; emotional stress-induced type: freezing events increase significantly when emotional stress is high; spatial constraint-sensitive type: spatial obstacles or narrow environments significantly induce freezing.

[0076] D. A user interaction terminal, which is communicatively connected to the data processing unit, is used to provide users with the results of Parkinson's disease frozen gait assessment.

[0077] In one embodiment, the non-walking gait simulation assessment system based on virtual reality interaction further includes a feedback module, which is configured to issue personalized guidance content based on the assessment results of the Parkinson's disease frozen gait of the test subject. The personalized guidance content may include any one or any combination of the following: gait improvement suggestions, early warning information of frozen gait, and task completion status.

[0078] For patients with cognitively driven gait, guidelines for daily living activities can be provided based on the dose-response relationship between the difficulty of the cognitive task and changes in non-walking simulated gait data (e.g., the correlation between cognitive task performance and gait parameters). For example, mild patients can perform low-complexity dual-task walking, such as walking while doing simple counting or naming exercises, but should avoid walking in complex environments that require rapid decision-making; moderate patients are advised to choose structured walking training, such as rhythmic walking along a fixed route, but should avoid sudden interruptions or route changes; severe patients are only advised to walk short distances on undisturbed straight paths, and any distracting motor tasks are prohibited.

[0079] For patients sensitive to spatial constraints, a progressive spatial adaptation training program can be provided based on the analysis results of parameters such as the ratio of passage width to shoulder width and obstacle density. For example, when the passage width / shoulder width ratio is <1.2, it is recommended to first conduct walking training in an open environment; when the ratio is between 1.2 and 1.5, walking in a standard-width passage can be attempted; when the ratio is >1.5, narrow space adaptation training can be conducted. At the same time, an appropriate obstacle avoidance training plan should be designed according to the obstacle density threshold.

[0080] For patients with stress-induced emotional states, targeted psychological intervention recommendations can be provided based on the results of emotional triggering tests. For those sensitive to highly threatening environments, systematic desensitization therapy is recommended; for those sensitive to social evaluation, social exposure training is suggested; and for those sensitive to time pressure, relaxation training and time management strategies are provided.

[0081] In one embodiment, the non-walking gait simulation assessment system based on virtual reality interaction may further include a visualization module. The visualization module is configured to visualize the assessment results of the Parkinson's disease frozen gait of the test subject, thereby obtaining a Parkinson's disease frozen gait assessment report. The content displayed in the Parkinson's disease frozen gait assessment report may include any one or any combination of the following: the test subject's gait performance under gait tasks of different difficulty, the occurrence of frozen gait, the potential for gait improvement, etc. The report content can provide a reference for the formulation of subsequent treatment plans.

[0082] In one embodiment, the visualization module includes multiple dimensions of data presentation. For severity visualization, a progressive color gradient from light green to dark red represents the trend of symptoms changing from mild to severe, while specific clinical score values ​​and corresponding symptom descriptions are annotated, enabling healthcare professionals to quickly grasp the patient's overall condition. For subtype classification visualization, a three-dimensional coordinate system chart can be generated, with cognitive load, spatial constraints, and emotional stress as the coordinate axes. The distribution of patient data points visually reflects the dominant subtype characteristics, supplemented by textual descriptions of typical symptoms.

[0083] For the visualization of specific recommendations, an interactive interface design can be used. For exercise guidance suggestions, a dynamically simulated diagram of human movement will be displayed, annotating the amplitude, frequency, and duration of recommended movements, while warning icons will indicate prohibited movements. Environmental adaptation suggestions will be presented using virtual scene reconstruction technology, allowing adjustment of parameters such as channel width and obstacle density, and visually demonstrating the patient's walking performance in different environmental configurations. The training progress tracking section uses a timeline format, comparing historical assessment data with preset target values ​​and automatically calculating the recommended training intensity for the next stage.

[0084] All visualizations support multi-level information display, allowing users to access more detailed data analysis through clicks or zooming. The system also offers report export functionality, generating standardized assessment documents containing key charts and data summaries for easy clinical archiving and cross-departmental collaboration. The visualization module's output can be directly linked to the hospital's electronic medical record system, providing intuitive evidence for multidisciplinary teams to develop personalized intervention plans.

[0085] Based on the aforementioned non-walking gait simulation assessment system based on virtual reality interaction, the present invention also provides a method for assessing frozen gait in Parkinson's disease, comprising the following steps: S1. Load a dynamically adjustable virtual reality test scenario onto the test subject through a virtual reality interaction module. The virtual reality test scenario includes gait tasks of simulated walking with different levels of difficulty. The test subject is a Parkinson's disease patient. S2. The data acquisition module collects non-walking simulated gait data of the subject performing gait tasks in real time and transmits it to the data processing unit; S3. The data processing unit receives the non-walking simulated gait data of the test subject sent by the data acquisition module, and obtains the Parkinson's disease frozen gait assessment results of the test subject based on the non-walking simulated gait data.

[0086] Figure 3 An embodiment of the Parkinson's disease frozen gait assessment method of the present invention is provided. During the assessment, the subject first wears VR glasses in a virtual reality interaction module and adjusts their sitting posture and foot position. Then, the physician uses a computer to access a non-walking gait simulation assessment system based on virtual reality interaction and selects a gait task. The subject then performs the task to begin the virtual reality movement assessment. The data acquisition module of the non-walking gait simulation assessment system based on virtual reality interaction collects the non-walking simulated gait data of the subject in real time while performing the gait task. The data processing unit then obtains the subject's Parkinson's disease frozen gait assessment result based on the subject's non-walking simulated gait data. Finally, the user interaction terminal receives and provides the Parkinson's disease frozen gait assessment result to the physician to assist the physician in making accurate diagnoses and treatments of the subject.

[0087] The present invention provides a non-walking gait simulation assessment system and method based on virtual reality interaction. This system utilizes a virtual reality interaction module to allow the test subject to complete a gait task in a virtual reality testing scenario, which occurs in conjunction with any one or any combination of cognitive load tasks, emotional load tasks, and spatial obstacle tasks. This simulates the test subject's real-time gait changes in different scenarios. Combined with a data acquisition module and processor, it achieves efficient and accurate assessment of frozen gait in Parkinson's disease. This ensures both the safety of the test subject during the gait assessment process and the objectivity of the assessment. Especially when the test subject frequently exhibits frozen gait, it can accurately record the time and frequency of foot stagnation or changes, helping doctors accurately understand the occurrence pattern of frozen gait and providing effective data guidance for personalized treatment of patients.

[0088] This invention provides a non-walking gait simulation assessment system based on virtual reality interaction, which overcomes the limitations of traditional gait assessment. Previous assessment methods largely rely on simplified gait tests and subjective evaluations, failing to fully simulate the complex and multifaceted environmental factors that may affect frozen gait. Through virtual reality technology, this invention can create highly realistic virtual scenarios that can include various complex situations patients might encounter in real life, such as busy streets, sudden obstacles, and narrow passageways.

[0089] The application of virtual reality allows the assessment process to go beyond static gait testing, enabling dynamic simulation of the impact of various environments on a patient's gait. By simulating these complex factors, doctors can observe the occurrence and exacerbation of gait freezing in specific situations, thus gaining a more comprehensive understanding of the triggering conditions and mechanisms of gait freezing.

[0090] Furthermore, this invention also considers patient safety. Patients do not need to walk around during the assessment; they can be seated. This conserves their energy, allowing for a longer and more thorough assessment, while avoiding the risk of falls caused by movement, thus ensuring a safe assessment process.

[0091] This invention enables the analysis of various factors that may induce frozen gait. For example, external stimuli in the environment (such as pedestrians, traffic, noise, etc.), the degree of confinement in space, and dynamic changes in the environment can all affect a patient's gait performance. Changes in these factors are often difficult to reproduce using traditional assessment methods, but in a virtual reality environment, patients can be tested in a controlled and adjustable scenario, making the assessment process more realistic.

[0092] Furthermore, virtual reality can be combined with sensors and motion capture devices to collect and dynamically analyze patients' non-walking simulated gait data in real time. This data can not only help assess the frequency and duration of gait freeze, but also provide valuable experimental data for studying the physiological mechanisms of gait freeze. Through deep learning algorithms, the virtual reality assessment system can identify different triggering factors and further explore their specific impact on patients' gait.

[0093] This invention contributes to the exploration of the pathophysiological mechanisms of gait freezing in Parkinson's disease, enabling researchers to further investigate these mechanisms. Through precise gait analysis, the system can help reveal the underlying physiological causes of gait freezing, such as neurodegeneration, motor neuron damage, and muscle coordination problems. This process provides technical support for a deeper understanding of the pathogenesis of Parkinson's disease and lays the foundation for developing new treatment methods and intervention strategies.

[0094] 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.

[0095] 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.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention 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; and these 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 the present invention.

Claims

1. A non-walking gait simulation and evaluation system based on virtual reality interaction, characterized in that, include: A virtual reality interaction module is configured to present a dynamically adjustable virtual reality test scene to the test subject, the virtual reality test scene including gait tasks of different difficulty simulating walking. A static postural support unit, comprising a rigid load-bearing structure and a footplate, is configured to support the patient in a non-walking static posture. The data acquisition module includes one or more motion sensors fixedly mounted on the pedal, configured to acquire non-walking simulated gait data of the subject in real time when the subject responds to the simulated walking gait task and send it to the data processing unit. The data processing unit, which is communicatively connected to the data acquisition module and the virtual reality interaction module, includes a processor and a memory. The processor is configured to execute a computer program stored in the memory. When the processor executes the computer program, it performs the following steps: receiving non-walking simulated gait data of the subject sent by the data acquisition module, and obtaining the gait evaluation result of the subject based on the non-walking simulated gait data of the subject.

2. The non-walking gait simulation and evaluation system based on virtual reality interaction according to claim 1, characterized in that, The subject of the test is a Parkinson's disease patient, and the gait assessment result is the Parkinson's disease frozen gait assessment result. The gait tasks of different difficulties include gait tasks that occur in conjunction with any one or any combination of the following: cognitive load tasks, emotional load tasks, and spatial obstacle tasks. Specifically, when the gait task occurs alone, it is a gait task with a difficulty level of 0. When the gait task occurs in conjunction with any one of the cognitive load task, emotional load task, or spatial obstacle task, it is a gait task with a difficulty level of I. When the gait task occurs in conjunction with any two of the cognitive load task, emotional load task, or spatial obstacle task, it is a gait task with a difficulty level of II. When the gait task occurs in conjunction with the cognitive load task, emotional load task, or spatial obstacle task, it is a gait task with a difficulty level of III.

3. The non-walking gait simulation and evaluation system based on virtual reality interaction according to claim 2, characterized in that, Non-walking simulated gait data includes any one or any combination of the following: stride time, cadence, stride length, gait speed, gait rhythm, and reaction time to cues.

4. The non-walking gait simulation and evaluation system based on virtual reality interaction according to claim 3, characterized in that, The process of obtaining the gait assessment results of the test subject based on the non-walking simulated gait data includes: The dynamic sliding window method was used to analyze the non-walking simulated gait data of the subjects in real time, and the key indicator data of the non-walking simulated gait data in each analysis window were obtained. Based on the key indicator data of each analysis window from the non-walking simulated gait data, combined with the preset evaluation criteria, the first step gait evaluation result of the test subject is obtained.

5. The non-walking gait simulation and evaluation system based on virtual reality interaction according to claim 4, characterized in that, Key performance indicator data include data for any one of the following indicators or any combination thereof: mode of gait time and its distribution characteristics, mean and standard deviation of gait frequency, coefficient of variation of gait rhythm, average reaction time to cues and its dispersion.

6. The non-walking gait simulation and evaluation system based on virtual reality interaction according to claim 5, characterized in that, The preset evaluation criteria include: A. For each analysis window, a window is determined to be a frozen event window if two or more of the following conditions are met simultaneously: The current step time is more than twice the mode of the step time within the analysis window; The coefficient of variation of gait rhythm is greater than 0.4; The current reaction time to the prompt exceeds two standard deviations of the mean reaction time in the analysis window; The current step frequency is less than 60% of the average step frequency in the analysis window; B. When two or more freeze event windows occur consecutively, or when freeze event windows occur multiple times within a preset time period, a continuous freeze gait event is determined to have occurred, and the specific time of the freeze gait event is recorded.

7. The non-walking gait simulation and evaluation system based on virtual reality interaction according to claim 6, characterized in that, The process of obtaining the gait assessment results of the test subject based on the non-walking simulated gait data includes: Based on the non-walking simulated gait data of the test subjects, the dose-response relationship between the difficulty of the cognitive load task and the change in non-walking simulated gait data, the dose-response relationship between the emotional intensity of the emotional load task and the duration of freezing, and the dose-response relationship between the constraint intensity of the spatial obstacle task and the occurrence of freezing were analyzed. By analyzing the dose-response relationship between the difficulty of the cognitive load task and changes in non-walking simulated gait data, the dose-response relationship between the emotional intensity of the emotional load task and the duration of freeze, and the dose-response relationship between the constraint intensity of the spatial obstacle task and the occurrence of freeze, the synergistic effect pattern of multidimensional factors was analyzed using the random forest algorithm to obtain the second gait assessment results of the test subjects.

8. The non-walking gait simulation and evaluation system based on virtual reality interaction according to claim 7, characterized in that, The second gait assessment results include any one of the following or any combination thereof: quantitative indicators of the severity of freezing events, probability distribution characteristics of freezing under different triggering factors, classification of freezing gait subtypes based on multidimensional data analysis, and among which, freezing gait subtypes include cognitive load-dominated type, emotional stress-induced type, and spatial constraint-sensitive type.

9. The non-walking gait simulation and evaluation system based on virtual reality interaction according to any one of claims 1-8, characterized in that, It also includes a user interaction terminal, which is communicatively connected to the data processing unit and is used to provide gait assessment results to the user.

10. A gait assessment method based on the non-walking gait simulation assessment system based on virtual reality interaction as described in any one of claims 1-9, comprising: S1. Load a dynamically adjustable virtual reality test scenario onto the test subject through a virtual reality interaction module. The virtual reality test scenario includes gait tasks of varying difficulty. S2. The data acquisition module collects non-walking simulated gait data of the subject in real time when performing simulated walking gait tasks and transmits it to the data processing unit; S3. The data processing unit receives the non-walking simulated gait data of the test subject sent by the data acquisition module, and obtains the gait evaluation result of the test subject based on the non-walking simulated gait data.

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