Gait analysis and anomaly detection system and method for intelligent wearable device

Abstract

The invention relates to the technical field of abnormal gait detection, in particular to a gait analysis and anomaly detection system and method for intelligent wearable equipment. The data acquisition module is used for acquiring motion and physiological data and road condition information; the moving speed calculation module is used for calculating the moving speed; the gait prediction module is used for acquiring a predicted gait and a predicted movement amount; the gait feature extraction module is used for extracting joint vector and joint angle features; the abnormal gait judgment module is used for judging whether the gait is abnormal or not. According to the method, the gait space equation is constructed, the neural network model is used for gait prediction, the time dependence and the nonlinear relation of the gait sequence are captured, and a more accurate prediction result is obtained; by comparing the predicted gait features with the actual gait features, comparing the similarity of the predicted movement amount with the actual movement amount, and comprehensively utilizing the joint vector, the joint angle and the movement amount features, various abnormal gait modes are recognized more comprehensively, and the accuracy and reliability of anomaly detection are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of abnormal gait detection, and in particular to a gait analysis and abnormality detection system for an intelligent wearable device, and a gait analysis and abnormality detection method for an intelligent wearable device. Background Art

[0002] With the development of science and technology, smart wearable devices have been widely used in the fields of health monitoring and behavior analysis. Among them, gait analysis is a very valuable technical means. Human gait contains a lot of information about individual physical condition, balance ability, nervous system operation, etc. Through the careful analysis of gait, some health risks can be detected, providing valuable basis for disease prevention, rehabilitation treatment, etc.

[0003] In gait analysis technology, the key is to extract and quantify the characteristic parameters of gait. Common gait characteristics include cadence, stride, leg swing symmetry, joint angle changes, etc. These characteristics reflect the operating status of the joints, muscles and nervous system of the human body. By measuring and analyzing these characteristics, some abnormal gait patterns can be found, thereby inferring possible health problems.

[0004] The existing gait abnormality detection system mainly collects motion data such as plantar pressure, cadence, stride length, etc. It is difficult to capture the time dependence and nonlinear relationship of the gait sequence, resulting in inaccurate detection results. Summary of the invention

[0005] The present invention provides a gait analysis and abnormality detection system and method for an intelligent wearable device, which are used to solve the defect that the detection results in the prior art are not accurate enough.

[0006] In one aspect, the present invention provides a gait analysis and abnormality detection system for a smart wearable device, comprising: The data acquisition module is used to collect the subjects' motion data, physiological data and road condition information.

[0007] The moving speed calculation module is used to establish a spatial coordinate system based on the road surface where the subject is located as a reference plane, and calculate the moving speed of the subject according to the gait changes of the subject at two adjacent moments in the spatial coordinate system.

[0008] The gait prediction module is used to construct the gait space equation according to the position coordinates of the subject, obtain the gait variables, and derive the gait variables, and then convert the gait space equation into the gait transfer equation. A prediction model based on a neural network is constructed, and the gait variable road condition information is input to output the movement function at the next moment. Any two moving points are selected in the movement function, and the two moving points are expanded to obtain the gait prediction equation, which includes the predicted gait at the next moment and the predicted movement amount in the next period.

[0009] The gait feature extraction module is used to extract the predicted gait features of the next moment's predicted gait and the actual gait features of the subject's actual gait at the next moment. The gait features include joint vectors and joint angle features.

[0010] The abnormal gait judgment module is used to obtain the abnormal detection score based on the similarity between the predicted gait characteristics and the actual gait characteristics, and the similarity between the predicted movement amount and the actual movement amount, and compare the abnormal detection score with the preset abnormal detection score threshold. If the abnormal detection score exceeds the abnormal detection score threshold range, it is judged as an abnormal gait.

[0011] According to a gait analysis and anomaly detection system for a smart wearable device provided by the present invention, the moving speed includes linear speed and angular speed, the linear speed is the average of the moving speed of the left foot and the moving speed of the right foot of the subject, and the angular speed is obtained according to the ratio of the angle turned by the subject at two adjacent moments to the time.

[0012] According to a gait analysis and anomaly detection system for a smart wearable device provided by the present invention, the gait space equation includes a position equation and a movement equation. The position equation is used to characterize the position coordinates and current deflection angle of the subject. The movement equation is used to characterize the movement speed and turning angle of the subject. The gait space equation is expressed as: ; Where K represents the gait variable, x represents the horizontal coordinate, y represents the vertical coordinate, z represents the vertical coordinate, δ represents the deflection angle, δ=(α,β,γ), S represents the movement variable, v represents the linear velocity, Indicates the steering angle.

[0013] According to a gait analysis and anomaly detection system for a smart wearable device provided by the present invention, the gait transfer equation is expressed as: ; In the formula, represents the lateral velocity, represents the longitudinal velocity, represents the vertical velocity, represents the angular velocity, and d represents the distance between the moving trajectories of the left and right feet.

[0014] According to a gait analysis and anomaly detection system for a smart wearable device provided by the present invention, the gait prediction equation includes a predicted gait equation and a predicted movement equation. The predicted gait equation is used to characterize the variables based on the current gait and movement amount, and obtain the predicted gait of the subject at the next moment. The predicted movement equation is used to characterize the predicted movement amount in the next period when the subject moves on the ideal movement trajectory. The gait prediction equation is expressed as: ; ; In the formula, represents the predicted gait at the next moment, is the current gait, represents the variable of movement, A represents the gait matrix, B represents the movement matrix, is the current movement amount, and C is the output matrix.

[0015] According to a gait analysis and anomaly detection system for a smart wearable device provided by the present invention, the predicted gait at the next moment includes the predicted gait amount and predicted movement amount of the subject at the next moment. Expressed as: ; In the formula, represents the predicted gait amount at the next moment, Indicates the predicted movement amount for the next period.

[0016] According to a gait analysis and anomaly detection system for a smart wearable device provided by the present invention, the process of extracting joint vectors includes: Obtain the three-dimensional coordinates of the center point of the subject's joint in the spatial coordinate system.

[0017] Set the three-dimensional coordinates of any two joints to P i =(x i ,y i , z i ), P j =(x j ,y j , z j ), then the joint P i and P j The joint vector is represented as u ij =(x j -x i ,y j -y i , z j -z i ).

[0018] Calculate the joint vectors of every two joints of the subject and construct the joint vector set U={u ij |i,j=1,2,3∧i <j}。

[0019] The process of extracting joint angle features includes: Set any two joint vectors u A and u B , then u A and u B The angle feature between is expressed as: ; Calculate the angle features between the joint vectors of the subjects respectively to form the angle feature set R={r uA，uB |u A ,u B ∈U∧u A ≠u B}.

[0020] According to a gait analysis and anomaly detection system for a smart wearable device provided by the present invention, the process of calculating the similarity between the predicted gait feature and the actual gait feature includes: The Pearson correlation coefficient was used to calculate the similarity between the joint vectors in the predicted gait features and the actual gait features.

[0021] The cosine similarity algorithm is used to calculate the similarity between the joint angle features in the predicted gait features and the actual gait features.

[0022] The similarity between the predicted movement and the actual movement is calculated using the relative difference percentage.

[0023] According to a gait analysis and abnormality detection system for a smart wearable device provided by the present invention, the process of determining whether a subject has gait abnormality includes: The joint vector similarity, joint angle similarity and movement amount similarity are weightedly summed to obtain the anomaly detection score.

[0024] The anomaly detection score is compared with the preset anomaly detection score threshold. If the anomaly detection score exceeds the anomaly detection score threshold range, it is judged as an abnormal gait.

[0025] On the other hand, the present invention also provides a gait analysis and abnormality detection method for a smart wearable device, comprising: S1. Collect the subjects’ exercise data, physiological data and road condition information.

[0026] S2. Calculate the moving speed of the subject, including: S21. Establish a spatial coordinate system based on the road surface where the subject is located as a reference plane.

[0027] S22. Calculate the moving speed of the subject according to the gait changes of the subject at two adjacent moments in the spatial coordinate system.

[0028] S3. Predict the subject's gait at the next moment, including: S31. According to the position coordinates of the subjects, a gait space equation describing the gait change law is constructed to obtain gait variables.

[0029] S32. Deriving the gait variables, and then converting the gait space equation into a gait transfer equation.

[0030] S33, construct a prediction model based on a neural network, input gait variable road condition information, and output the movement function at the next moment.

[0031] S34. Select any two moving points in the moving function and expand the two moving points to obtain a gait prediction equation. The gait prediction equation includes the predicted gait at the next moment and the predicted movement amount in the next period.

[0032] S4. extracting the predicted gait features of the predicted gait at the next moment and the actual gait features of the subject's actual gait at the next moment, wherein the gait features include joint vectors and joint angle features.

[0033] S5. Obtain anomaly detection scores according to the similarity between the predicted gait features and the actual gait features, and the similarity between the predicted movement amount and the actual movement amount.

[0034] S6. Compare the anomaly detection score with a preset anomaly detection score threshold. If the anomaly detection score exceeds the anomaly detection score threshold range, it is determined to be an abnormal gait.

[0035] The present invention provides a gait analysis and anomaly detection system and method for a smart wearable device, which provides rich input information for subsequent gait analysis and anomaly detection by collecting the subject's motion data, combining physiological data and road condition information, and helps to more accurately characterize the subject's gait characteristics. By establishing a moving speed calculation module based on the subject's spatial coordinate system. The method based on the spatial coordinate system can better reflect the subject's motion state in the actual environment and reduce the measurement error caused by changes in the measurement environment.

[0036] The present invention constructs a gait space equation and converts it into a gait transfer equation, and then uses a neural network model to perform gait prediction. Compared with the traditional statistical prediction model, it can better capture the time dependency and nonlinear relationship of the gait sequence, thereby obtaining a more accurate prediction result.

[0037] The present invention compares the similarity between the predicted gait features and the actual gait features, and introduces the similarity between the predicted movement amount and the actual movement amount as the detection basis. The method of comprehensively utilizing the joint vector features, joint angle features and movement amount features can more comprehensively identify various abnormal gait patterns and improve the accuracy and reliability of abnormality detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 It is a structural schematic diagram of a gait analysis and abnormality detection system for a smart wearable device provided by an embodiment of the present invention; Figure 2 It is a flow chart of a gait analysis and abnormality detection method for a smart wearable device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Combine the following Figure 1-Figure 2 The present invention describes a gait analysis and abnormality detection system and method for an intelligent wearable device.

[0042] Figure 1 It is a structural schematic diagram of a gait analysis and anomaly detection system for a smart wearable device provided in an embodiment of the present invention.

[0043] like Figure 1 As shown, an embodiment of the present invention provides a gait analysis and anomaly detection system and method for a smart wearable device. The executing entity can be a gait analysis and anomaly detection system for a smart wearable device, including: a data acquisition module, a moving speed calculation module, a gait prediction module, a gait feature extraction module and an abnormal gait judgment module.

[0044] The data acquisition module is used to collect the subject's motion data, physiological data and road condition information. The motion data includes the subject's location information, running direction, etc. Among them, the location information can be obtained by installing a GPS locator, and the running direction can be obtained by installing a direction recognition detector. Physiological data includes the distance between the subject's left and right foot movement trajectories. Motion data and road condition information can be obtained by installing corresponding sensors. For example, road condition information can be obtained by installing multiple cameras, radar detectors, etc. on the subject.

[0045] The moving speed calculation module is used to establish a spatial coordinate system based on the road surface where the subject is located as the reference plane, map the corresponding gait coordinates in the spatial coordinate system according to the subject's position coordinates and travel direction at each moment, and calculate the subject's moving speed based on the subject's gait changes at two adjacent moments.

[0046] Select any point on the moving road surface as the origin, and select three mutually perpendicular directions on the road surface as the X-axis, Y-axis, and Z-axis to form a spatial coordinate system based on the road surface. Then the position of the subject relative to the origin, that is, the position coordinate, is recorded as (x i ,y i , z i ).

[0047] The moving speed includes linear speed and angular speed. The linear speed is the average of the moving speed of the subject's left foot and the moving speed of the right foot. The angular speed is obtained based on the ratio of the angle turned by the subject at two adjacent moments to the time.

[0048] Assuming the sampling time interval between two adjacent moments is very small, we can derive the following from the geometric relationship: ; Expanding the above formula, we can get: ; According to the speed formula: ; The formula for moving speed is: ; ;

[0049] In the formula, v represents linear velocity, ω represents angular velocity, Indicates the speed of the right foot, represents the speed of the left foot, d represents the distance between the moving trajectories of the left and right feet, θ represents the angle turned between two adjacent moments, and represents the difference in the distance moved by the left and right feet of the subject in two adjacent moments.

[0050] The gait prediction module is used to construct the gait space equation according to the position coordinates, obtain the gait variables, and derive the gait variables, and then convert the gait space equation into the gait transfer equation. A prediction model based on a neural network is constructed, and the gait variable road condition information is input to output the movement function at the next moment. Two moving points are randomly selected in the movement function, and the two moving points are expanded to obtain the gait prediction equation, and then the subject's predicted gait at the next moment and the predicted movement amount in the next period are obtained.

[0051] The gait space equation includes the position equation and the movement equation. The position equation is used to characterize the subject's position coordinates and current deflection angle. The movement equation is used to characterize the subject's movement speed and turning angle. The gait space equation is expressed as: ; Where K represents the gait variable, x represents the horizontal coordinate, y represents the vertical coordinate, z represents the vertical coordinate, δ represents the deflection angle, δ=(α,β,γ), S represents the movement variable, Indicates the steering angle.

[0052] The gait transfer equation is expressed as: ; In the formula, represents the lateral velocity, represents the longitudinal velocity, represents the vertical velocity, Indicates the angular velocity.

[0053] Construct a prediction model based on a neural network, input gait variable road condition information, and output the next moment movement function. The neural network includes multiple activation functions, which are used to predict the gait amount at the next moment. The gait amount at the next moment is expressed by the movement function as: ; In the formula, is the moving function at time t+1, , are the gait amount and movement amount at time t+1 respectively.

[0054] Pick any two points in this shift function: and ,in, , are the gait quantities at any time n and m, , are the movement amounts at any time n and m respectively.

[0055] Place the two selected points in Expand on it, that is: ; In the formula, is the gait amount at time m-1.

[0056] After sorting, we can get: ; Let the gait volume prediction value , Prediction value of movement ,in, is the movement amount at time m, is the movement amount at time n. Introduce sampling time T, and, ,in, It is the predicted value of the movement amount in the previous period.

[0057] We can get: ; In the formula, represents the current gait amount, represents the movement amount, A1 is the first step matrix, and B1 is the first movement matrix.

[0058] In the process of gait prediction, it is necessary to know all the predicted quantities and gait quantities in adjacent time periods, and then find the optimal value of the cost matrix. Therefore, it is necessary to construct a new gait quantity. The final gait prediction equation is expressed as: ; ; In the formula, represents the predicted gait at the next moment, is the current gait, represents the variable of movement, A represents the gait matrix, B represents the movement matrix, represents the current movement amount, and C represents the output matrix.

[0059] The gait prediction equation is optimally solved to obtain the predicted gait at the next moment.

[0060] Define Γ, Λ, Υ as the intermediate transition matrices, and: ; ; ; definition ; but ; Set the optimization objective function to: ; In the formula, represents the gait amount at the previous moment, represents the weight coefficient, represents the relaxation factor, E represents the identity matrix, M represents the gait weighting matrix, and G represents the movement weighting matrix.

[0061] Let the error term , ; Modeling the problem as a constrained quadratic programming problem, we can: ; ; In the formula, J represents the optimization function, Q represents the quadratic term matrix, represents a linear matrix, , , , represents the constraint matrix.

[0062] make ; but ; Optimize the subject's predicted gait volume at the next moment and the predicted movement volume in the next period, and then get the predicted gait volume at the next moment .

[0063] The gait feature extraction module is used to extract the predicted gait features of the predicted gait at the next moment and the actual gait features of the subject's actual gait at the next moment. The gait features include joint vectors and joint angle features.

[0064] The process of extracting joint vectors includes: Obtain the three-dimensional coordinates of the center point of the subject's joint in the spatial coordinate system.

[0065] In this embodiment, the joints may include hip joints, knee joints, ankle joints, and the like.

[0066] Set the three-dimensional coordinates of any two joints to P i =(x i ,y i , z i ), P j =(x j ,y j , z j ), then the joint P i and P j The joint vector is represented as u ij =(x j -x i ,y j -y i , z j -z i ).

[0067] Calculate the joint vectors of every two joints of the subject and construct the joint vector set U={u ij |i,j=1,2,3∧i <j}。

[0068] The process of extracting joint angle features includes: Set any two joint vectors u A and u B , then u A and u B The angle feature between is expressed as: ; Calculate the angle features between the joint vectors of the subjects respectively to form the angle feature set R={r uA，uB |u A,u B ∈U∧u A ≠u B}.

[0069] The abnormal gait judgment module is used to obtain the abnormal detection score based on the similarity between the predicted gait features and the actual gait features, and the similarity between the predicted movement amount and the actual movement amount, and compare the abnormal detection score with the preset abnormal detection score threshold. If the abnormal detection score exceeds the abnormal detection score threshold range, it is judged as an abnormal gait.

[0070] The process of calculating the similarity between the predicted gait features and the actual gait features includes: The Pearson correlation coefficient is used to calculate the similarity between the joint vectors in the predicted gait features and the actual gait features. The formula is expressed as: ; In the formula, represents the actual joint vector, represents the predicted joint vector, Represents the total number of joint vectors.

[0071] The cosine similarity algorithm is used to calculate the similarity between the predicted gait features and the joint angle features in the actual gait features. The formula is expressed as: ; In the formula, Represents the joint vector in the actual gait and The angle characteristics between Represents the joint vector in the predicted gait and The angle characteristics between Represents the total number of angular features.

[0072] The similarity between the predicted movement and the actual movement is calculated using the relative difference percentage, expressed as: ; In the formula, Indicates the predicted movement amount for the next period, Indicates the actual movement amount for the next period.

[0073] The process of determining whether a subject has gait abnormality includes: The joint vector similarity, joint angle similarity and movement deviation scores are weighted and summed to obtain the anomaly detection score, which is expressed as: ; In the formula, , and Represents the weight coefficient.

[0074] The anomaly detection score is compared with the preset anomaly detection score threshold. If the anomaly detection score exceeds the anomaly detection score threshold range, it is judged as an abnormal gait.

[0075] In summary, this embodiment provides a gait analysis and anomaly detection system for an intelligent wearable device. By collecting the motion data of the subject, combined with physiological data and road condition information, it provides rich input information for subsequent gait analysis and anomaly detection, which helps to more accurately characterize the gait characteristics of the subject. By establishing a moving speed calculation module based on the spatial coordinate system where the subject is located. The method based on the spatial coordinate system can better reflect the motion state of the subject in the actual environment and reduce the measurement error caused by changes in the measurement environment. By constructing a gait space equation and converting it into a gait transfer equation, and then using a neural network model for gait prediction. Compared with the traditional statistical prediction model, it can better capture the time dependence and nonlinear relationship of the gait sequence, so as to obtain more accurate prediction results. Not only is the similarity between the predicted gait characteristics and the actual gait characteristics compared, but also the similarity between the predicted movement amount and the actual movement amount is introduced as the detection basis. The method of comprehensively utilizing joint vector features, joint angle features and movement amount features can more comprehensively identify various abnormal gait patterns and improve the accuracy and reliability of anomaly detection.

[0076] Based on the same general inventive concept, the present invention also protects a gait analysis and abnormality detection method for a smart wearable device. The gait analysis and abnormality detection method for a smart wearable device provided by the present invention is described below. The gait analysis and abnormality detection method for a smart wearable device described below and the gait analysis and abnormality detection system for a smart wearable device described above can be referenced to each other.

[0077] Figure 2 It is a flow chart of a gait analysis and abnormality detection method for a smart wearable device provided in an embodiment of the present invention.

[0078] like Figure 2 As shown, the gait analysis and abnormality detection method of the smart wearable device includes: S1. Collect the subjects’ exercise data, physiological data and road condition information.

[0079] S2. Calculate the moving speed of the subject, including: S21. Establish a spatial coordinate system based on the road surface where the subject is located as a reference plane.

[0080] S22. Calculate the moving speed of the subject according to the gait changes of the subject at two adjacent moments in the spatial coordinate system.

[0081] S3. Predict the subject's gait at the next moment, including: S31. According to the position coordinates of the subjects, a gait space equation describing the gait change law is constructed to obtain gait variables.

[0082] S32. Deriving the gait variables, and then converting the gait space equation into a gait transfer equation.

[0083] S33, construct a prediction model based on a neural network, input gait variable road condition information, and output the movement function at the next moment.

[0084] S34. Select any two moving points in the moving function and expand the two moving points to obtain a gait prediction equation. The gait prediction equation includes the predicted gait at the next moment and the predicted movement amount in the next period.

[0085] S4. extracting the predicted gait features of the predicted gait at the next moment and the actual gait features of the subject's actual gait at the next moment, wherein the gait features include joint vectors and joint angle features.

[0086] S5. Obtain anomaly detection scores according to the similarity between the predicted gait features and the actual gait features, and the similarity between the predicted movement amount and the actual movement amount.

[0087] S6. Compare the anomaly detection score with a preset anomaly detection score threshold. If the anomaly detection score exceeds the anomaly detection score threshold range, it is determined to be an abnormal gait.

[0088] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A gait analysis and anomaly detection system for smart wearable devices, characterized in that: include: A data collection module is used to collect the subjects' exercise data, physiological data and road condition information; A moving speed calculation module, used to establish a spatial coordinate system based on the road surface where the subject is located as a reference plane, and calculate the moving speed of the subject according to the gait changes of the subject at two adjacent moments in the spatial coordinate system; A gait prediction module is used to construct a gait space equation according to the position coordinates of the subject, obtain gait variables, and derive the gait variables to convert the gait space equation into a gait transfer equation; and to construct a prediction model based on a neural network, input the gait variables and the road condition information, and output a movement function at the next moment; select any two movement points in the movement function, and expand the two movement points to obtain a gait prediction equation, wherein the gait prediction equation includes the predicted gait at the next moment and the predicted movement amount in the next time period; A gait feature extraction module, used to extract the predicted gait features of the predicted gait at the next moment and the actual gait features of the subject's actual gait at the next moment, wherein the gait features include joint vectors and joint angle features; The abnormal gait judgment module is used to obtain an abnormal detection score based on the similarity between the predicted gait feature and the actual gait feature, and the similarity between the predicted movement amount and the actual movement amount, and compare the abnormal detection score with a preset abnormal detection score threshold. If the abnormal detection score exceeds the abnormal detection score threshold range, it is judged as an abnormal gait.

2. The gait analysis and abnormality detection system for a smart wearable device according to claim 1, characterized in that: The moving speed includes linear speed and angular speed. The linear speed is the average of the moving speed of the subject's left foot and the moving speed of the right foot. The angular speed is obtained according to the ratio of the angle turned by the subject at two adjacent moments to the time.

3. The gait analysis and abnormality detection system for a smart wearable device according to claim 1, characterized in that: The gait space equation includes a position equation and a movement equation; the position equation is used to characterize the position coordinates and current deflection angle of the subject; the movement equation is used to characterize the movement speed and turning angle of the subject; the gait space equation is expressed as: ； Where K represents the gait variable, x represents the horizontal coordinate, y represents the vertical coordinate, z represents the vertical coordinate, δ represents the deflection angle, δ=(α,β,γ), S represents the movement variable, v represents the linear velocity, Indicates the steering angle.

4. The gait analysis and abnormality detection system for a smart wearable device according to claim 1, characterized in that: The gait transfer equation is expressed as: ； In the formula, represents the lateral velocity, represents the longitudinal velocity, represents the vertical velocity, represents the angular velocity, represents the steering angle, α, β, γ represent the components of the deflection angle δ on the X, Y, and Z axes, and d represents the distance between the moving trajectories of the left and right feet.

5. The gait analysis and abnormality detection system for a smart wearable device according to claim 1, characterized in that: The gait prediction equation includes a predicted gait equation and a predicted movement equation; the predicted gait equation is used to characterize the variables according to the current gait and movement amount, and obtain the predicted gait of the subject at the next moment; the predicted movement equation is used to characterize the predicted movement amount in the next period when the subject moves on the ideal movement trajectory; the gait prediction equation is expressed as: ； ； In the formula, represents the predicted gait at the next moment, is the current gait, represents the variable of movement, A represents the gait matrix, B represents the movement matrix, is the current movement amount, and C is the output matrix.

6. The gait analysis and abnormality detection system for a smart wearable device according to claim 1, characterized in that: The predicted gait at the next moment includes the predicted gait amount and predicted movement amount of the subject at the next moment; the predicted gait at the next moment Expressed as: ； In the formula, represents the predicted gait amount at the next moment, Indicates the predicted movement amount for the next period.

7. The gait analysis and abnormality detection system for a smart wearable device according to claim 1, characterized in that: The process of extracting the joint vector includes: Obtaining the three-dimensional coordinates of the center point of the subject's joint in the spatial coordinate system; Set the three-dimensional coordinates of any two joints to P i =(x i ,y i , z i ), P j =(x j ,y j , z j ), then the joint P i and P j The joint vector is represented as u ij =(x j -x i ,y j -y i , z j -z i ); Calculate the joint vectors of every two joints of the subject respectively to form a joint vector set U={u ij |i,j=1,2,3∧i <j}; The extraction process of the joint angle feature includes: Set any two joint vectors u A and u B , then u A and u B The angle feature between is expressed as: ； Calculate the angle features between the joint vectors of the subjects respectively to form the angle feature set R={r uA，uB |u A ,u B ∈U∧u A ≠u B }.

8. The gait analysis and abnormality detection system for a smart wearable device according to claim 7, characterized in that: The process of calculating the similarity between the predicted gait feature and the actual gait feature includes: Calculate the similarity between the predicted gait feature and the joint vectors in the actual gait feature using the Pearson correlation coefficient; Calculating the similarity between the predicted gait feature and the joint angle feature in the actual gait feature using a cosine similarity algorithm; The similarity between the predicted movement amount and the actual movement amount is calculated using a relative difference percentage.

9. The gait analysis and abnormality detection system for a smart wearable device according to claim 1, characterized in that: The process of determining whether a subject has gait abnormality includes: The joint vector similarity, joint angle similarity and movement amount similarity are weightedly summed to obtain the anomaly detection score; The abnormality detection score is compared with a preset abnormality detection score threshold, and if the abnormality detection score exceeds the abnormality detection score threshold range, it is determined to be an abnormal gait.

10. A gait analysis and anomaly detection method for a smart wearable device, using a gait analysis and anomaly detection system for a smart wearable device as claimed in any one of claims 1 to 9, characterized in that: Methods include: S1, collect the subjects' exercise data, physiological data and road condition information; S2. Calculate the moving speed of the subject, including: S21, establishing a spatial coordinate system based on the road surface where the subject is located as a reference plane; S22, calculating the moving speed of the subject according to the gait changes of the subject at two adjacent moments in the spatial coordinate system; S3. Predict the subject's gait at the next moment, including: S31. According to the position coordinates of the subjects, a gait space equation describing the gait change law is constructed to obtain gait variables; S32, deriving the gait variables, and then converting the gait space equation into a gait transfer equation; S33, constructing a prediction model based on a neural network, inputting gait variable road condition information, and outputting a movement function at the next moment; S34, selecting any two moving points in the moving function, and expanding the two moving points to obtain a gait prediction equation, wherein the gait prediction equation includes a predicted gait at the next moment and a predicted movement amount in the next period; S4, extracting the predicted gait features of the predicted gait at the next moment and the actual gait features of the subject's actual gait at the next moment, the gait features including joint vectors and joint angle features; S5. Obtain anomaly detection scores according to the similarity between the predicted gait features and the actual gait features, and the similarity between the predicted movement amount and the actual movement amount; S6. Compare the anomaly detection score with a preset anomaly detection score threshold. If the anomaly detection score exceeds the anomaly detection score threshold range, it is determined to be an abnormal gait.

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