Solution method of jss, evaluation method and evaluation system for upper arm muscle rehabilitation exercise

Abstract

This invention discloses a method for solving the JSD (Joint Action-Impedance Synergy Index), a method for evaluating upper arm muscle rehabilitation exercises, and an evaluation system, belonging to the fields of rehabilitation assessment and medical imaging technology. Its key technical points are: acquiring the instantaneous instability sequence of joint displacement and the average impedance change value, and constructing the Action-Impedance Synergy Index (AISI) as an evaluation indicator to achieve a three-level probability distribution classification of muscle rehabilitation levels. This invention integrates temporal and physiological characteristics of movement, providing an objective, quantitative, and multimodal muscle rehabilitation assessment scheme suitable for evaluating the effectiveness of rehabilitation training.

Description

Technical Field

[0001] This invention relates to the field of rehabilitation assessment and medical imaging technology, specifically to a JSD solution method, an upper arm muscle rehabilitation exercise evaluation method, and an evaluation system. Background Technology

[0002] Rehabilitation physical training (RPT) is a crucial component of postoperative and stroke recovery, aiming to strengthen weakened muscle groups and improve neuromuscular control. Accurate assessment of patient rehabilitation progress is paramount, directly impacting the development of subsequent training programs and the patient's potential rehabilitation outcomes. Therefore, establishing effective indicators for assessing patient training progress is extremely important.

[0003] The main methods for evaluating RPT progress currently include:

[0004] (1) Objective evaluation by physical therapists. For example, Yu Duisheng's "Handbook of Rehabilitation Medicine Evaluation" [M]. Huaxia Publishing House, 1993, divides muscle strength evaluation into five levels, evaluating the strength of different muscles through different movements. However, this evaluation method relies too much on the therapist's personal experience, making it difficult to identify subtle changes in strength and movement, and thus has limited evaluation effectiveness.

[0005] (2) Surface electromyography (sEMG) signal acquisition. Surface electromyography (sEMG) signal acquisition has problems such as unstable signals and inability to provide information on deep muscles.

[0006] like:

[0007] CN112043268A (National Research Center for Rehabilitation Aids) obtains muscle tone evaluation indicators by analyzing electromyographic signals, and acquires evaluation indicators for movement time, movement smoothness, differences between the same movement, and movement trajectory deviation by analyzing three-dimensional motion information. It uses the analytic hierarchy process (AHP) to analyze the weights of these five evaluation indicators in the assessment of rehabilitation effects, thus obtaining the upper limb rehabilitation training effect. Although this method can evaluate the initiative in rehabilitation, its assessment dimensions mainly rely on the amplitude of electromyographic signals and external contact force, lacking direct detection of the internal physiological state of the muscles (such as muscle tissue composition, blood flow distribution, edema, etc.), making it difficult to comprehensively reflect the intrinsic mechanism of muscle function recovery.

[0008] CN112043268A (Tianjin University of Technology) measures upper limb rehabilitation movement based on the degree of participation in rehabilitation movements using electromyography (EMG) signals and contact forces during the rehabilitation process. Although this method is relatively comprehensive in terms of evaluation dimensions, it relies on a professional three-dimensional motion capture system and multi-channel EMG acquisition equipment. The system is costly and complex to operate, making it difficult to widely apply in home or community rehabilitation environments. Furthermore, it cannot achieve real-time, continuous monitoring and feedback of rehabilitation status.

[0009] KR20140071739A (Daegu Gyeongbuk Institute of Science and Technology) analyzes the amplitude and frequency of electromyographic (EMG) signals to calculate muscle activation and fatigue, thereby assessing the degree of upper limb muscle recovery. However, this method relies solely on EMG signals and cannot assess the standardization, coordination, and quality of movement execution. Furthermore, it is susceptible to factors such as electrode placement and skin impedance, resulting in limited signal stability and reliability.

[0010] (3) Electrical Impedance Tomography (EIT). For example, “Sun Bo, Panji Nursetia Darma, Zhang Quancheng, et al. Study on electrical properties of calf muscles under neuromuscular electrical stimulation [J]. Progress in Biochemistry and Biophysics, 2023, 50(6):1443-1453” uses electrical impedance tomography (EIT) to study the electrical properties of calf muscles under neuromuscular electrical stimulation (NMES), and uses EIT as a long-term monitoring method to visualize the training effect of NMES training on human calf muscles.

[0011] In summary, existing rehabilitation assessment methods share the following common shortcomings:

[0012] (1) Single or insufficient assessment dimensions: Most methods rely on only a single type of signal (such as electromyography or kinematics), or although multiple signals are fused, they do not deeply combine the internal physiological state of the muscles with the quality of external movements, making it difficult to comprehensively and realistically reflect the level of rehabilitation.

[0013] (2) Dependence on specialized equipment and environment: Existing solutions usually require expensive specialized equipment (such as three-dimensional motion capture systems and multi-channel electromyography devices) and professional personnel to operate, which limits their application in popular scenarios such as homes and communities.

[0014] (3) Insufficient real-time and continuity: Most methods focus on single or phased assessments, making it difficult to achieve real-time monitoring and dynamic feedback during rehabilitation training, and unable to adjust training intensity and methods in a timely manner.

[0015] (4) Lack of direct assessment of the intrinsic state of muscles: Existing methods are mostly based on electrophysiological signals such as electromyography or kinematic parameters, which cannot intuitively and quantitatively assess intrinsic physiological indicators such as muscle tissue composition, blood flow changes, and edema status, which are of great significance for the rehabilitation process.

[0016] To overcome the above shortcomings, there is an urgent need for a rehabilitation evaluation method that can integrate exercise quality assessment and muscle intrinsic state detection. Summary of the Invention

[0017] The purpose of this invention is to solve the problems existing in the prior art and provide a method for solving JSD.

[0018] Another objective of this application is to provide a method for evaluating upper arm muscle rehabilitation exercises.

[0019] Another objective of this application is to provide an upper arm muscle rehabilitation exercise evaluation system.

[0020] A method for solving JSD includes the following steps:

[0021] S1, obtain the time-wrist joint trajectory relationship curve tx(t), where t is time and x(t) is the wrist joint trajectory;

[0022] S2, filter the tx(t) curve in step S1 to obtain the smoothed time-wrist joint trajectory curve t-xlp(t), where xlp(t) is the smoothed wrist joint trajectory;

[0023] S3, calculate the time-error relationship curve te(t): e(t) = x(t) - xlp(t);

[0024] S4, calculate the degree of wrist joint trajectory fluctuation JSD(t) at any time t, which is obtained by the following formula:

[0025] ;

[0026] Where w represents the time sliding window.

[0027] Furthermore, the value range of w is [0.2s, 0.3s].

[0028] Furthermore, step S2 employs a second-order or fourth-order Butterworth low-pass filter for filtering.

[0029] An evaluation method for upper arm muscle rehabilitation exercises identifies the treatment effect by comparing the t-JSD(t) curves before and after treatment: the t-JSD(t) curves before and after treatment are obtained using the aforementioned JSD solution method;

[0030] If the maximum value of the t-JSD(t) curve after treatment is less than the maximum value of the t-JSD(t) curve before treatment, it indicates that the treatment is effective; otherwise, it indicates that the treatment is ineffective.

[0031] A method for evaluating upper arm muscle rehabilitation exercises includes the following steps:

[0032] S100, obtain the end time points t1, t2, ..., t3 of each of N consecutive bicep curl exercise cycles. N The corresponding JSD(t1), JSD(t2), ..., JSD(t)N N is a natural number greater than or equal to 20;

[0033] It includes the following sub-steps:

[0034] S101, time acquisition from t 起始 To t 末尾 The wrist joint trajectory relationship curve tx(t), where t1-t 起始 >w / 2,t 末尾 - t N >w / 2;

[0035] S102, filter the tx(t) curve in S101 to obtain the smoothed time-wrist joint trajectory curve t-xlp(t), where xlp(t) is the smoothed wrist joint trajectory;

[0036] S103, calculate the time-error relationship curve te(t): e(t) = x(t) - xlp(t);

[0037] S104, calculate t1, t2, ..., t N The degree of wrist joint trajectory fluctuation JSD(t1), JSD(t2), ..., JSD(t) N );

[0038] Any t j JSD(t) corresponding to time t j The following formula is used for calculation:

[0039] ;

[0040] j is any natural number from 1 to N;

[0041] S200, obtain t0, t1, t2, t3, ..., t based on the same excitation frequency. N The corresponding boundary voltage matrices are V(t0), V(t1), V(t2), V(t3), ..., V(t). N );

[0042] S300, based on the data obtained from S200, acquires t1, t2, t3, ..., t N The corresponding average impedance changes are ΔZ(t1), ΔZ(t2), ΔZ(t3), ..., ΔZ(t). N );

[0043] S400, solve for the action-impedance compatibility index AISI:

[0044] AISI={[JSD(t1)△Z(t1)+JSD(t2)△Z(t2)](t2-t1) / 2+[JSD(t2)△Z(t2)+JSD(t3)△Z(t3)](t3-t2) / 2+……+[JSD(t j-1 )△Z(t j-1 )+JSD(t j )△Z(t j )](t j -t j-1 ) / 2……+[JSD(t N-1 )△Z(t N-1 )+JSD(t N )△Z(t N )](t N -t N-1 ) / 2]} / {2N[Var(JSD) Var(△Z)] 0.5};

[0045] Where Var(JSD) represents JSD(t1), JSD(t2), ..., JSD(t) N The variance of ) is represented by Var(ΔZ), where Var(ΔZ) represents ΔZ(t1), ΔZ(t2), ΔZ(t3), ..., ΔZ(t4). N The variance of ).

[0046] S500, based on the muscle rehabilitation assessment results provided by AISI:

[0047] If AISI ≥ 0.80, it indicates muscle rehabilitation;

[0048] If 0.80 > AISI ≥ 0.60, it indicates mild muscle damage;

[0049] If 0.60 > AISI, it indicates moderate to severe muscle damage.

[0050] Furthermore, step S300 includes the following sub-steps:

[0051] S301, calculate t1, t2, ..., t N The boundary voltage difference matrix at time t1, ΔV(t2), ..., ΔV(t) N );

[0052] The boundary voltage matrix V(t0) before the start of motion is used as a reference, and compared with t j The difference between the boundary voltage matrices at time t is obtained. j Boundary voltage difference matrix ΔV(t) at time t j ):

[0053] ΔV(t j )=V(t j)-V(t0), where j is any natural number from 1 to N;

[0054] S302, calculate t1, t2, ..., t N The relative conductivity matrices Δσ(t1), Δσ(t2), ..., Δσ(t0) at time t0 and time t0 N );

[0055] S303, Solve for the normalized values ​​t1, t2, ..., t N The relative conductivity matrix Δσ between time t0 and time t0 norm (t1), Δσ norm (t2), ...Δσ norm (t N );

[0056] Δσ norm (t j )=Δσ(t j ) / max{Δσ(t j )},max{Δσ(t j )} represents the matrix Δσ(t) j The maximum value in );

[0057] S304, solve for △Z(t1), △Z(t2), △Z(t3), ..., △Z(t4). N );

[0058] At any time t j The average impedance change value ΔZ(t) j ): ΔZ(t) j )=mean(Δσ norm (t j ()), mean means to calculate the average.

[0059] Furthermore, N can be 20, 25, or 30.

[0060] The advantages of the technical solution of this invention are mainly reflected in:

[0061] First, the first basic concept of this application is to propose JSD to characterize the degree of shaking of the subject's movements when performing rehabilitation exercises.

[0062] The solution method is as follows:

[0063] S1, obtain the time-wrist joint trajectory relationship curve tx(t), where t is time and x(t) is the wrist joint trajectory;

[0064] S2, filter the tx(t) curve in step S1 to obtain the smoothed time-wrist joint trajectory curve t-xlp(t), where xlp(t) is the smoothed wrist joint trajectory;

[0065] S3, calculate the time-error relationship curve te(t): e(t) = x(t) - xlp(t);

[0066] S4, calculate the degree of wrist joint trajectory fluctuation JSD(t) at any time t, which is obtained by the following formula:

[0067] ;

[0068] Where w represents the time sliding window.

[0069] Second, this application proposes a method for assessing muscle rehabilitation levels, which requires evaluation from two aspects: the standard and stability of the subject's movements during rehabilitation exercises, and the muscle state during rehabilitation exercises. These two aspects are used to jointly assess the level of muscle rehabilitation. Specifically, a new specific parameter, AISI, is proposed. AISI's numerical response to motor-resistance temporal synergy is discussed. Using AISI has the following advantages: quantitatively assessing the degree of recovery of upper arm muscle control; and enabling objective comparison of the effects of different rehabilitation programs.

[0070] AISI is defined as follows:

[0071] AISI={[JSD(t1)△Z(t1)+JSD(t2)△Z(t2)](t2-t1) / 2+[JSD(t2)△Z(t2)+JSD(t3)△Z(t3)](t3-t2) / 2+……+[JSD(t j-1 )△Z(t j-1 )+JSD(t j )△Z(t j )](t j -t j-1 ) / 2……+[JSD(t N-1 )△Z(t N-1 )+JSD(t N )△Z(t N )](t N -t N-1 ) / 2]} / {2N[Var(JSD) Var(△Z)] 0.5};

[0072] JSD(t) j ) refers to t j Instantaneous instability of joint displacement at any given moment reflects the degree of motion vibration;

[0073] △Z(t j () refers to the sequence of impedance (or conductivity) changes at time t. j The value reflects muscle fatigue or nerve activation.

[0074] In the AISI calculation process, the normalized relative conductivity matrix Δσ is used. norm (t j This avoids measurement differences caused by different excitation frequencies and amplitude differences caused by different measurement conditions. Attached Figure Description

[0075] The present invention will be further described in detail below with reference to the embodiments shown in the accompanying drawings, but this does not constitute any limitation on the present invention.

[0076] Figure 1 This is a schematic diagram of wrist joint trajectory acquisition based on a 3D vision motion capture system.

[0077] Figure 2 This is a diagram illustrating the solution process for JSD.

[0078] Figure 3 This demonstrates the specific characteristics of AISI in muscle rehabilitation, mild muscle injury, and moderate to severe muscle injury. Detailed Implementation

[0079] The objectives, advantages, and features of this invention will be explained through the following non-limiting description of preferred embodiments. These embodiments are merely typical examples of applying the technical solutions of this invention, and all technical solutions formed by equivalent substitutions or equivalent transformations fall within the scope of protection claimed by this invention.

[0080] <I. Experimental Design and Data Acquisition>

[0081] This study initially recruited 104 participants. All participants signed informed consent forms and were physically capable of performing bicep curls.

[0082] Beforehand, each participant was instructed to perform standard bicep curls. At the start of the experiment, participants wore EIT muscle detection devices on their upper arms and performed N bicep curls with a dumbbell of weight M under the monitoring of a 3D visual motion capture system. During the exercise, the 3D visual motion capture system and the EIT muscle detection device simultaneously collected the following two types of data:

[0083] (1) Instantaneous instability sequence JSD(t) of joint displacement: acquired by the 3D vision motion capture system;

[0084] (2) Boom boundary voltage signal V(t): acquired by a 16-channel EIT device in adjacent excitation-adjacent measurement mode, with a total of 208 voltage groups;

[0085] In the data quality control phase, 14 subjects were excluded based on the following criteria:

[0086] (1) Due to objective factors (such as camera placement angle and lighting), the 3D visual motion capture system did not completely and correctly capture the entire process of the subject performing the bicep curl.

[0087] (2) The EIT reconstructed image contains ≥5 consecutive frames of artifacts, reconstruction failure, or obvious abnormal electrode contact;

[0088] (3) The required number of repetitions were not reached due to insufficient strength or improper technique;

[0089] The final sample size included in the analysis was 90 cases, comprising: a normal control group (30 cases), a mild muscle injury group (30 cases), and a moderate to severe muscle injury group (30 cases). The final scatter plot is shown below. Figure 1 .

[0090] <II. Jitter Severity Degree (JSD) of Joint Displacement>

[0091] This application proposes an evaluation index for the stability of human joint motion trajectory: Joint Displacement Instability JSD.

[0092] A method for calculating the Jitter Severity Degree (JSD) of wrist joint trajectory includes the following steps:

[0093] S1, obtain the time-wrist joint trajectory relationship curve tx(t); t represents time, and x(t) represents the wrist joint trajectory at time t;

[0094] S2, filter the tx(t) curve in step S1 to obtain the smoothed time-wrist joint trajectory curve t-xlp(t); xlp(t) represents the smoothed wrist joint trajectory;

[0095] S3, calculate the time-error relationship curve te(t): e(t) = x(t) - xlp(t);

[0096] S4, calculate the average amplitude JSD(t) of the jitter at any time t, using the following formula:

[0097] ;

[0098] w represents the time sliding window. The smaller w is, the better the JSD(t) curve can capture rapid fluctuations, but the more sensitive it is to noise, and the greater the curve's own jitter. The larger w is, the smoother and more stable the JSD(t) curve is, but it will blur rapid details and produce a "lag" effect. In this application, which is for evaluating rehabilitation movements (wrist flexion), w = 0.2s~0.3s is appropriate.

[0099] It should be noted that the wrist joint trajectory indicates the wrist joint angle or position.

[0100] It should be noted that the time span of the time-wrist joint trajectory relationship curve tx(t) in step S1 is from t0 to t1. N The time span of the JSD(t) curve is: t0 + w / 2 ~ t N - w / 2.

[0101] It should be noted that the filtering in step S2 uses a second-order or fourth-order Butterworth low-pass filter to filter out high-frequency noise and obtain the smoothed trajectory xlp(t). This is an existing technology, such as: https: / / blog.csdn.net / qq_45267848 / article / details / 113858560, CN103792699A, etc., and will not be elaborated here.

[0102] The significance of JSD's evaluation lies in:

[0103] (1) The patient completes the standardized bending exercise, and the time-wrist joint trajectory relationship curve tx(t) before and after treatment is collected by the camera. Then, the t-JSD(t) curve before and after treatment can be compared to identify the treatment effect.

[0104] (2) is a prerequisite indicator for AISI evaluation indicators.

[0105] <III. AISI Evaluation Indicators>

[0106] The solution method for AISI includes the following steps:

[0107] S100, obtain N consecutive bicep curl cycles [t0,t1], [t1,t2], [t2,t3], ..., [t N-1 ,t N The end times of each motion cycle are t1, t2, t3, ..., t. N The corresponding JSD(t1), JSD(t2), JSD(t3), ..., JSD(t) N );

[0108] It includes the following sub-steps:

[0109] S101, time acquisition from t 起始 To t 末尾 The wrist joint trajectory relationship curve tx(t), where t1-t 起始 >w / 2,t 末尾 - t N >w / 2;

[0110] S102, filter the tx(t) curve in S101 to obtain the smoothed time-wrist joint trajectory curve t-xlp(t), where xlp(t) is the smoothed wrist joint trajectory;

[0111] S103, calculate the time-error relationship curve te(t): e(t) = x(t) - xlp(t);

[0112] S104, calculate t1, t2, ..., t N The degree of wrist joint trajectory fluctuation JSD(t1), JSD(t2), ..., JSD(t) N );

[0113] Any t j JSD(t) corresponding to time t j The following formula is used for calculation:

[0114] ;

[0115] Where j is any natural number from 1 to N; w represents the time sliding window, and the value of w ranges from [0.2s, 0.3s].

[0116] S200, obtain t0, t1, t2, t3, ..., t based on the same excitation frequency. N The corresponding boundary voltage matrices are V(t0), V(t1), V(t2), V(t3), ..., V(t). N );

[0117] S300, based on the data obtained from S200, acquires t1, t2, t3, ..., t N The corresponding average impedance changes are ΔZ(t1), ΔZ(t2), ΔZ(t3), ..., ΔZ(t). N );

[0118] It includes the following sub-steps:

[0119] S301, calculate t1, t2, ..., t N The boundary voltage difference matrix at time t1, ΔV(t2), ..., ΔV(t) N );

[0120] The boundary voltage matrix V(t0) before the start of motion is used as a reference, and compared with t j The difference between the boundary voltage matrices at time t is obtained. j Boundary voltage difference matrix ΔV(t) at time t j ):

[0121] ΔV(t j )=V(t j)-V(t0), where j is any natural number from 1 to N;

[0122] S302, the TK-Noser method (or other existing technical solutions) is used to calculate t1, t2, ..., t N The relative conductivity matrices Δσ(t1), Δσ(t2), ..., Δσ(t0) at time t0 and time t0 N );

[0123] S303, Solve for the normalized values ​​t1, t2, ..., t N The relative conductivity matrix Δσ between time t0 and time t0 norm (t1), Δσ norm (t2), ...Δσ norm (t N );

[0124] Δσ norm (t j )=Δσ(t j ) / max{Δσ(t j )},max{Δσ(t j )} represents the matrix Δσ(t) j The maximum value in );

[0125] S304, solve for △Z(t1), △Z(t2), △Z(t3), ..., △Z(t4). N );

[0126] At any time t j The average impedance change value ΔZ(t) j ): ΔZ(t) j )=mean(Δσ norm (t j ), mean represents solving for the average; △Z(t) j () refers to the impedance change sequence at time t j The value reflects muscle fatigue or nerve activation.

[0127] S400, solve for the Action-Impedance Synergy Index (AISI):

[0128] AISI={[JSD(t1)△Z(t1)+JSD(t2)△Z(t2)](t2-t1) / 2+[JSD(t2)△Z(t2)+JSD(t3)△Z(t3)](t3-t2) / 2+……+[JSD(t j-1 )△Z(t j-1 )+JSD(t j )△Z(tj )](t j -t j-1 ) / 2……+[JSD(t N-1 )△Z(t N-1 )+JSD(t N )△Z(t N )](t N -t N-1 ) / 2]} / {2N[Var(JSD) Var(△Z)] 0.5};

[0129] Where Var(JSD) represents JSD(t1), JSD(t2), ..., JSD(t) N The variance of ) is represented by Var(ΔZ), where Var(ΔZ) represents ΔZ(t1), ΔZ(t2), ΔZ(t3), ..., ΔZ(t4). N The variance of ).

[0130] S500, based on the muscle rehabilitation assessment results provided by AISI:

[0131] If AISI ≥ 0.80, it indicates muscle rehabilitation;

[0132] If 0.80 > AISI ≥ 0.60, it indicates mild muscle damage;

[0133] If 0.60 > AISI, it indicates moderate to severe muscle damage.

[0134] It should be noted that JSD(t j ) refers to t j Instantaneous instability of joint displacement at any given moment reflects the degree of motion vibration;

[0135] It should be noted that N refers to the number of repetitions of the bicep curl exercise, which is generally taken as 20, 25, or 30.

[0136] It should be noted that the dumbbells used in the bicep curl exercise are standard 5kg dumbbells.

[0137] It should be noted that the TK-Noser method calculates t j The relative conductivity matrix Δσ(t) with t0 j The steps are as follows: First, solve for the sensitivity matrix J based on the shape of the subject's upper arm muscles; then calculate Δσ(t) j ): Δσ(t) i )=(J T ·J+k t ·I+k n W) -1 ·J T ·ΔV(t j ); J TIt is the transpose of J, k t k n These are the regularization parameters, where W represents a diagonal matrix of the same order and diagonal elements as J; and I represents an identity matrix with the same number of columns as J.

[0138] The symbols and their physical meanings in this application are explained below:

[0139] JSD: Wrist joint trajectory fluctuation degree.

[0140] t: time.

[0141] x(t): wrist joint trajectory.

[0142] xlp(t): Smoothed wrist joint trajectory.

[0143] e(t): x(t) - xlp(t).

[0144] JSD(t): The degree of wrist joint trajectory fluctuation at any time t.

[0145] w: Time sliding window, value range: [0.2s, 0.3s].

[0146] t j-1 ,t j Let j represent the start and end times of the j-th consecutive bicep curl cycle, where j is any natural number from 1 to N.

[0147] t 起始 t 末尾 The starting and ending times of the wrist joint trajectory relationship curve tx(t) are input when using the upper arm muscle rehabilitation exercise evaluation method.

[0148] V(t0), V(t1), V(t2), V(t3),…, V(t N ): t0, t1, t2, t3,…,t N The corresponding boundary voltage matrix.

[0149] △Z(t1), △Z(t2), △Z(t3),…, △Z(t N ): t1, t2, t3, ..., t N The corresponding average impedance change value.

[0150] AISI: Action-Impedance Co-index.

[0151] Var(JSD): JSD(t1), JSD(t2),...JSD(t N The variance of ).

[0152] Var(△Z): △Z(t1), △Z(t2), △Z(t3),…, △Z(t N The variance of ).

[0153] ΔV(t1), ΔV(t2),…, ΔV(t N ): t1, t2, ..., t N The boundary voltage difference matrix between time t1 and time t0. Δσ(t1), Δσ(t2), ..., Δσ(t... N ): t1, t2, ..., t N The relative conductivity matrix between time t0 and time t0.

[0154] Δσ norm (t1), Δσ norm (t2), ...Δσ norm (t N ): t1, t2, ..., t N The relative conductivity matrix between time t0 and time t0.

[0155] max{Δσ(t j )}:Matrix Δσ(t j The maximum value in ).

[0156] mean(Δσ) norm (t j )): Matrix Δσ norm (t j The average value of the elements of ).

[0157] N: The number of consecutive bicep curls.

[0158] The above-described embodiments are preferred embodiments of the present invention and are only used to facilitate the illustration of the present invention. They are not intended to limit the present invention in any way. Any person skilled in the art who makes local modifications or alterations to the technical content disclosed in the present invention without departing from the scope of the technical features of the present invention shall still fall within the scope of the technical features of the present invention.

Claims

1. A method for evaluating upper arm muscle rehabilitation exercises, characterized in that, Includes the following steps: S100, N consecutive bicep curl movement periods are [t0, t1], [t1, t2], [t2, t3], …, [t N-1 , t N ];get t1, t2, …, t N corresponding JSD (t1), JSD (t2), …, JSD (t N ); N is a natural number greater than or equal to 20; It includes the following sub-steps: S101, time acquisition from t 起始 To t 末尾 The wrist joint trajectory relationship curve tx(t), where t1-t 起始 >w / 2,t 末尾 -t N >w / 2; S102, filter the tx(t) curve in S101 to obtain the smoothed time-wrist joint trajectory curve t-xlp(t), where xlp(t) is the smoothed wrist joint trajectory; S103, calculate the time-error relationship curve te(t): e(t) = x(t) - xlp(t); S104, calculate t1, t2, ..., t N The degree of wrist joint trajectory fluctuation JSD(t1), JSD(t2), ..., JSD(t) N ); Any t j JSD(t) corresponding to time t j The following formula is used for calculation: ； w represents the time sliding window, and j is any natural number from 1 to N; S200, obtain the values ​​at t0, t1, t2, t3, ..., t4 based on the same excitation frequency. N The boundary voltage matrices corresponding to time points are V(t0), V(t1), V(t2), V(t3), ..., V(t) N ); S300, based on the data obtained from S200, acquires t1, t2, t3, ..., t N The average impedance changes at time points ΔZ(t1), ΔZ(t2), ΔZ(t3), ..., ΔZ(t) are: N ); S400, solve for the action-impedance compatibility index AISI: AISI={[JSD(t1)△Z(t1)+JSD(t2)△Z(t2)](t2-t1) / 2+[JSD(t2)△Z(t2)+JSD(t3)△Z(t3)](t3-t2) / 2+……+[JSD(t2) j-1 )△Z(t j-1 )+JSD(t j )△Z(t j )](the j -the j-1 ) / 2……+[JSD(t N-1 )△Z(t N-1 )+JSD(t N )△Z(t N )](the N -the N-1 ) / 2]} / {2N[Var(JSD) Var(△Z)] 0.5 }; Where Var(JSD) represents JSD(t1), JSD(t2), ..., JSD(t) N The variance of ) is represented by Var(ΔZ), where Var(ΔZ) represents ΔZ(t1), ΔZ(t2), ΔZ(t3), ..., ΔZ(t4). N The variance of ). S500, based on the muscle rehabilitation assessment results provided by AISI: If AISI ≥ 0.80, it indicates muscle rehabilitation; If 0.80 > AISI ≥ 0.60, it indicates mild muscle damage; If 0.60 > AISI, it indicates moderate to severe muscle damage.

2. The method for evaluating upper arm muscle rehabilitation exercises according to claim 1, characterized in that, The value of w ranges from [0.2s, 0.3s].

3. The method for evaluating upper arm muscle rehabilitation exercises according to claim 1, characterized in that, Step S300 includes the following sub-steps: S301, calculate t1, t2, ..., t N The boundary voltage difference matrix between time t0 and time t1: ΔV(t1), ΔV(t2), ..., ΔV(t) N ); Any t j Boundary voltage difference matrix ΔV(t) at time t j ): ΔV(t) j )=V(t j )-V(t0), where j is any natural number from 1 to N; S302, calculate t1, t2, ..., t N The relative conductivity matrices Δσ(t1), Δσ(t2), ..., Δσ(t0) at time t0 and time t0 N ); S303, Solve for the normalized values ​​t1, t2, ..., t N The relative conductivity matrix Δσ between time t0 and time t0 norm (t1), Δσ norm (t2), ...Δσ norm (t N ); Δσ norm (t j )=Δσ(t j ) / max{Δσ(t j )},max{Δσ(t j )} represents the matrix Δσ(t) j The maximum value in ); S304, solve for △Z(t1), △Z(t2), △Z(t3), ..., △Z(t4). N ); At any time t j The average impedance change value ΔZ(t) j ): ΔZ(t) j )=mean(Δσ norm (t j ()), mean means to calculate the average.

4. A method for evaluating upper arm muscle rehabilitation exercises according to claim 1, 2, or 3, characterized in that, N is 20, 25, or 30.

5. An upper arm muscle rehabilitation exercise evaluation system, characterized in that, It includes: a storage module, a JSD solution module, an average impedance change value solution module, and an AISI solution module; The storage module stores the wrist joint trajectory relationship curve tx(t) and the curves at t0, t1, t2, t3, ..., t4 based on the same excitation frequency. N The boundary voltage matrices corresponding to time points are V(t0), V(t1), V(t2), V(t3), ..., V(t) N ); t0, t1, t2, t3,…,t N The meaning is: N consecutive bicep curl cycles are respectively represented by [t0,t1], [t1,t2], [t2,t3], ..., [t N-1 ,t N ]express; The JSD solver module is used to solve for t1, t2, ..., t N The corresponding JSD(t1), JSD(t2), ..., JSD(t) N The solution method is as follows: Step A, the JSD solver module reads time from t 起始 To t 末尾 The wrist joint trajectory relationship curve tx(t), where t1-t 起始 >w / 2,t 末尾 - t N >w / 2; Step B: Filter the tx(t) curve to obtain the smoothed time-wrist joint trajectory curve t-xlp(t), where xlp(t) is the smoothed wrist joint trajectory; Step C, calculate the time-error relationship curve te(t): e(t) = x(t) - xlp(t); Step D: Calculate t1, t2, ..., t N The degree of wrist joint trajectory fluctuation JSD(t1), JSD(t2), ..., JSD(t) N ); any t j JSD(t) corresponding to time t j The following formula is used for calculation: w represents the time sliding window, and j is any natural number from 1 to N; The average impedance change calculation module reads V(t0), V(t1), V(t2), V(t3), ..., V(t... N Solve for t1, t2, t3, ..., t N The average impedance changes at time points ΔZ(t1), ΔZ(t2), ΔZ(t3), ..., ΔZ(t) are: N ); The AISI solver module is used to solve AISI problems, and its solution method is as follows: AISI={[JSD(t1)△Z(t1)+JSD(t2)△Z(t2)](t2-t1) / 2+[JSD(t2)△Z(t2)+JSD(t3)△Z(t3)](t3-t2) / 2+……+[JSD(t j-1 )△Z(t j-1 )+JSD(t j )△Z(t j )](t j -t j-1 ) / 2……+[JSD(t N-1 )△Z(t N-1 )+JSD(t N )△Z(t N )](t N -t N-1 ) / 2]} / {2N[Var(JSD) Var(△Z)] 0.5 }; where Var(JSD) represents JSD(t1), JSD(t2), ..., JSD(t) N The variance of ) is represented by Var(ΔZ), where Var(ΔZ) represents ΔZ(t1), ΔZ(t2), ΔZ(t3), ..., ΔZ(t4). N The variance of ).

6. The upper arm muscle rehabilitation exercise evaluation system according to claim 5, characterized in that, The solution method of the average impedance change value calculation module is as follows: Step A: Calculate t1, t2, ..., t N The boundary voltage difference matrix between time t0 and time t1: ΔV(t1), ΔV(t2), ..., ΔV(t) N ); Any t j Boundary voltage difference matrix ΔV(t) at time t j ): ΔV(t) j )=V(t j )-V(t0), where j is any natural number from 1 to N; Step B: Calculate t1, t2, ..., t N The relative conductivity matrices Δσ(t1), Δσ(t2), ..., Δσ(t0) at time t0 and time t0 N ); Step C: Solve for the normalized values ​​t1, t2, ..., t N The relative conductivity matrix Δσ between time t0 and time t0 norm (t1), Δσ norm (t2), ...Δσ norm (t N ); Δσ norm (t j )=Δσ(t j ) / max{Δσ(t j )},max{Δσ(t j )} represents the matrix Δσ(t) j The maximum value in ); Step D: Solve for △Z(t1), △Z(t2), △Z(t3), ..., △Z(t) N ); at any time t j The average impedance change value ΔZ(t) j ): ΔZ(t) j )=mean(Δσ norm (t j ()), mean means to calculate the average.

7. The upper arm muscle rehabilitation exercise evaluation system according to claim 5, characterized in that, Also includes: The assessment module provides a muscle rehabilitation assessment result based on the AISI obtained by the AISI solving module: if AISI ≥ 0.80, it indicates muscle rehabilitation; if 0.80 > AISI ≥ 0.60, it indicates mild muscle injury; if 0.60 > AISI, it indicates moderate to severe muscle injury.

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