Ankle Joint Rehabilitation Exercise Evaluation Method Based on Force Time Series

Abstract

The object of the present invention is to provide a method for evaluating ankle joint rehabilitation exercises based on force time series. First, a coupled model of an ankle joint rehabilitation robot simulation model and a human body model is established in a biomechanical simulation software, and the rehabilitation exercises are planned using this coupled model. Secondly, a prototype of the ankle joint rehabilitation robot is used to perform the planned rehabilitation exercises, and the surface electromyography signals of relevant muscles during the rehabilitation exercises are collected using an electromyograph. Then, wavelet transform is used to denoise the collected surface electromyography signals, and the active segments of the surface electromyography signals are detected. Finally, the force time series of this rehabilitation exercise is obtained by combining the active segments of each muscle, and this rehabilitation exercise is evaluated by comparing it with the force time series during normal gait of healthy people.

Description

Technical Field

[0001] The present invention belongs to the technical field of ankle rehabilitation robot control, and particularly relates to a method for evaluating ankle rehabilitation movement based on force time series. Background Art

[0002] Ankle injury is one of the most common diseases in life. With the development of robot technology, many ankle rehabilitation robots have emerged on the market to assist patients in rehabilitation exercises. Existing research mainly focuses on mechanism design, driving methods, and control strategies, and pays less attention to whether the rehabilitation exercises performed by patients using ankle rehabilitation robots are reasonable, which easily leads to poor rehabilitation effects for patients or abnormal walking after the ankle strength is restored. Therefore, the selection of ankle rehabilitation exercises is very important, and it is particularly crucial to evaluate ankle rehabilitation exercises and then feed back to the control of the robot.

[0003] Force time series refers to the sequence of the force application time and duration of related muscles during movement. Summary of the Invention

[0004] For the ankle rehabilitation movement targeted by the present invention, the force time series of the related muscles when the patient uses the ankle rehabilitation robot for rehabilitation movement is compared with the force time series of the same muscles when a healthy person walks with a normal gait. If the results are similar, it can be considered that the ankle rehabilitation movement at this time is beneficial to the patient's rehabilitation; if the results are quite different, it can be considered that the ankle rehabilitation movement at this time is not beneficial to the patient's rehabilitation and may lead to abnormal gait in the patient after the ankle strength is restored.

[0005] The above is the basic principle based on which the present invention is designed.

[0006] In view of the fact that there are few evaluation methods for the rationality of ankle rehabilitation movement in current research, the present invention proposes a method for evaluating ankle rehabilitation movement based on force time series, which is used as the basis for selecting ankle rehabilitation movement by evaluating the rehabilitation movement during the use of the ankle rehabilitation robot.

[0007] In the solution of the present invention, first, a coupled model of an ankle rehabilitation robot simulation model and a human body model is established in a biomechanical simulation software, and the coupled model is used for the planning of rehabilitation movement. Secondly, a prototype of the ankle rehabilitation robot is used to perform the planned rehabilitation movement, and the surface electromyographic signals of the related muscles during the rehabilitation movement are collected by using an electromyograph. Then, wavelet transform is used to perform noise reduction processing on the collected surface electromyographic signals, and the active segments of the surface electromyographic signals are detected. Finally, the force time series of the rehabilitation movement is obtained by combining the active segments of each muscle, and the rehabilitation movement is evaluated by comparing it with the force time series during normal gait of a healthy person.

[0008] The technical solution specifically adopted by the present invention to solve its technical problems is as follows:

[0009] An ankle joint rehabilitation exercise evaluation method based on force time series, characterized by comprising the following steps:

[0010] Step S1: Establish a coupled model of an ankle joint rehabilitation robot simulation model and a human body model in a biomechanical simulation software to plan rehabilitation exercises;

[0011] Step S2: Use a prototype of an ankle joint rehabilitation robot to perform the planned rehabilitation exercises, and use an electromyograph to collect the surface electromyographic signals of relevant muscles during the rehabilitation exercises;

[0012] Step S3: Use wavelet transform to denoise the collected surface electromyographic signals and detect the active segments of the surface electromyographic signals;

[0013] Step S4: Combine the active segments of each muscle to obtain the force time series of the rehabilitation exercise, and evaluate the rehabilitation exercise by comparing it with the force time series during normal gait of healthy people.

[0014] Further, in step S1, an ankle joint rehabilitation robot with a 2-SPU / RR parallel mechanism is used. Through a plug-in in Solidworks, the model of the ankle joint rehabilitation robot is imported into the biomechanical simulation software AMS to establish a coupled model with the human body model, and redundant constraints in the ankle joint rehabilitation robot model are removed; the simulation motion of the coupled model is performed by setting a driving function, and biomechanical results are output.

[0015] Further, in step S2, the physical prototype of the ankle joint rehabilitation robot is used to perform the planned motion, and the surface electromyographic signals of relevant muscles during the motion are collected; according to the functions of the muscles related to the ankle joint, the tibialis anterior muscle, peroneus brevis muscle, gastrocnemius muscle, and soleus muscle are selected for collecting surface electromyographic signals.

[0016] Further, in step S3:

[0017] Use wavelet transform to denoise the collected surface electromyographic signals, where the wavelet basis function is sym5, the decomposition level is 5 layers, the threshold rule adopts a universal threshold, and the threshold is adjusted according to the noise estimation of different layers;

[0018] The universal threshold formula is:

[0019] where n is the signal length and σ is the noise intensity;

[0020] Detect the active segments of the signal after denoising through an active segment detection function. If the amplitude of the signal is greater than the threshold, set it to 1, otherwise set it to 0;

[0021] The detection function is as follows:

[0022] where s(n) is the active segment detection function and x′(n) is the signal after noise reduction;

[0023] Considering that the EMG signal is prone to generating spike signals due to the influence of noise, an effective active signal interval time T is set, and the time interval T0 between adjacent active signals is detected. If T0 > T, this signal is removed as a noise spike signal; if T0 < T, it is retained.

[0024] The start time and duration of the muscle exertion are obtained by detecting the active segment of the surface EMG signal.

[0025] Furthermore, in step S4: the active segment of the muscle is used to represent when the muscle starts to be activated and the activation duration during the rehabilitation exercise using the ankle rehabilitation robot, and the activity representations of multiple muscles in one exercise are combined to obtain the force time series of each muscle;

[0026] By comparing the force time series of each muscle during the movement process with the force time series of the same muscle during normal gait walking, the planned movement is evaluated. If the force time series results are similar, it indicates that the movement is beneficial to the rehabilitation of the ankle joint and will not have an adverse impact on the patient's gait; if the force time series results differ greatly, it indicates that the movement is not conducive to the rehabilitation of the ankle joint and may lead to abnormal gait of the patient, thereby guiding the rehabilitation movement planning of the ankle rehabilitation robot.

[0027] Compared with the prior art, the present invention and its preferred solutions can evaluate the rehabilitation movement during the use of the ankle rehabilitation robot and serve as the basis for selecting the ankle rehabilitation movement. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The present invention will be further described in detail below with reference to the drawings and specific embodiments:

[0029] Figure 1 is a schematic flow chart of the method in the embodiment of the present invention.

[0030] Figure 2 is a schematic structural diagram of the ankle rehabilitation robot using a 2-SPU / RR parallel mechanism in the embodiment of the present invention.

[0031] Figure 3 is a schematic diagram of the established human-machine coupling model in the embodiment of the present invention.

[0032] Figure 4 is a schematic diagram of collecting surface EMG signals during the rehabilitation movement in the embodiment of the present invention.

[0033] Figure 5 It is a schematic diagram of the start time and duration of the force exerted by a muscle in an embodiment of the present invention.

[0034] Figure 6 It is a force time sequence diagram in the rehabilitation exercise in an embodiment of the present invention.

[0035] Figure 7 It is a force time sequence diagram of a healthy person under normal gait in an embodiment of the present invention. Detailed implementation manners

[0036] To make the features and advantages of this patent more obvious and understandable, specific embodiments are given below and described in detail as follows:

[0037] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs.

[0038] It should be noted that the terms used here are only for describing specific implementation manners and are not intended to limit the exemplary implementation manners according to the present application. As used here, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "include" and / or "comprise" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0039] The implementation flowchart of the technical solution of the embodiment of the present invention is as Figure 1 shown, and each step is introduced as follows:

[0040] (1) Establish a coupled model of the ankle rehabilitation robot simulation model and the human body model in the biomechanical simulation software, and use this coupled model to plan the rehabilitation exercise.

[0041] (2) Use the prototype of the ankle rehabilitation robot to perform the planned rehabilitation exercise, and use an electromyograph to collect the surface electromyographic signals of the relevant muscles during the rehabilitation exercise.

[0042] (3) Use wavelet transform to denoise the collected surface electromyographic signals and detect the active segments of the surface electromyographic signals.

[0043] (4) Combine the active segments of each muscle to obtain the force time sequence of this rehabilitation exercise, and evaluate this rehabilitation exercise by comparing it with the force time sequence of a healthy person during normal gait.

[0044] By comparing the force time series of relevant muscles during the ankle joint rehabilitation exercise with the force time series during normal gait of healthy people, the rationality of the planned ankle joint rehabilitation exercise can be evaluated, avoiding the situation where the selected rehabilitation exercise causes the patient to have a deformed gait.

[0045] Specifically, the design purpose of this embodiment is mainly to collect surface electromyography signals of relevant muscles during the operation of the planned rehabilitation exercise using an ankle joint rehabilitation robot, detect the active segments of the collected signals to obtain the force time series of this rehabilitation exercise, and use this force time series to evaluate this rehabilitation exercise. The key points include the following:

[0046] (1) Establish a coupled model of the ankle joint rehabilitation robot simulation model and the human body model in a biomechanical simulation software, and use this coupled model to plan the rehabilitation exercise.

[0047] This embodiment uses an ankle joint rehabilitation robot with a 2-SPU / RR parallel mechanism. The specific structure is as Figure 2 shown. This mechanism is composed of a fixed base (10), a moving platform (7), two SPU parallel branches, and a constraint branch. The constraint branch is an RR branch composed of a U-shaped frame (2) and support frames (8, 8'). The axes of the two revolute pairs intersect at the rotation center O of the mechanism. This branch restricts the ankle joint rehabilitation robot to rotate only around the x-axis and z-axis, that is, it can only achieve plantar flexion / dorsiflexion and inversion / eversion movements of the ankle, and the constraint branch is connected to the moving platform through a connecting rod (1). The two parallel SPU branches are respectively composed of spherical hinges (3, 3'), stepping push rods (4, 4'), and U-pairs (9, 9'), and the two SPU branches are respectively connected to the moving platform through Z-shaped connecting rods (5, 5'). By adjusting the height of the lifting platform (6), the rotation center of the ankle can be adjusted to be approximately coincident with the rotation center of the ankle joint rehabilitation robot.

[0048] Through the plug-in in Solidworks, the model of this ankle joint rehabilitation robot is imported into the biomechanical simulation software AMS to establish a coupled model with the human body model. It should be noted to remove the redundant constraints in the ankle joint rehabilitation robot model. The established human-machine coupled model is as Figure 3 shown. By setting the driving function to perform the simulation movement of the coupled model, the setting of the driving function is convenient, so it is easy to plan different movement trajectories in the biomechanical simulation software. At the same time, the simulation software can perform inverse dynamics analysis and output biomechanical results, which is beneficial to the preliminary evaluation of the planned trajectory.

[0049] (2) Use the prototype of the ankle joint rehabilitation robot to perform the planned rehabilitation exercise, and use an electromyograph to collect the surface electromyography signals of relevant muscles during the rehabilitation exercise.

[0050] The ankle rehabilitation robot used in this embodiment has two degrees of freedom and can assist the ankle in performing two rehabilitation exercises: plantar flexion / dorsiflexion and inversion / eversion. Therefore, the corresponding rehabilitation exercise planned for this time starts from the dorsiflexion position and makes a combined movement of plantar flexion / dorsiflexion and inversion / eversion counterclockwise. Considering the range of motion of the ankle joint, the maximum angles that can be reached by plantar flexion, dorsiflexion, inversion, and eversion are set to 15°. The physical prototype of the ankle rehabilitation robot is used to perform the planned movement, and the surface electromyography (sEMG) signals of the relevant muscles during the movement are collected. According to the functions of the muscles related to the ankle joint, the tibialis anterior muscle, peroneus brevis muscle, gastrocnemius muscle, and soleus muscle are selected for collecting the sEMG signals. The collection of the sEMG signals during the rehabilitation exercise is as Figure 4 shown.

[0051] (3) Use wavelet transform to denoise the collected sEMG signals and detect the active segments of the sEMG signals.

[0052] Use wavelet transform to denoise the collected sEMG signals, where the wavelet basis function is sym5, the decomposition level is 5 layers, the threshold rule uses the universal threshold, and the threshold is adjusted according to the noise estimation of different layers.

[0053] The universal threshold formula is:

[0054] where n is the signal length and σ is the noise intensity.

[0055] Detect the active segments of the denoised signals through the active segment detection function. If the amplitude of the signal is greater than the threshold, set it to 1; otherwise, set it to 0.

[0056] The detection function is:

[0057] where s(n) is the active segment detection function and x′(n) is the denoised signal.

[0058] Considering that the EMG signals are prone to generating spike signals affected by noise, set an effective active signal interval time T, and detect the time interval T0 between adjacent active signals. If T0 > T, then remove this signal as a noise spike signal; if T0 < T, then retain it.

[0059] By detecting the active segments of the sEMG signals, the start time and duration of the muscle force can be obtained, as Figure 5 shown. From top to bottom are the original EMG signal, the denoised EMG signal, the result after threshold processing, and the representation of the muscle active segment.

[0060] (4) Combine the active segments of each muscle to obtain the force time series of this rehabilitation exercise, and evaluate this rehabilitation exercise by comparing it with the force time series during normal gait of healthy people.

[0061] The active segments of the muscles can indicate when the muscles start to be activated and the duration of activation during the rehabilitation exercise using the ankle rehabilitation robot. By combining the activity representations of multiple muscles in one movement, the force timings of each muscle can be seen. By comparing the force timings of each muscle during the movement process with the force timings of the same muscles during normal gait walking, the planned movement can be evaluated. If the force timing results are similar, it indicates that the movement is beneficial for ankle rehabilitation and will not have an adverse impact on the patient's gait; if the force timing results differ significantly, it indicates that the movement is not conducive to ankle rehabilitation and may lead to abnormal gait in the patient. The force timings of the four selected muscles in the planned rehabilitation movement and the force timings of healthy people during normal gait are respectively as Figure 6 and Figure 7 shown. It can be observed that the two are relatively close, indicating that the planned rehabilitation movement is relatively reasonable and beneficial for ankle rehabilitation.

[0062] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0063] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0064] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0065] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the steps specified in one process or multiple processes and / or blocks Figure 1 one process or multiple processes and / or blocks Figure 1 or functions specified in one block or multiple blocks.

[0066] As described above, it is only a preferred embodiment of the present invention, and it is not a limitation of the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes. However, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

[0067] This patent is not limited to the above best mode. Anyone can derive various other forms of the ankle joint rehabilitation motion evaluation method based on force time series under the inspiration of this patent. All equal changes and modifications made according to the scope of the patent application of the present invention shall fall within the scope covered by this patent.

Claims

1. An ankle joint rehabilitation exercise evaluation method based on force time series, characterized in that, Including the following steps: Step S1: Establish a coupled model of the ankle rehabilitation robot simulation model and the human body model in the biomechanical simulation software to plan the rehabilitation movement; Step S2: Use the prototype of the ankle rehabilitation robot to perform the planned rehabilitation movement, and collect the surface electromyogram signals of the relevant muscles during the rehabilitation movement by using an electromyograph; Step S3: Use wavelet transform to denoise the collected surface electromyogram signals, and detect the active segments of the surface electromyogram signals; Step S4: Combine the active segments of each muscle to obtain the force time series of the rehabilitation movement, and evaluate the rehabilitation movement by comparing it with the force time series of a healthy person during normal gait; In step S1, use an ankle rehabilitation robot with a 2-SPU / RR parallel mechanism. Through the plug-in in Solidworks, import the model of the ankle rehabilitation robot into the biomechanical simulation software AMS, establish a coupled model with the human body model, and remove the redundant constraints in the ankle rehabilitation robot model; perform the simulation movement of the coupled model by setting the driving function and output the biomechanical results; In step S3: Use wavelet transform to denoise the collected surface electromyogram signals, where the wavelet basis function is sym5, the decomposition level is 5 layers, the threshold rule adopts the universal threshold, and the threshold is adjusted according to the noise estimation of different layers; The general threshold formula is as follows: where n is the signal length and σ is the noise intensity; Detect the active segments of the signal after denoising through the active segment detection function. If the amplitude of the signal is greater than the threshold, set it to 1, otherwise set it to 0; The detection function is as follows: where s(n) is the active segment detection function and x′(n) is the denoised signal; Considering that the electromyogram signal is prone to generate spike signals affected by noise, set an effective active signal interval time T, and detect the time interval T0 between adjacent active signals. If T0 > T, remove this signal as a noise spike signal. If T0 < T, retain it; Obtain the start time and duration of the muscle exertion by detecting the active segments of the surface electromyogram signals.

2. The ankle joint rehabilitation exercise evaluation method based on force time series according to claim 1, wherein In step S2, apply the physical prototype of the ankle rehabilitation robot to perform the planned movement, and collect the surface electromyogram signals of the relevant muscles during the movement; according to the functions of the ankle-related muscles, select the tibialis anterior muscle, peroneus brevis muscle, gastrocnemius muscle, and soleus muscle to collect the surface electromyogram signals.

Images