CN116687429A - Muscle real-time monitoring and analyzing system based on lower limb exoskeleton robot - Google Patents
Muscle real-time monitoring and analyzing system based on lower limb exoskeleton robot Download PDFInfo
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Abstract
The invention discloses a muscle real-time monitoring and analyzing system based on a lower limb exoskeleton robot, belonging to the technical field of medical health detection instruments, comprising: the device comprises a lower limb exoskeleton body, a surface myoelectric sensor, an acceleration signal sensor, a small display, an alarm sound generating device and a data acquisition and analysis device; the surface myoelectric sensor is used for collecting surface myoelectric signals of the lower limb muscle groups; the acceleration signal sensor is used for measuring the acceleration value of the lower leg during walking; the data acquisition and analysis device is used for acquiring and processing the electromyographic signals and the acceleration values, and calculating and outputting the current muscle fatigue degree and gait stability according to the electromyographic signals and the acceleration values; the display screen and the sounding device are used for prompting a user in time when the system detects that the muscle enters a fatigue state or gait is unstable at the moment.
Description
Technical Field
The invention belongs to the technical field of medical health detection instruments, and particularly relates to a muscle real-time monitoring and analyzing system based on a lower limb exoskeleton robot and a using method thereof.
Background
According to the Chinese stroke report, in 2019, the new stroke of China reaches 394 tens of thousands, the total number of stroke patients exceeds 2876 tens of thousands, according to incomplete statistics, only 5% -20% of patients after the stroke can be completely recovered, and 55% -75% of patients still have serious limb dysfunction after 6 months.
Hemiplegia rehabilitation is a long process generally, and many hemiplegia patients can only choose to accept short-term treatment or perform rehabilitation training at home in the face of a tight medical resource, but such training is difficult to standardize, and treatment results are difficult to quantify, so that an accurate improvement and optimization treatment scheme cannot be achieved, and the actual rehabilitation effect is influenced. Therefore, further research and improvement of the existing therapeutic rehabilitation method for patients with cerebrovascular disease sequelae are particularly important and urgent.
The human gait refers to the posture and the motion of a person when walking, and the normal walking gait has the characteristics of coordination, balance and periodicity. Gait analysis is recognized as an effective assessment method in the field of human exercise rehabilitation research, and can sufficiently quantify the exercise behavior of walking. Clinical gait analysis aims at revealing key links and influencing factors of gait abnormality through biomechanics and kinematics means, so as to assist rehabilitation evaluation and treatment and complete relevant clinical diagnosis, curative effect evaluation, mechanism research and the like of patients.
The lower limb exoskeleton robot is a device which can be worn on the lower limb of a human body and is used for assisting a person with disabled lower limb to recover walking ability. For people with partial loss of the lower limb exercise ability, the lower limb exoskeleton can also play an obvious auxiliary rehabilitation role. When a hemiplegic patient wearing lower limb exoskeleton carries out rehabilitation training by means of the hemiplegic patient wearing lower limb exoskeleton, fatigue is easy to generate and the patient is not self-aware because of weak muscle capacity, if the rehabilitation training is continued under the condition, bad influences such as gait instability are easy to generate, and negative effects are generated on the whole rehabilitation process. Therefore, the set of process of adding the fatigue detection device to the lower limb exoskeleton, processing and comparing the collected myoelectricity data and timely feeding back the result to the user has obvious practical value. The current common way to detect muscle fatigue is to collect muscle surface electromyographic signals, calculate the average frequency or spectrum distance, and define the fatigue degree of the muscle according to the numerical variation of the average frequency and spectrum distance.
This approach has been applied in numerous examples.
The gait detection of the patient can be completed by means of the surface electromyographic signals of the lower limb muscle groups and the shank flexion and extension acceleration, the current walking state of the patient is simulated, and the bad gait detection of the patient can be completed by comparing the trends of the gait of the patient each time and comparing the trends with the normal gait. The technology is widely used in the rehabilitation field at present.
Xie Longhan et al in China, the invention discloses a multi-mode sensing system (patent number: CN 114366559A) for a lower limb rehabilitation robot, which comprises a plurality of sensors, and the state sensing of the lower limb movement is completed by collecting various human body data. However, the patent does not use the lower limb electromyographic signals as the main parameters for gait detection, and does not include the functions of bad gait real-time detection and warning. ( The content is added: the robot needs to use the exoskeleton robot for rehabilitation training, most of patients with serious disability of lower limbs have relatively weak functions of nerves and muscles of the lower limbs, the sensing capability and the control capability of the lower limbs are poor, if the robot does not have the functions of detecting and alarming bad gait in real time, the conditions of unconsciousness, unstable gait and unconsciousness in the rehabilitation training process can possibly occur, if the rehabilitation training is continued under the conditions, the rehabilitation treatment effect of the patients is greatly reduced, and the robot can even produce a reaction after a long time. )
Chen Sai the step counting and movement state evaluation device mentioned in the Chinese patent publication (patent No. CN 105561567A) integrates a myoelectric sensor and a movement sensor, and can select a wearing position according to the application way of a user to complete the effect of monitoring the current movement state. However, the patent does not fully consider the actual needs of severely disabled lower limb individuals using lower limb exoskeleton robots for rehabilitation training. Firstly, a set of independent systems is not deeply integrated with the lower limb exoskeleton robot, in which case, if the system is used in combination with the lower limb exoskeleton robot, the sensor may shake or squeeze due to structural differences of the exoskeleton, and the monitoring effect may be seriously affected; and each time the patient needs to charge and maintain two sets of systems of the exoskeleton and the evaluation device, the patient with difficulty in limb movement is more troublesome. In the data noise reduction process, the Butterworth noise reduction method is adopted in the embodiment, and the Kalman filtering method is adopted in the invention.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. A real-time monitoring and analyzing system for muscles based on a lower limb exoskeleton robot is provided. The technical scheme of the invention is as follows:
a real-time monitoring and analyzing system for muscles based on a lower limb exoskeleton robot, comprising: the device comprises a lower limb exoskeleton body, a surface myoelectric sensor, an acceleration signal sensor, a small display, an alarm sound generating device and a data acquisition and analysis device; wherein,,
the lower limb exoskeleton organism is worn on the lower body of a user and is used for assisting the user to walk and perform lower limb
Exercise capacity rehabilitation training; the surface myoelectric sensor is used for detecting surface myoelectric signals of the lower limb muscle groups; the acceleration signal sensor is used for collecting acceleration during each stepping; the small display is used for displaying the current muscle fatigue and gait stability; the alarm sound generating device is integrated with the display and is used for generating alarm sound to remind a user when the system detects muscle fatigue or gait instability; the data acquisition and analysis device is used for collecting and storing electromyographic signals and acceleration signals, finishing data processing through methods of noise reduction, signal segmentation, characteristic value extraction and the like, comparing the processed data with the data of a healthy person and the near-several rehabilitation training data of the patient in real time, transmitting the processing result to the display and the sounding device in real time, and fully integrating the elements with the exoskeleton of the lower limb except the data acquisition and analysis device, wherein the exoskeleton is used for uniformly supplying power.
Further, the surface myoelectric sensors are eight in number and are respectively adhered to the tibialis anterior muscle, gastrocnemius muscle, rectus femoris muscle and tendons of biceps femoris muscle on the left side and the right side of a user.
Furthermore, the acceleration signal sensors are two in number and are respectively arranged in the midget of the lower leg at the left side and the right side of the user;
the small display is worn between the wrists of the user.
Further, after the data acquisition and analysis device receives the electromyographic signals and the acceleration values, noise reduction processing is firstly carried out, interference information is filtered, then characteristic value extraction is carried out, and the current fatigue degree of a patient is defined through the change of the characteristic values;
when a patient walks each step, the electromyographic signals will have corresponding wave peaks, the characteristic values extracted by the system are mainly the average frequency at the moment, and after a proper amount of movement, the average frequency will gradually show a trend of decreasing, so that the muscle can be understood to gradually enter a fatigue state;
in addition, the spectral distance of the electromyographic signals is calculated, and the index is taken as a main parameter for judging the muscle fatigue degree together with the average frequency.
Further, the extracting the characteristic value specifically includes:
the electromyographic signals acquired by using the electromyographic patch are interfered by 50hz power frequency noise, and the electromyographic signals are eliminated by using a single frequency trap in an IIR filter;
in the signal acquisition process, a baseline drift phenomenon occurs to low-frequency interference signals, the low-frequency signals are called as trend terms of the signals, and a smoothing filter is used for smoothing the signals to eliminate the trend terms;
after finishing the power frequency noise reduction and removing trending, the original signal is required to be subjected to global noise reduction, and a plurality of global noise reduction methods are adopted, wherein a Kalman filtering method is adopted;
the original electromyographic signal is output as a continuous long section, and in order to realize the purpose of monitoring data in real time, the electromyographic signal is subjected to sliding window data segmentation, wherein the window length is set to be 300ms in the example;
after the noise reduction and the segmentation are completed, extracting the characteristic value of each segment of data, wherein the characteristic value comprises an average frequency and a spectrum distance;
research shows that the fatigue degree of muscle is increased, the main frequency of the power spectrum of the surface electromyographic signal is changed from high to low, the average power value is gradually reduced, and the greater the fatigue degree is, the stronger the descending trend is.
The average frequency calculation formula isWhere f is the frequency and fs is the sampling rate.
The method for calculating the spectrum distance of the electromyographic signals further comprises the following steps of:
k-based spectral distance M k Is that
Spectral distance is
Furthermore, the acquired acceleration value is used for gait segmentation, and in the walking process, each heel landing is a peak of acceleration, and a part between two peak points can be used as a gait cycle;
after the acceleration value of the middle part of the calf of the patient is acquired in real time, the acceleration value is quickly transmitted to a data analysis device and is used as auxiliary data of gait analysis to be added into the operation process;
the cooperative analysis of the lower limb muscles can be completed through waveforms of the muscle electromyographic signals of each group and gait segmentation results obtained through acceleration analysis, and the whole gait acquisition process is completed through the results;
each group of obtained gait data is stored as important supplementary data, and is immediately compared with the gait of the normal person stored in the data analysis device, and meanwhile, each group of gait data of the patient is compared in real time;
when the detected gait of the patient is greatly different from the normal gait or is obviously changed from the initial gait of the patient, the system judges the gait as suspected bad gait, the current condition is immediately displayed on a display, and meanwhile, the sounding device also immediately gives an alarm to prompt the patient to pay attention. Furthermore, the surface myoelectric sensor has the characteristics of high efficiency, easiness in use and accuracy in acquisition, can meet the sampling frequency of 2000hz, and adopts PICO series products produced by Cometa company; the acceleration signal sensor can meet the sampling frequency of 1000hz, and a BMA253 type acceleration sensor produced by BOSCH company is adopted; the display needs to be worn on the wrist of the patient, so the display has the characteristics of small size and portability;
the sound generating device needs to effectively generate prompt sound in time, so the sound generating device needs to have the characteristics of bright and clear sound and can be integrated with a display; the data acquisition and analysis device should have a strong computing power and a large data storage space. Except for the data acquisition and analysis device, the rest parts are fully integrated with the lower limb exoskeleton robot, and the exoskeleton robot supplies power to the lower limb exoskeleton robot.
Furthermore, when the patient walks each step, the electromyographic signals will have corresponding wave peaks, the characteristic values extracted by the system are mainly the average frequency at the moment, and after a proper amount of movement, the average frequency will gradually show a trend of decreasing, namely, the muscles gradually enter a fatigue state; in addition, the spectrum distance of the electromyographic signals is calculated, and the index is taken as a main parameter for judging the muscle fatigue degree together with the average frequency;
when the muscle group is detected to enter a fatigue state, the system sends a prompt, and a prompt signal is immediately transmitted to the display screen and the sounding device.
The invention has the advantages and beneficial effects as follows:
1) The perception early warning system provided by the invention can be used together with an exoskeleton robot, and can be used for carrying out real-time condition detection and prompt of bad gait for a patient.
2) The invention constructs a brand new steady-state system based on electromyographic signal detection, which has the functions of muscle fatigue analysis and gait acquisition and comparison through data processing.
3) The invention can simultaneously take the functions of data acquisition and analysis into consideration, and can effectively improve the recovery effect of the lower limbs of the patient.
4) In the prior art, no gait real-time analysis detection system which is mainly used for myoelectric signal detection and is used in combination with an exoskeleton exists, and the current mainstream gait detection systems in the market mostly finish gait detection by using a high-precision industrial camera and taking three-position motion capture as a core, such as Qualisys and the like; at present, a gait detection system based on electromyographic signals, such as TeaCapiv, is also available, but is not fully combined with the exoskeleton of the lower limb, and cannot monitor and early warn the whole gait of a serious patient with most loss of walking function in the rehabilitation training process. Aiming at the defects of the prior art, the invention provides a brand-new gait real-time analysis and monitoring system aiming at the lower limb incapacitation crowd and taking muscle fatigue detection as a core. The technology can effectively collect and process the myoelectric signals on the surface of the lower limb muscle group and the stepping acceleration of the lower leg of the patient in real time, and judges the fatigue degree of the current patient according to the acquired data; the system has the sound prompt function to remind the patient to pay attention to the current self condition, stop or pause the rehabilitation exercise and start proper rest when obvious muscle fatigue and gait instability occur.
The invention fully considers the actual requirement of the lower limb disabled people on the basis of the scene of independently carrying out walking function rehabilitation training by means of the lower limb exoskeleton robot, and provides the muscle fatigue and gait stability monitoring system fused with the depth of the lower limb exoskeleton, compared with the prior art, the invention has the following two main advantages, firstly, the invention combines the monitoring system with the depth of the lower limb exoskeleton, simplifies the process of carrying out rehabilitation training by means of the lower limb exoskeleton to a greater extent, and provides more convenient selection for patients; the second point is that the present muscle fatigue degree and gait stability degree of the patient can be calculated and compared in real time, and when the state of the patient is poor, the patient can make a sound in time, and the pattern is displayed for reminding, so that the rehabilitation training efficiency of the patient can be effectively improved.
Drawings
Fig. 1 is a flow chart of a real-time monitoring and analyzing system for muscles based on a lower limb exoskeleton robot according to a preferred embodiment of the present invention.
Fig. 2 is a schematic diagram of an embodiment of the present invention.
Fig. 3 is a flowchart of the kalman filtering method according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and specifically described below with reference to the drawings in the embodiments of the present invention. The described embodiments are only a few embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
as shown in fig. 1, the gait real-time analysis and monitoring system taking muscle fatigue detection as a core comprises a lower limb exoskeleton body, a surface myoelectric sensor, an acceleration signal sensor, a small display, an alarm sound generating device and a data acquisition and analysis device.
The lower limb exoskeleton organism is worn on the lower body of a user and is used for assisting the user to walk and carrying out rehabilitation training on the exercise capacity of the lower limb.
The surface electromyographic sensor is used for detecting surface electromyographic signals of the lower limb muscle groups.
The acceleration signal sensor is used for collecting acceleration during each stepping.
The miniature display is used for displaying the current muscle fatigue and gait stability.
The alarm sound generating device is integrated with a display and is used for generating alarm sound to remind a user when the system detects muscle fatigue or gait instability.
The data acquisition and analysis device is used for collecting and storing electromyographic signals and acceleration signals, completing data processing and comparison, and transmitting the processing results to the display and the sounding device in real time.
The lower limb exoskeleton is worn on the lower limb of a patient, plays a role in assisting the walking of the patient, and can play a role in assisting the rehabilitation of a hemiplegic patient.
The system should be integrated on the lower extremity exoskeleton.
The surface myoelectric sensor should be adhered to the surface of the skin of a patient, and the myoelectric signals of each action are fully collected.
The surface myoelectric sensors are required to be eight in number and are respectively adhered to the tibialis anterior muscle, gastrocnemius muscle, rectus femoris muscle and tendons of biceps femoris muscle on the left side and the right side of a patient.
The surface electromyographic sensor can rapidly transmit electromyographic signals to the data acquisition and processing device.
The acceleration signal sensors are two in number and are respectively arranged at the middle positions of the left and right lower legs of the patient.
The acceleration sensor is used for detecting the shank acceleration during each stepping, transmitting measured data to the data analysis device, and assisting in calculating the step length and the gait cycle so as to improve the gait detection capability.
The small display is used for displaying the current muscle fatigue and gait stability of the patient in real time.
The small display is connected with the data analysis and processing device, has a necessary display area capable of simultaneously displaying the muscle fatigue degree and gait stability, has certain portability, and can be directly worn on the wrist of a patient.
The alarm sound generating device is used for generating alarm sound in time when a patient enters a fatigue state or gait instability, and prompting the patient to pay attention to the current state.
The alarm sound generating device is small in appearance, is connected with the data analysis processing device, can be integrated with a display, and can quickly and clearly generate a relatively loud alarm sound.
The data acquisition and analysis device has certain computing capacity and data storage space, wherein the computing capacity is used for completing information acquisition and processing, and the storage space is used for storing gait data of normal people and recording the actual recovery condition of a patient.
The data acquisition and analysis device is used for analyzing and processing the electromyographic signals and the shank flexion and extension acceleration signals in real time, is used for completing the judgment of fatigue and the work of gait comparison analysis, and rapidly transmits the processing result to the display and the sounding device.
The data acquisition and analysis device needs to have the capability of rapidly and accurately transmitting signals, and on the basis, the installation position is not particularly required, and the data acquisition and analysis device can be arranged on an exoskeleton structure or around a patient training site.
The system directly acts on the body of a patient, so that the joint and other parts need to be smoothly transited, and the wearing is comfortable.
In the invention, the patient wears the lower limb exoskeleton as shown in the figure, the main function of the lower limb exoskeleton is to assist the hemiplegic patient to carry out rehabilitation training, and the system is integrated with the exoskeleton.
Step 1, detecting the current lower limb recovery condition of a patient, judging the motion capability of the patient, and determining whether the current physical condition of the patient is suitable for using the lower limb exoskeleton to develop rehabilitation training.
And 2, if the wearable lower limb exoskeleton is suitable, sequentially adhering the electrode patches to the tibialis anterior, the gastrocnemius, the rectus femoris and the biceps femoris on the left side and the right side of the patient, and placing the acceleration sensor in the middle of the calf of the patient.
In order to ensure the electromyographic signal acquisition quality, when the electrode patch is adhered, the patch should be adhered to the myoabdominal position of the measured part, the hair of the part should be cleaned as much as possible before the adhesion, and the part is cleaned by alcohol, for example, the surface of the skin is poor in quality due to roughness and the like, and dead skin and the like at the part can be properly cleaned by adopting superfine sand paper.
The stability of the acceleration sensor should be noted when the acceleration sensor is worn, so that the data acquisition effect is not affected due to unnecessary shaking when a patient moves.
When the electrode patch is worn, the electrode patch starts to automatically collect myoelectricity data on the surface of the patient lower limb muscle group at the frequency of 2000 hz.
When the rehabilitation training starts, the acceleration sensor starts to automatically collect the leg acceleration data of the patient in the whole process.
The collected electromyographic signals and the acceleration values are quickly transmitted to the data collection and analysis device.
After the data acquisition and analysis device receives the electromyographic signals and the acceleration values, noise reduction processing is firstly carried out, interference information is filtered, then characteristic value extraction is carried out, and the current fatigue degree of a patient is defined through the change of the characteristic values.
When the patient walks each step, the electromyographic signals will have corresponding peaks, the characteristic values extracted by the system are mainly the average frequency at the moment, and after a proper amount of movement, the average frequency will gradually show a decreasing trend, so that the muscle can be understood to gradually enter a fatigue state.
In addition, the system calculates the spectral distance of the electromyographic signals, and the index is taken as a main parameter for judging the muscle fatigue degree together with the average frequency.
When the muscle group is detected to enter a fatigue state, the system sends a prompt, a prompt signal is immediately transmitted to the display screen and the sound generating device, and the display screen is highlighted in a high-brightness and high-saturation color so as to draw attention of a patient to the greatest extent and play the most effective reminding role; meanwhile, the sounding device can also give out sharp and clear warning sounds to remind the patient to pay attention to the self condition so as to carry out proper rest.
The acceleration value is mainly used for gait segmentation, generally, each heel landing is a peak of acceleration in the walking process, and a part between two peak points can be used as a gait cycle.
After the acceleration value of the middle part of the lower leg of the patient is acquired in real time, the acceleration value is quickly transmitted to a data analysis device and is used as auxiliary data of gait analysis to be added into the operation process.
The cooperative analysis of the lower limb muscles can be completed through waveforms of the myoelectric signals of the muscles of each group and gait segmentation results obtained through acceleration analysis, and the whole gait acquisition process is completed through the results.
Each set of gait data obtained should be stored as important supplementary data and immediately compared with the normal human gait stored in the data analysis device, and at the same time, each set of gait data of the patient should be compared in real time.
When the detected gait of the patient is greatly different from the normal gait or is obviously changed from the initial gait of the patient, the system judges the gait as suspected bad gait, the current condition is immediately displayed on a display, and meanwhile, the sounding device also immediately gives an alarm to prompt the patient to pay attention.
In order to maximize the rehabilitation effect, the patient should pay attention to each system reminder, and the rehabilitation training is stopped or paused as required.
If a patient performs rehabilitation training with bad gait for a long time, the overall rehabilitation training effect is adversely affected, and the limb movement ability of the patient is in a weaker state, so that compared with the ordinary person, the patient is more likely to cause movement injury due to bad gait.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The above examples should be understood as illustrative only and not limiting the scope of the invention. Various changes and modifications to the present invention may be made by one skilled in the art after reading the teachings herein, and such equivalent changes and modifications are intended to fall within the scope of the invention as defined in the appended claims.
Claims (7)
1. Muscle real-time supervision analytic system based on low limbs ectoskeleton robot, characterized by comprising: the device comprises a lower limb exoskeleton body, a surface myoelectric sensor, an acceleration signal sensor, a small display, an alarm sound generating device and a data acquisition and analysis device; wherein,,
the lower limb exoskeleton organism is worn on the lower body of a user and is used for assisting the user to walk and carrying out lower limb exercise capacity rehabilitation training; the surface myoelectric sensor is used for detecting surface myoelectric signals of the lower limb muscle groups; the acceleration signal sensor is used for collecting acceleration during each stepping; the small display is used for displaying the current muscle fatigue and gait stability; the alarm sound generating device is integrated with the display and is used for generating alarm sound to remind a user when the system detects muscle fatigue or gait instability; the data acquisition and analysis device is used for collecting and storing electromyographic signals and acceleration signals, finishing data processing through methods of noise reduction, signal segmentation, characteristic value extraction and the like, comparing the processed data with the data of a healthy person and the near-several rehabilitation training data of the patient in real time, transmitting the processing result to the display and the sounding device in real time, and fully integrating the elements with the exoskeleton of the lower limb except the data acquisition and analysis device, wherein the exoskeleton is used for uniformly supplying power.
2. The real-time monitoring and analyzing system for muscles based on a lower limb exoskeleton robot according to claim 1, wherein the total number of the surface myoelectric sensors is eight, and the surface myoelectric sensors are respectively adhered to the tendons of the tibialis anterior, the gastrocnemius, the rectus femoris and the biceps femoris of the left and right sides of the user.
3. The real-time monitoring and analyzing system for muscles based on a lower limb exoskeleton robot according to claim 1, wherein the acceleration signal sensors are two in number and are respectively arranged in the midget of the lower leg at the left side and the right side of the user;
the small display is worn between the wrists of the user.
4. The real-time muscle monitoring and analyzing system based on the lower limb exoskeleton robot of claim 1, wherein the data acquisition and analyzing device firstly performs noise reduction treatment after receiving the electromyographic signals and the acceleration values, filters interference information, then performs characteristic value extraction, and defines the current fatigue degree of the patient through the change of the characteristic values;
when a patient walks each step, the electromyographic signals will have corresponding wave peaks, the characteristic values extracted by the system are mainly the average frequency at the moment, and after a proper amount of movement, the average frequency will gradually show a trend of decreasing, so that the muscle can be understood to gradually enter a fatigue state;
in addition, the spectral distance of the electromyographic signals is calculated, and the index is taken as a main parameter for judging the muscle fatigue degree together with the average frequency.
5. The real-time monitoring and analyzing system for muscle based on lower extremity exoskeleton robot as set forth in claim 4, wherein said extracting the characteristic value specifically includes:
the electromyographic signals acquired by using the electromyographic patch are interfered by 50hz power frequency noise, and the electromyographic signals are eliminated by using a single frequency trap in an IIR filter;
in the signal acquisition process, a baseline drift phenomenon occurs to low-frequency interference signals, the low-frequency signals are called as trend terms of the signals, and a smoothing filter is used for smoothing the signals to eliminate the trend terms;
after finishing the power frequency noise reduction and removing trending, the original signal is required to be subjected to global noise reduction, and a plurality of global noise reduction methods are adopted, wherein a Kalman filtering method is adopted;
the original electromyographic signal is output as a continuous long section, and in order to realize the purpose of monitoring data in real time, the electromyographic signal is subjected to sliding window data segmentation, wherein the window length is set to be 300ms in the example;
after the noise reduction and the segmentation are completed, extracting the characteristic value of each segment of data, wherein the characteristic value comprises an average frequency and a spectrum distance;
research shows that the fatigue degree of muscle is increased, the main frequency of the power spectrum of the surface electromyographic signal is changed from high to low, the average power value is gradually reduced, and the greater the fatigue degree is, the stronger the descending trend is.
The average frequency calculation formula isWhere f is the frequency and fs is the sampling rate.
The method for calculating the spectrum distance of the electromyographic signals further comprises the following steps of:
k-based spectral distance M k Is that
Spectral distance is
6. The real-time monitoring and analyzing system for muscles based on a lower extremity exoskeleton robot as claimed in claim 4, wherein said collected acceleration values are used for gait segmentation, and each heel landing is a peak of acceleration during walking, and the portion between two peak points can be used as a gait cycle;
after the acceleration value of the middle part of the calf of the patient is acquired in real time, the acceleration value is quickly transmitted to a data analysis device and is used as auxiliary data of gait analysis to be added into the operation process;
the cooperative analysis of the lower limb muscles can be completed through waveforms of the muscle electromyographic signals of each group and gait segmentation results obtained through acceleration analysis, and the whole gait acquisition process is completed through the results;
each group of obtained gait data is stored as important supplementary data, and is immediately compared with the gait of the normal person stored in the data analysis device, and meanwhile, each group of gait data of the patient is compared in real time;
when the detected gait of the patient is greatly different from the normal gait or is obviously changed from the initial gait of the patient, the system judges the gait as suspected bad gait, the current condition is immediately displayed on a display, and meanwhile, the sounding device also immediately gives an alarm to prompt the patient to pay attention.
7. The real-time monitoring and analyzing system for muscles based on a lower limb exoskeleton robot according to any one of claims 1 to 6, wherein the surface myoelectric sensor has the characteristics of high efficiency, easy use and accurate acquisition, can meet the sampling frequency of 2000hz, and adopts PICO series products produced by the company Cometa; the acceleration signal sensor can meet the sampling frequency of 1000hz, and a BMA253 type acceleration sensor produced by BOSCH company is adopted; the display needs to be worn on the wrist of the patient, so the display has the characteristics of small size and portability; the sound generating device needs to effectively generate prompt sound in time, so the sound generating device needs to have the characteristics of bright and clear sound and can be integrated with a display; the data acquisition and analysis device should have a strong computing power and a large data storage space. Except for the data acquisition and analysis device, the rest parts are fully integrated with the lower limb exoskeleton robot, and the exoskeleton robot supplies power to the lower limb exoskeleton robot.
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| CN117064380A (en) * | 2023-10-17 | 2023-11-17 | 四川大学华西医院 | Anti-fall early warning system and method for myoelectricity detection on lower limb surface and related products |
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| CN117064380A (en) * | 2023-10-17 | 2023-11-17 | 四川大学华西医院 | Anti-fall early warning system and method for myoelectricity detection on lower limb surface and related products |
| CN117064380B (en) * | 2023-10-17 | 2023-12-19 | 四川大学华西医院 | Anti-fall early warning system and method for myoelectricity detection on lower limb surface and related products |
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