CN106691447B - Muscle training aid device, muscle training evaluation device and method - Google Patents
Muscle training aid device, muscle training evaluation device and method Download PDFInfo
- Publication number
- CN106691447B CN106691447B CN201710100111.3A CN201710100111A CN106691447B CN 106691447 B CN106691447 B CN 106691447B CN 201710100111 A CN201710100111 A CN 201710100111A CN 106691447 B CN106691447 B CN 106691447B
- Authority
- CN
- China
- Prior art keywords
- muscle
- information
- training
- phase
- evaluation device
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/389—Electromyography [EMG]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1116—Determining posture transitions
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1121—Determining geometric values, e.g. centre of rotation or angular range of movement
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
- A61B5/4519—Muscles
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
- A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
Abstract
The present disclosure relates to a muscle training aid, a muscle training evaluation device and a muscle training evaluation method, and relates to the field of rehabilitation training of human bodies, the muscle training aid comprising: the sensor group is used for acquiring the posture information, the surface electromyographic signals and the skin conductivity information of the training target so as to acquire first data; the data processing mechanism is coupled with the sensor group and is used for acquiring muscle electric signal liveness information and power spectrum median frequency information according to the surface muscle electric signals; the data transmission mechanism is coupled with the sensor group and the data processing mechanism and is used for transmitting the gesture information, the skin conductivity information, the muscle electric signal activity information and the power spectrum median frequency information to a muscle training evaluation device. The present disclosure may help a user to improve training accuracy.
Description
Technical Field
The disclosure relates to the technical field of human rehabilitation training, in particular to a muscle training auxiliary device, a muscle training evaluation device and a muscle training evaluation method.
Background
With the increasing aging of the population structure, the number of aged patients suffering from osteoarthritis has a rapid rise, and the number of people who have undergone hip and knee joint replacement and arthroscopic surgery has been kept on a 2-digit trend for the last 5 years. The problems of trauma, aging and necrosis and loss of functions of the hip and knee joint are solved for the patient through the operation, the pain of the patient is solved, and the life quality is greatly improved.
However, due to the deficiency of the existing medical resources, the patients often cannot obtain enough rehabilitation guidance after the operation, and cannot perform scientific and effective rehabilitation exercise, so that the final effect of the operation is greatly reduced. The isometric muscle contraction training of gluteus muscles and quadriceps femoris is needed after the conventional lower limb operation, but the movement range of the training limbs is not large, patients are needed to effectively control muscles, the muscle force with enough strength is needed to be excited for each action, and each group of training needs to reach or approach to a fatigue state, so that the muscle force can be improved at the highest efficiency, and the damage to joints after the operation is avoided.
The current similar human physiological signal acquisition system is mainly in a laboratory stage, is not miniaturized and light in weight, prevents users from performing free muscle training to a certain extent, and the synchronous real-time multi-channel sensor data are acquired without effective closed-loop control, so that the phenomena of sweating, fatigue, heating and the like cannot be effectively regulated, and the accurate measurement and the effective guidance of the equivalent length contraction training cannot be performed. How to obtain accurate and efficient scientific training guidance more conveniently is essential for realizing postoperative rehabilitation.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
It is an object of the present disclosure to provide a muscle training aid, a muscle training evaluation device, and a muscle training evaluation method, which overcome, at least in part, one or more of the problems due to the limitations and disadvantages of the related art.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to one aspect of the present disclosure, there is provided a muscle training aid comprising:
the sensor group is used for acquiring the posture information, the surface electromyographic signals and the skin conductivity information of the training target so as to acquire first data;
the data processing mechanism is coupled with the sensor group and is used for acquiring muscle electric signal liveness information and power spectrum median frequency information according to the surface muscle electric signals;
and the data transmission mechanism is coupled with the sensor group and the data processing mechanism and is used for transmitting the gesture information, the skin conductivity information, the muscle electric signal activity information and the power spectrum median frequency information to a muscle training evaluation device.
In an exemplary embodiment of the present disclosure, the sensor group includes:
the gesture acquisition sensor is used for acquiring the gesture inclination angle of the training target;
the muscle electric signal acquisition sensor is used for acquiring surface muscle electric signals of the training target;
and the skin conductivity acquisition sensor is used for acquiring the skin conductivity coefficient of the training target.
In one exemplary embodiment of the present disclosure, the sensors in the sensor group each include a reference electrode and a measurement electrode and the reference electrode and measurement electrode connection lines of each of the sensors are longitudinally parallel to the muscle.
In an exemplary embodiment of the present disclosure, the muscle electrical signal acquisition sensor further includes:
and the voltage primary amplifier is used for amplifying the voltage signal between the reference electrode and the measuring electrode of the muscle electric signal acquisition sensor to acquire the surface muscle electric signal.
In one exemplary embodiment of the present disclosure, the data processing mechanism includes a fourier transform unit for performing fourier transform according to the surface muscle electrical signal to obtain the muscle electrical signal liveness information and power spectrum median frequency information.
According to one aspect of the present disclosure, there is provided a muscle training evaluation device comprising:
the data receiving module is used for receiving the gesture information, the skin conductivity information, the muscle electric signal activity information and the power spectrum median frequency information transmitted by the data transmission mechanism according to any one of the above;
the phase identification module is used for identifying a corresponding phase according to the gesture information and the muscle electrical signal liveness information;
the fatigue index calculation module is used for calculating a fatigue index according to the gesture information change, the power spectrum median frequency change and the conductivity coefficient change under the preset phase identified by the phase identification module;
and the comprehensive evaluation module is used for evaluating the training result of the training target by combining the fatigue index and the subjective score.
In an exemplary embodiment of the present disclosure, the phase includes:
one or more of a relax phase, a lift phase, a hold phase, and a fall phase.
In an exemplary embodiment of the present disclosure, the calculating the fatigue index includes:
calculating the fatigue index according to the product of the preset coefficient stability variance and the logarithm of the median reduction value of the power spectrum; wherein the preset coefficient includes: and calculating a muscle stability coefficient according to the posture information, a myoelectric signal reduction coefficient according to the surface myoelectric signal and a conductivity coefficient according to the conductivity information.
In an exemplary embodiment of the present disclosure, the muscle training evaluation device further includes:
and the display is coupled with the muscle training evaluation device and used for displaying the evaluation result output by the muscle training evaluation device.
In an exemplary embodiment of the present disclosure, the muscle training evaluation device further includes:
and the memory is coupled with the muscle training evaluation device and is used for storing the evaluation result output by the muscle training evaluation device.
According to one aspect of the present disclosure, there is provided a muscle training evaluation method including:
receiving posture information, skin conductivity information, muscle electrical signal liveness information and power spectrum median frequency information transmitted by the data transmission mechanism according to any one of the above;
identifying a corresponding phase according to the gesture information and the muscle electric signal liveness information;
calculating a fatigue index according to the gesture information change, the power spectrum median frequency change and the conductivity coefficient change under the preset phase identified by the phase identification module;
and evaluating the training result of the training target by combining the fatigue index and the subjective score.
In an exemplary embodiment of the present disclosure, the phase includes:
one or more of a relax phase, a lift phase, a hold phase, and a fall phase.
In an exemplary embodiment of the present disclosure, the calculating the fatigue index includes:
calculating the fatigue index according to the product of the preset coefficient stability variance and the logarithm of the median reduction value of the power spectrum; wherein the preset coefficient includes: and calculating a muscle stability coefficient according to the posture information, a myoelectric signal reduction coefficient according to the surface myoelectric signal and a conductivity coefficient according to the conductivity information.
According to the muscle training auxiliary device, the muscle training evaluation device and the muscle training evaluation method, training data and evaluation results are obtained through real-time acquisition, processing, transmission and evaluation of posture information, muscle electrophysiological signals and the like of a target training position. On the one hand, the accuracy of acquiring training data can be improved through the provided muscle training auxiliary device; in addition, the data can be further processed, so that the data can be conveniently transmitted to a subsequent muscle training evaluation device for evaluation. On the other hand, the training effect can be quantitatively calculated by the muscle training evaluation device in combination with the data input by the muscle training auxiliary device; in still another aspect, data support can be provided for a doctor to evaluate training targets and to formulate rehabilitation training plans, so that a patient can obtain accurate and efficient scientific training guidance more conveniently.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
Fig. 1 shows a block schematic diagram of a muscle training aid in an exemplary embodiment of the present disclosure.
Fig. 2 illustrates a myoelectric patch installation schematic diagram in an exemplary embodiment of the present disclosure.
Fig. 3 shows a schematic diagram of quadriceps isometric contraction training in an example embodiment of the disclosure.
Fig. 4 shows a schematic diagram of popliteal muscle isometric contraction training in an example embodiment of the disclosure.
Fig. 5 shows a schematic representation of posture change during popliteal cord muscle isometric contraction training in an example embodiment of the disclosure.
Fig. 6 illustrates a schematic diagram of a synchronous change in the corresponding electromyographic signals and tilt angle accompanying a posture change during the isometric contraction training of the levator ani muscles provided in fig. 5 in an exemplary embodiment of the disclosure.
Fig. 7 shows a circuit diagram of a muscle training aid in an exemplary embodiment of the present disclosure.
Fig. 8 shows a block schematic diagram of a muscle training evaluation device in an exemplary embodiment of the present disclosure.
Fig. 9 is a block diagram illustrating a mobile terminal to which the above-described muscle training evaluation apparatus is applied in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present disclosure. One skilled in the relevant art will recognize, however, that the aspects of the disclosure may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
In this example embodiment, there is provided a muscle training aid 10, and referring to fig. 1, the muscle training aid 10 in this example embodiment may include: a sensor group 11, a data processing means 21 and a data transmission means 31. Wherein: the sensor group 11 may be used to acquire pose information, surface electromyographic signals, and skin conductivity information of the training target to acquire the first data. A data processing mechanism 21, coupled to the sensor set, may be configured to obtain muscle electrical signal liveness information and power spectrum median frequency information from the surface muscle electrical signals. The data transmission mechanism 31, coupled to the sensor group and the data processing mechanism, may be configured to transmit the posture information, skin conductivity information, muscle electrical signal activity information, and power spectrum median frequency information to a muscle training evaluation device 41.
The muscle training aid 10 provided in the present exemplary embodiment is provided to the muscle training evaluation device 41 described below by acquiring, processing, and transmitting posture information of a training target, muscle electrophysiological signals, and the like through the sensor group 11, the data processing mechanism 21, and the data transmission mechanism 31 to obtain basic data. The device improves the accuracy of training data on the one hand, and can further process the data, so that the data can be conveniently transmitted to a subsequent muscle training evaluation device for evaluation.
Next, each part of the muscle training aid in the present exemplary embodiment will be described in more detail.
In this example embodiment, the muscle training aid provided by the present disclosure may be applied to isometric muscle contraction training after conventional lower limb surgery. The conventional lower limb operation can comprise limb fracture, in particular intra-articular or dryend fracture, open reduction internal fixation; artificial joint replacement, ligament reconstruction; traumatic arthritis, rheumatoid arthritis synoviectomy, suppurative arthritis drainage; joint contracture, adhesion loosening, arthroscopy, and the like. However, the present disclosure is not limited thereto, and other rehabilitation therapies that can be applied to the muscle training aid device are within the scope of the claims of the present disclosure.
In this example embodiment, the isometric contraction is defined as the contraction of the muscle with a constant length and only an increase in tension, also known as static contraction. Isometric contraction can keep certain joints in certain positions, and conditions are created for the movement of other joints. To maintain a certain posture, some muscles must perform isometric contraction, for example, when performing squat-up movements, the muscles of the shoulder strap and torso contract in length to ensure the vertical posture of the torso. In more complex movements, the body posture and hence the form of contraction of the muscles change, often in a mixture of isometric and isotonic contractions. In this example embodiment, the target training site to which the muscle training aid device is applied may be quadriceps femoris (fig. 3), popliteal cord muscle (fig. 4), and the like, where the limb activity of such muscle training is not large, and the patient needs to effectively control the muscle.
In the present exemplary embodiment, the sensor group 11 may include: the system comprises an attitude acquisition sensor, a muscle electric signal acquisition sensor and a skin conductivity acquisition sensor.
In addition, the above-mentioned several sensors can include reference electrode and measuring electrode, several sensors including above-mentioned two kinds of electrodes can be set up on the myoelectric subsides 10 in a concentrated way, and the reference electrode and measuring electrode line of each sensor can be with the muscle longitudinal direction parallel of target training position in order to guarantee accuracy and the sensitivity of data acquisition. In this example embodiment, referring to fig. 2, the myoelectric patch 10 may be designed into a shape with different sizes, such as an ellipse, a strip, etc., according to the difference in shape and area of the training part; the mounting mode is not limited to the patch type provided in the present exemplary embodiment, and may be used in combination with carriers of different training positions and training strengths, the carrier can be a sleeved elastic band or a magic tape type elastic band and the like. Wherein:
the attitude acquisition sensor may be used to acquire an attitude inclination of the training target. Before a user starts formal muscle training, firstly, posture initialization is needed, and initialized posture information is acquired: referring to fig. 3, taking quadriceps isometric contraction training as an example, the initialization gesture requires a user to fold legs to lie on a bed surface, a gesture acquisition sensor acquires a current gesture inclination angle and calibrates the difference between a current carrier coordinate system and a space coordinate system, a gesture coordinate system is newly established and records a current inclination angle value, and the current inclination angle value is used as a reference inclination angle.
A muscle electrical signal acquisition sensor may be used to acquire surface muscle electrical signals of the training target. Referring to fig. 5 and 6, the surface electromyographic signals are electrical signals accompanying muscle contraction, and are important methods for noninvasively detecting muscle activity on the body surface, and are commonly used in physical science research, rehabilitation medicine clinic and the like. The muscle electrical signal-acquisition sensor may further comprise a voltage primary amplifier that may be used to amplify the voltage signal between the reference electrode and the measurement electrode of the muscle electrical signal-acquisition sensor to obtain the surface muscle electrical signal. In the actual acquisition process, the biological electromyographic signals directly acquired between the reference electrode and the measurement electrode have the problems of low frequency, weak signals, more interference signals and the like, so that the subsequent electromyographic signal processing effect is not obvious, the accuracy of data is affected, accurate tracking and feedback cannot be obtained in training, and the voltage primary amplifier can be used for effectively filtering noise, inhibiting power frequency interference and polarization voltage, so that the requirement of the subsequent processing of the electromyographic signals is met.
For example, before a user starts a formal muscle training, first, an electromyographic signal is initialized, electromyographic signal information of an initialized posture is collected, and also, taking a quadriceps femoris isometric contraction training as an example, the user is required to keep the initialized posture to keep legs in a closed state and lie on a bed surface, a mean median of the electromyographic signals at intervals of 2s is collected as a 0 benchmark, and a mean median of the electromyographic signals at the time of maximum strength contraction is recorded as a 100% benchmark.
Further, a skin conductivity acquisition sensor may be used to acquire the skin conductivity coefficient of the training target. The skin conductivity factor reflects sweat secretion from the skin surface and is used to measure muscle fatigue. The current skin conductivity coefficient is also recorded during the initialization phase before the user starts the formal muscle training. Furthermore, it will be readily appreciated by those skilled in the art that, in other exemplary embodiments of the present disclosure, the sensor set may further include one or more other physiological information collectors, such as a heart rate collector, a blood pressure collector, an energy consumption collector, etc., which are not particularly limited in this exemplary embodiment.
In this example embodiment, the data processing mechanism 21 provided by the present disclosure may include a fourier transform unit configured to perform fourier transform according to the surface muscle electrical signal to obtain the muscle electrical signal liveness information and the power spectrum median frequency information.
In particular, fourier transformation is a method of analyzing signals, which can analyze the components of the signals and can use these components to synthesize the signals. Many waveforms may be used as components of a signal, such as sine waves, square waves, saw tooth waves, etc., and fourier transforms use sine waves as components of a signal, for example, in signal processing, fourier transforms typically use to break down a signal into a frequency spectrum-displaying amplitude magnitudes corresponding to frequency. In this embodiment, the fourier transform unit in the data processing mechanism 21 may perform fourier transform according to the surface muscle electrical signal to obtain the muscle electrical signal liveness information and the median frequency information of the power spectrum.
In this exemplary embodiment, the data transmission mechanism 31 provided in the present disclosure, coupled to the sensor group and the data processing mechanism, may be configured to transmit the posture information, skin conductivity information, muscle electrical signal activity information, and power spectrum median frequency information to a muscle training evaluation device 41.
As will be appreciated by those skilled in the art, the transmission mode may be Bluetooth, wi-Fi, zigbee (a two-way wireless communication technology in a short distance), and a wireless communication technology such as short distance communication NFC (Near Field Communication). On one hand, the wireless transmission mode is used, so that a user only needs to wear the training guidance instrument in training, wire connection is not needed, and the wireless transmission device has the characteristics of convenience in use, freedom of limbs, light weight, portability and the like, and also has the advantages of instantaneity, low power consumption, long endurance time and the like; on the other hand, the data is transmitted to the muscle training and evaluating device 41 in a wireless manner, so that the physical port requirement of the muscle training and evaluating device 41 for receiving the data is reduced, and the data can be received only by configuring a corresponding data receiving module. The data transmission mechanism 31 may further include one or more other transmission modes, which are not particularly limited in the present exemplary embodiment.
Furthermore, in an exemplary embodiment of the present disclosure, a circuit diagram design of the muscle training aid is shown in fig. 7.
The present exemplary embodiment further provides a muscle training evaluation device 41, referring to fig. 8, coupled with the data transmission mechanism 31, the muscle training evaluation device 41 may include: a data receiving module 42, a phase identification module 43, a fatigue index calculation module 44 and a comprehensive evaluation module 45. May be used to evaluate the training results of the training object in combination with the information transmitted by the data transmission means 31. On the one hand, the training effect can be quantitatively calculated by combining the data input by the muscle training evaluation device and the muscle training auxiliary device by the patient; on the other hand, the method can provide data support for doctors to evaluate training targets and make rehabilitation training plans, so that patients can obtain accurate and efficient scientific training guidance more conveniently.
In this exemplary embodiment, the muscle training evaluation device 41 receives the data sent by the data transmission mechanism 31, and the muscle training evaluation device 41 may be set in a rehabilitation training program, such as APP, where the rehabilitation training program may be installed in a mobile phone end, a smart watch or a computer, or may be installed in a network television or any other terminal capable of establishing a wireless connection with the data transmission mechanism 31, which is not limited in this exemplary embodiment.
The data receiving module 42 may be configured to receive the gesture information, the skin conductivity information, the muscle electrical signal activity information, and the median frequency information of the power spectrum transmitted by the data transmitting mechanism 31.
Further, the phase identification module 43 may be configured to identify a corresponding phase according to the gesture information and the muscle electrical signal activity information; the phase may include: relaxation phase, lifting phase, holding phase, and falling phase. The posture information is that in a posture coordinate system, an x-direction inclination angle value of a training target is acquired in training, and the x-direction inclination angle value is compared with a reference inclination angle; the muscle electric signal activity information is based on 0-100% of the reference of the muscle electric signal in the initialized state. For example, in the user training process, the x-direction inclination angle value of the training target is acquired to be a, at this time, a is 5 degrees larger than the reference inclination angle in the initialized state, and the activity of the muscle electrical signals of the training target is acquired to be 30%. And recognizing that the training of the user is in one of the phases at the moment according to the corresponding gesture information of the four phases in different modes and the data range set by the muscle electric signal liveness information. The real-time phase identification can more accurately feed back the gesture information and intensity in the training process to the user, thereby helping the user to scientifically control the training process. For example, the four phases are identified based on the data ranges set by the posture information and the muscle electrical signal liveness information in different modes.
In an exemplary embodiment, using quadriceps isometric contraction training as an example, four phases of identification:
a relaxation phase, the x-direction inclination angle a, and a reference inclination angle of-2 degrees < a < reference inclination angle +2 degrees, and the electromyographic signal activity degree <10%;
lifting the phase, wherein the inclination angle a in the x direction is larger than the reference inclination angle +5°, and the electromyographic signal activity is larger than 20%;
maintaining a phase, wherein the inclination angle a in the x direction is larger than the reference inclination angle +8°, and the electromyographic signal activity is larger than 40%;
and the phase falls, the inclination angle a in the x direction is equal to a < reference inclination angle +8°, and the electromyographic signal activity degree is more than 20%.
In another exemplary embodiment, four phases are identified, taking popliteal isometric contraction training as an example:
a relaxation phase, the x-direction inclination angle a, and a reference inclination angle of-2 degrees < a < reference inclination angle +2 degrees, and the electromyographic signal activity degree <10%;
lifting the phase, wherein the inclination angle a in the x direction is larger than the reference inclination angle +2°, and the electromyographic signal activity is larger than 20%;
maintaining a phase, wherein the inclination angle a in the x direction is larger than the reference inclination angle +4°, and the electromyographic signal activity is larger than 40%;
and the phase falls, the inclination angle a in the x direction is equal to a < reference inclination angle +4°, and the electromyographic signal activity degree is more than 20%.
The above-mentioned phase identification module may further include one or more other identification modes, and the corresponding phase types and the phase identification data ranges are set according to different training requirements, which are not limited in particular in the present exemplary embodiment.
In the present exemplary embodiment, the fatigue index calculation module 44 may be configured to calculate a fatigue index according to the change in the posture information, the change in the median frequency of the power spectrum, and the change in the conductivity coefficient at the preset phase identified by the phase identification module; wherein said calculating a fatigue index comprises: calculating the fatigue index according to the product of the preset coefficient stability variance and the logarithm of the median reduction value of the power spectrum; the preset coefficients comprise: and calculating a muscle stability coefficient according to the posture information, a myoelectric signal reduction coefficient according to the surface myoelectric signal and a conductivity coefficient according to the conductivity information.
Specifically, the preset coefficients may include three aspects:
the muscle stability factor, the duration of the hold phase, and the acceleration change associated with a certain direction. In other words, the duration of the posture of the user in the holding phase during the training is long, and the duration period stability and the acceleration of the change of the posture inclination angle reflect the muscle stability coefficient. When the duration time in one period of the muscle training under the phase maintenance is reduced by 80% compared with the longest period, and the acceleration variance in the z direction in the current posture coordinate system is increased to more than 10%, the reduction of the muscle stability coefficient can be judged;
electromyographic signal reduction coefficients, a reduction in the median frequency value of the power spectrum, for example a reduction in the median frequency of the power spectrum by 10Hz;
the conductivity coefficient is calculated according to the conductivity information, reflects the secretion degree of sweat in the training process of the user, and further reflects the fatigue degree, for example, the conductivity coefficient is improved by more than 2 times.
Wherein the calculating the fatigue index may include: the fatigue index is calculated from the product of the above three coefficient stability variances and the logarithm of the median reduction in power spectrum. It will be appreciated by those skilled in the art that the fatigue index calculation module provided in the present exemplary embodiment is not limited to the above calculation method, and the formula for calculation and the coefficients used may be adjusted according to the actual situation.
In this exemplary embodiment, the comprehensive evaluation module 45 may evaluate the training result of the training target in combination with the fatigue index and the subjective score. Subjective scores may be provided after each completion of a set of actions or after the completion of all exercises, and may include, for example, the degree of muscle soreness, the feeling of exercise, and the like. The muscle soreness degree can be set to be non-pain, slight pain, relatively pain, very pain and the like; or scoring, wherein the degree of muscle soreness is expressed in terms of ten, 0 represents total pain free, and 10 represents very soreness. The training experience can be set easily, just, more difficult, very difficult and the like, and scoring system can be uniformly adopted, so that later comprehensive calculation is facilitated. The subjective scores in the comprehensive evaluation module may also include subjective scores in other aspects or may be conveniently fed back to the doctor for reference in the form of a message, which is not particularly limited in the present exemplary embodiment.
In this exemplary embodiment, the muscle training evaluation device may further include a display coupled to the muscle training evaluation device 41 for displaying the evaluation result output by the muscle training evaluation device 41. The muscle training evaluation device 41 may be disposed in a rehabilitation training program, where the rehabilitation training program may be installed in a mobile phone end or a computer, or may be installed in a network television or any other terminal capable of establishing a wireless connection with the data transmission mechanism 31, and the display is a display mechanism of a mobile phone display, a smart watch display, a computer display, a television display or other corresponding terminals, so that on one hand, a user can conveniently view training data in time during use, and on the other hand, the user can also conveniently operate and use the muscle training evaluation device.
In this exemplary embodiment, the muscle training evaluation device 45 may further include a memory coupled to the muscle training evaluation device 41 for storing the evaluation result output by the muscle training evaluation device 41. Also, the muscle training and evaluation device 41 may be provided in a rehabilitation training program, which further includes a storage function, and stores all training data and evaluation results of the user in a corresponding memory, so as to facilitate subsequent viewing.
In other embodiments of the present disclosure, the muscle training assistance device may further send training data and evaluation results to other users, such as doctors or rehabilitation instruction institutions, to provide reference data for developing a rehabilitation training plan of the next step; the data links may also be transferred to other applications for sharing and viewing.
In addition, the present exemplary embodiment also provides a muscle training evaluation method, which may further include: receiving attitude information, skin conductivity information, muscle electric signal liveness information and power spectrum median frequency information transmitted by the data transmission mechanism; identifying a corresponding phase according to the gesture information and the muscle electric signal liveness information; calculating a fatigue index according to the gesture information change, the power spectrum median frequency change and the conductivity coefficient change under the preset phase identified by the phase identification module; and evaluating the training result of the training target by combining the fatigue index and the subjective score.
Wherein the phase comprises: one or more of a relax phase, a lift phase, a hold phase, and a fall phase. The calculating of the fatigue index includes: calculating the fatigue index according to the product of the preset coefficient stability variance and the logarithm of the median reduction value of the power spectrum; wherein the preset coefficient includes: and calculating a muscle stability coefficient according to the posture information, a myoelectric signal reduction coefficient according to the surface myoelectric signal and a conductivity coefficient according to the conductivity information.
The specific details of each of the above-mentioned muscle training evaluation methods have been described in detail in the corresponding muscle training evaluation apparatuses, and thus are not described here again.
In other embodiments of the present disclosure, referring to fig. 9, a mobile terminal 600 to which the above-described muscle training evaluation apparatus 41 is applied may include one or more of the following components: a processing component 602, a memory 604, a power component 606, a multimedia component 608, an audio component 610, an input/output (I/O) interface 612, a sensor component 614, and a communication component 616.
The processing component 602 generally controls overall operation of the mobile terminal 600, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 602 may include one or more processors 620 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 602 can include one or more modules that facilitate interaction between the processing component 602 and other components. For example, the processing component 602 may include a multimedia module to facilitate interaction between the multimedia component 604 and the processing component 602.
The memory 604 is configured to store various types of data to support operations at the device 600. Examples of such data include instructions for any application or method operating on the mobile terminal 600, contact data, phonebook data, messages, pictures, videos, and the like. The memory 604 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
The power supply component 606 provides power to the various components of the mobile terminal 600. The power supply components 606 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the mobile terminal 600.
The multimedia component 608 includes a screen between the mobile terminal 600 and the user that provides an output interface. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 608 includes a front camera and/or a rear camera. The front-facing camera and/or the rear-facing camera may receive external multimedia data when the device 600 is in an operational mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
The audio component 610 is configured to output and/or input audio signals. For example, the audio component 610 includes a Microphone (MIC) configured to receive external audio signals when the mobile terminal 600 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 604 or transmitted via the communication component 616. In some embodiments, audio component 610 further includes a speaker for outputting audio signals.
The I/O interface 612 provides an interface between the processing component 602 and peripheral interface modules, which may be a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.
The sensor assembly 614 includes one or more sensors for providing status assessment of various aspects of the mobile terminal 600. For example, the sensor assembly 614 may detect the on/off state of the device 600, the relative positioning of the components, such as the display and keypad of the mobile terminal 600, the sensor assembly 614 may also detect a change in position of the mobile terminal 600 or a component of the mobile terminal 600, the presence or absence of a user's contact with the mobile terminal 600, the orientation or acceleration/deceleration of the mobile terminal 600, and a change in temperature of the mobile terminal 600. The sensor assembly 614 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact. The sensor assembly 614 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 614 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 616 is configured to facilitate communication between the mobile terminal 600 and other devices, either wired or wireless. The mobile terminal 600 may access a wireless network based on a communication standard, such as WiFi,2G or 6G, or a combination thereof. In one exemplary embodiment, the communication component 616 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component 616 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the mobile terminal 600 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for executing the methods described above.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (11)
1. A muscle training aid, comprising:
the sensor group is used for acquiring the posture information, the surface muscle electrical signals and the skin conductivity information of the training target to acquire first data, the sensors in the sensor group comprise reference electrodes and measuring electrodes, and the connecting lines of the reference electrodes and the measuring electrodes of the sensors are longitudinally parallel to muscles; the sensor group comprises a muscle electric signal acquisition sensor, wherein the muscle electric signal acquisition sensor comprises a voltage primary amplifier, and the voltage primary amplifier is used for amplifying a voltage signal between a reference electrode and a measuring electrode of the muscle electric signal acquisition sensor so as to acquire the surface muscle electric signal; the data processing mechanism is coupled with the sensor group and is used for acquiring muscle electric signal liveness information and power spectrum median frequency information according to the surface muscle electric signals;
and the data transmission mechanism is coupled with the sensor group and the data processing mechanism and is used for transmitting the gesture information, the skin conductivity information, the muscle electric signal activity information and the power spectrum median frequency information to a muscle training evaluation device.
2. The muscle training aid of claim 1, wherein the sensor set comprises:
the gesture acquisition sensor is used for acquiring the gesture inclination angle of the training target;
the muscle electric signal acquisition sensor is used for acquiring surface muscle electric signals of the training target;
and the skin conductivity acquisition sensor is used for acquiring the skin conductivity coefficient of the training target.
3. The muscle training aid of claim 1, wherein the data processing mechanism comprises a fourier transform unit for fourier transforming the surface muscle electrical signal to obtain the muscle electrical signal liveness information and power spectrum median frequency information.
4. A muscle training evaluation device, comprising:
a data receiving module for receiving the gesture information, skin conductivity information, muscle electric signal activity information and power spectrum median frequency information transmitted by the data transmission mechanism according to claims 1-3;
the phase identification module is used for identifying a corresponding phase according to the gesture information and the muscle electrical signal liveness information;
the fatigue index calculation module is used for calculating a fatigue index according to the gesture information change, the power spectrum median frequency change and the conductivity coefficient change under the preset phase identified by the phase identification module;
and the comprehensive evaluation module is used for evaluating the training result of the training target by combining the fatigue index and the subjective score.
5. The muscle training evaluation device of claim 4, wherein the phase comprises:
one or more of a relax phase, a lift phase, a hold phase, and a fall phase.
6. The muscle training evaluation device of claim 4, wherein the calculating a fatigue index comprises:
calculating the fatigue index according to the product of the preset coefficient stability variance and the logarithm of the median reduction value of the power spectrum; wherein the preset coefficient includes: and calculating a muscle stability coefficient according to the posture information, a myoelectric signal reduction coefficient according to the surface myoelectric signal and a conductivity coefficient according to the conductivity information.
7. The muscle training evaluation device of claim 4, further comprising:
and the display is coupled with the muscle training evaluation device and used for displaying the evaluation result output by the muscle training evaluation device.
8. The muscle training evaluation device of claim 4, further comprising:
and the memory is coupled with the muscle training evaluation device and is used for storing the evaluation result output by the muscle training evaluation device.
9. A method of evaluating muscle training, comprising:
receiving posture information, skin conductivity information, muscle electrical signal liveness information and power spectrum median frequency information transmitted by the data transmission mechanism according to claims 1-3;
identifying a corresponding phase according to the gesture information and the muscle electric signal liveness information;
calculating a fatigue index according to the gesture information change, the power spectrum median frequency change and the conductivity coefficient change under the preset phase identified by the phase identification module;
and evaluating the training result of the training target by combining the fatigue index and the subjective score.
10. The muscle training evaluation method of claim 9, wherein the phase comprises:
one or more of a relax phase, a lift phase, a hold phase, and a fall phase.
11. The muscle training evaluation method of claim 9, wherein the calculating a fatigue index comprises:
calculating the fatigue index according to the product of the preset coefficient stability variance and the logarithm of the median reduction value of the power spectrum; wherein the preset coefficient includes: and calculating a muscle stability coefficient according to the posture information, a myoelectric signal reduction coefficient according to the surface myoelectric signal and a conductivity coefficient according to the conductivity information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710100111.3A CN106691447B (en) | 2017-02-23 | 2017-02-23 | Muscle training aid device, muscle training evaluation device and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710100111.3A CN106691447B (en) | 2017-02-23 | 2017-02-23 | Muscle training aid device, muscle training evaluation device and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106691447A CN106691447A (en) | 2017-05-24 |
| CN106691447B true CN106691447B (en) | 2023-09-12 |
Family
ID=58916892
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710100111.3A Active CN106691447B (en) | 2017-02-23 | 2017-02-23 | Muscle training aid device, muscle training evaluation device and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106691447B (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108229283B (en) * | 2017-05-25 | 2020-09-22 | 深圳市前海未来无限投资管理有限公司 | Electromyographic signal acquisition method and device |
| CN109259761B (en) * | 2018-08-09 | 2021-01-08 | 江汉大学 | Method, device and storage medium for detecting muscle endogenous fatigue |
| CN109864749A (en) * | 2019-01-30 | 2019-06-11 | 北京津发科技股份有限公司 | Pay attention to training method, assessment method and its device of control ability |
| CN110477897B (en) * | 2019-08-15 | 2022-04-01 | 中国体育国际经济技术合作有限公司 | Physical ability testing method and system |
| CN112807002A (en) * | 2019-11-18 | 2021-05-18 | 深圳市理邦精密仪器股份有限公司 | Parameter optimization method, system, equipment and storage medium of muscle training instrument |
| CN112263254A (en) * | 2020-06-11 | 2021-01-26 | 复旦大学附属华山医院 | Human body energy consumption prediction system based on surface electromyogram signal sensor and prediction method thereof |
| CN111657957B (en) * | 2020-07-02 | 2023-06-09 | 中国医学科学院生物医学工程研究所 | Lip muscle strength analysis method and system based on time-frequency joint analysis |
| CN112089442B (en) * | 2020-08-21 | 2023-10-13 | 意领科技有限公司 | Muscle training method and system for providing visual feedback by utilizing ultrasonic imaging |
| CN114081479A (en) * | 2021-12-08 | 2022-02-25 | 清华大学 | Body state detection method and device, electronic equipment and intelligent garment |
| WO2023102908A1 (en) * | 2021-12-10 | 2023-06-15 | 深圳大学 | Multi-modal strength training assistance method and system |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007209453A (en) * | 2006-02-08 | 2007-08-23 | Delta Tooling Co Ltd | Muscle fatigue evaluation system |
| CN102210925A (en) * | 2010-04-08 | 2011-10-12 | 上海体育学院 | Sports training analysis system based on digital site and electromechanical signal |
| CN103584884A (en) * | 2013-11-07 | 2014-02-19 | 中国科学院深圳先进技术研究院 | Muscle force assessment method and device and muscle rehabilitation training tracking and assessment method and system |
| CN104208865A (en) * | 2013-06-03 | 2014-12-17 | 飞比特公司 | Fitness monitoring device with altimeter |
| CN104337666A (en) * | 2014-11-05 | 2015-02-11 | 中山大学 | Multi-muscle collaborative myoelectricity feedback rehabilitation training system and method |
| CN104382595A (en) * | 2014-10-27 | 2015-03-04 | 燕山大学 | Upper limb rehabilitation system and method based on myoelectric signal and virtual reality interaction technology |
| CN105561567A (en) * | 2015-12-29 | 2016-05-11 | 中国科学技术大学 | Step counting and motion state evaluation device |
| CN105771182A (en) * | 2016-05-13 | 2016-07-20 | 中国科学院自动化研究所 | Active exercise control method and device for rehabilitation robot |
| CN105963926A (en) * | 2016-04-29 | 2016-09-28 | 中国科学院苏州生物医学工程技术研究所 | Multi-mode fusion hand function rehabilitation training and intelligent evaluation system |
| CN207412163U (en) * | 2017-02-23 | 2018-05-29 | 北京纳通科技集团有限公司 | Muscular training apparatus for evaluating |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101501661B1 (en) * | 2013-06-10 | 2015-03-12 | 한국과학기술연구원 | Wearable electromyogram sensor system |
-
2017
- 2017-02-23 CN CN201710100111.3A patent/CN106691447B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007209453A (en) * | 2006-02-08 | 2007-08-23 | Delta Tooling Co Ltd | Muscle fatigue evaluation system |
| CN102210925A (en) * | 2010-04-08 | 2011-10-12 | 上海体育学院 | Sports training analysis system based on digital site and electromechanical signal |
| CN104208865A (en) * | 2013-06-03 | 2014-12-17 | 飞比特公司 | Fitness monitoring device with altimeter |
| CN103584884A (en) * | 2013-11-07 | 2014-02-19 | 中国科学院深圳先进技术研究院 | Muscle force assessment method and device and muscle rehabilitation training tracking and assessment method and system |
| CN104382595A (en) * | 2014-10-27 | 2015-03-04 | 燕山大学 | Upper limb rehabilitation system and method based on myoelectric signal and virtual reality interaction technology |
| CN104337666A (en) * | 2014-11-05 | 2015-02-11 | 中山大学 | Multi-muscle collaborative myoelectricity feedback rehabilitation training system and method |
| CN105561567A (en) * | 2015-12-29 | 2016-05-11 | 中国科学技术大学 | Step counting and motion state evaluation device |
| CN105963926A (en) * | 2016-04-29 | 2016-09-28 | 中国科学院苏州生物医学工程技术研究所 | Multi-mode fusion hand function rehabilitation training and intelligent evaluation system |
| CN105771182A (en) * | 2016-05-13 | 2016-07-20 | 中国科学院自动化研究所 | Active exercise control method and device for rehabilitation robot |
| CN207412163U (en) * | 2017-02-23 | 2018-05-29 | 北京纳通科技集团有限公司 | Muscular training apparatus for evaluating |
Non-Patent Citations (1)
| Title |
|---|
| 牟洪雨等.人体上肢表面肌电反馈康复训练系统的研制.中国康复医学杂志.2003,第第18卷卷(第第18卷期),第1-2节. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106691447A (en) | 2017-05-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106691447B (en) | Muscle training aid device, muscle training evaluation device and method | |
| US20230410679A1 (en) | Systems and methods for facilitating mind-body-emotion state self-adjustment and functional skills development by way of biofeedback and environmental monitoring | |
| KR102270209B1 (en) | Wearable electronic device | |
| CN107249441B (en) | Electronic device and method for measuring biological information | |
| US20180344160A1 (en) | Multipurpose Diagnostic Examination Apparatus And System | |
| US8519835B2 (en) | Systems and methods for sensory feedback | |
| CN109716443B (en) | Motion management method and system using electromyography sensor | |
| US11534358B2 (en) | Orthosis systems and rehabilitation of impaired body parts | |
| EP2919434B1 (en) | Method for determining data source | |
| WO2014151874A1 (en) | Systems, methods and devices for assessing and treating pain, discomfort and anxiety | |
| CN105988588A (en) | Electronic device including touch panel and method for controlling the same | |
| Depari et al. | A wearable smartphone-based system for electrocardiogram acquisition | |
| US10959641B2 (en) | Apparatus and method for measuring bioelectrical impedance and apparatus and method for measuring biometric information | |
| US20170210010A1 (en) | System and method of controlling robot by brain electrical signals | |
| US11705748B2 (en) | Wearable gesture recognition device for medical screening and associated operation method and system | |
| US20200193858A1 (en) | Unobtrusive motivation estimation | |
| Jeong et al. | Closed-loop network of skin-interfaced wireless devices for quantifying vocal fatigue and providing user feedback | |
| KR102539145B1 (en) | Apparatus and method for measuring bio signal | |
| CN207412163U (en) | Muscular training apparatus for evaluating | |
| Ma et al. | Muscle fatigue detection and treatment system driven by internet of things | |
| US20220189626A1 (en) | Systems and methods for detecting and addressing quality issues in remote therapy sessions | |
| Usakli et al. | A novel EOG-based wireless rapid communication device for people with motor neuron diseases | |
| Jovanov et al. | Sensors and systems for obesity care and research | |
| RU163942U1 (en) | MEDICAL TRANSMITTER | |
| CN112826454B (en) | Information pushing method and related product |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20170921 Address after: 100082 Beijing city Haidian District Deshengmen West Street No. 15 ocean scenery area 8-2-1201 room Applicant after: BEIJING NATON TECHNOLOGY GROUP Co.,Ltd. Applicant after: BEIJING SHANGDA MEDICAL TECHNOLOGY Co.,Ltd. Applicant after: BEIJING YIMAI TECHNOLOGY CO.,LTD. Address before: 100082 Beijing city Haidian District Deshengmen West Street No. 15 ocean scenery area 8-2-1201 room Applicant before: BEIJING NATON TECHNOLOGY GROUP Co.,Ltd. Applicant before: BEIJING SHANGDA MEDICAL TECHNOLOGY Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |