CN113545750A - Wearable device for upper limb rehabilitation and assessment - Google Patents
Wearable device for upper limb rehabilitation and assessment Download PDFInfo
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- 210000001364 upper extremity Anatomy 0.000 title claims abstract description 27
- 210000003205 muscle Anatomy 0.000 claims abstract description 47
- 238000012549 training Methods 0.000 claims abstract description 44
- 238000002567 electromyography Methods 0.000 claims abstract description 22
- 238000011156 evaluation Methods 0.000 claims abstract description 13
- 238000012544 monitoring process Methods 0.000 claims abstract description 13
- 238000012806 monitoring device Methods 0.000 claims abstract description 11
- 210000000707 wrist Anatomy 0.000 claims abstract description 11
- 238000001914 filtration Methods 0.000 claims description 14
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- 238000013528 artificial neural network Methods 0.000 claims description 4
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- 230000009471 action Effects 0.000 claims description 2
- 238000011084 recovery Methods 0.000 abstract description 2
- 210000004247 hand Anatomy 0.000 abstract 2
- 230000033001 locomotion Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 230000007246 mechanism Effects 0.000 description 4
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- 208000006011 Stroke Diseases 0.000 description 3
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- 210000003414 extremity Anatomy 0.000 description 2
- 230000003321 amplification Effects 0.000 description 1
- 238000005452 bending Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
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- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 210000000245 forearm Anatomy 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000004118 muscle contraction Effects 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
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- 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]
- A61B5/397—Analysis of electromyograms
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- A—HUMAN NECESSITIES
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
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- 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
- A61B5/1118—Determining activity level
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- A61B5/224—Measuring muscular strength
- A61B5/225—Measuring muscular strength of the fingers, e.g. by monitoring hand-grip force
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- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4848—Monitoring or testing the effects of treatment, e.g. of medication
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- 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
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
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Abstract
The utility model provides a wearable device that upper limbs are recovered and aassessment, including gloves body and rehabilitation training and monitoring devices, rehabilitation training and monitoring devices include heart rate sensor, pneumatic muscle drive adjusting device, EMG electromyography sensor and main control unit, wherein heart rate sensor sets up the wrist part at the gloves body, pneumatic muscle sets up the finger part at the gloves body, pneumatic muscle drive adjusting device sets up the back of the hand part at the gloves body and is connected with pneumatic muscle and controls, EMG electromechanical sensor sets up the upper arm part at the gloves body, main control unit is connected with other each device and sets up on the gloves body. The device provides a scheme for rehabilitation monitoring and evaluation of patients with the unsound hand functions by analyzing the relevant signals of the hands aiming at the unsound hand functions of the patients, utilizes the sensor to collect the relevant signals, detects the measured signals and grades or classifies the signals to judge the recovery condition of the hands of the patients.
Description
Technical Field
The invention relates to the technical field of medical detection, in particular to a wearable device for upper limb rehabilitation and evaluation.
Background
At present, stroke and stroke incidence rate rise year by year, and the disease causes the loss of limb movement function of a patient, especially the loss of upper limb movement function, and greatly influences the daily life of the patient. Therefore, the treatment and rehabilitation of stroke patients are urgent and important. In clinical rehabilitation, a rehabilitation doctor usually carries out one-to-one continuous passive training on an affected limb, the labor intensity is high, the training effect cannot be guaranteed, and the intelligent level of the existing rehabilitation robot and the existing auxiliary instrument is not high, so that the development of the rehabilitation robot with the high intelligent level is of great significance.
Chinese patent publication No. CN109620646B discloses a forearm internal/external rotation mechanism for upper limb rehabilitation training device, which is characterized by comprising: a rotation axis offset mechanism (comprising a case, a motor arranged in the case, an internal and external rotation base connected with the case, etc.); the wrist reversing mechanism comprises a connecting shaft, a wrist reversing shell, an upper positioning pin hole, a lower positioning pin hole and the like, wherein the tail end of the connecting shaft is connected with the case and is provided with a unthreaded hole, the wrist reversing shell is connected with the connecting shaft, and the centripetal end of the wrist reversing shell is symmetrically provided with a tapered upper positioning pin hole and a tapered lower positioning pin hole; and the supporting plate reversing mechanism comprises a T-shaped connecting pipe riveted with the wrist reversing shell, and a first pipe clamp and a second pipe clamp which are arranged at the positions of thin pipes at the two ends of the T-shaped connecting pipe. The device has a certain rehabilitation training effect, but is too complex and not suitable for daily living of patients, and meanwhile, the measurement result is inaccurate; chinese patent publication No. CN111888194A discloses a method, system, apparatus and storage medium for upper limb rehabilitation training, wherein the method comprises: acquiring first posture data of each joint on a mechanical arm driven by an upper limb of a patient, and acquiring first upper limb characteristic information of the patient according to the first posture data; selecting a first training prescription from a preset rehabilitation training prescription library according to the first upper limb characteristic information; acquiring a first training parameter of a first training prescription, and controlling a mechanical arm to drive the upper limb of a patient to perform rehabilitation training according to the first training parameter; and acquiring second posture data and a moment value of each joint on the mechanical arm, adjusting the first training parameter according to the second posture data and the moment value to obtain a second training parameter, and controlling the mechanical arm to drive the upper limb of the patient to perform rehabilitation training according to the second training parameter. But the device has single measuring signal, thereby being incapable of reflecting other damaged conditions of the hand of the patient and being not suitable for being used as rehabilitation monitoring equipment.
Disclosure of Invention
The invention aims to provide a hand robot suitable for activities such as difficult large-scale bending and grabbing of fingers with unsound hand functions of a patient, designs a rehabilitation monitoring and evaluation hand robot, and aims to solve the problems of hand shortage, evaluation subjectivity and the like of a traditional therapist. The hand robot provided by the invention is suitable for patients with incompetent hand functions, has small volume and light weight, is convenient to wear, and does not cause psychological burden to the patients.
A wearable device for upper limb rehabilitation and assessment comprises a glove body and a rehabilitation training and monitoring device, wherein the rehabilitation training and monitoring device is arranged on the glove body;
the rehabilitation training and monitoring device comprises a heart rate sensor, pneumatic muscles, a pneumatic muscle driving and adjusting device, an EMG (electromyography) sensor and a main control unit, wherein the heart rate sensor is arranged on the wrist part of the glove body, the pneumatic muscles are arranged on the finger parts of the glove body, the pneumatic muscle driving and adjusting device is arranged on the back of the hand part of the glove body and is connected with the pneumatic muscles for control, the EMG electromechanical sensor is arranged on the upper arm part of the glove body, and the main control unit is connected with other devices and is arranged on the glove body;
the main control unit is communicated with the upper computer, sends data collected by each sensor, and receives a pneumatic muscle control command to the pneumatic muscle driving and adjusting device.
Further, the glove is an elastic mitten comprising an upper arm, a lower arm and a palm portion.
Furthermore, the AD8232 sensor is adopted, the electrocardiosignals of PR and QT intervals are obtained by the aid of an amplifier, and an electrocardiogram is drawn to serve as the output of the analog quantity.
Further, the specific implementation steps of heart rate evaluation by the heart rate sensor are as follows: collecting heart rate data of a wrist of a collector, acquiring original data during hand rehabilitation training and amplifying the data; transmitting the amplified data to an upper computer, filtering the amplified data, performing frequency domain analysis on the filtered data, judging whether the intensity is overlarge, if so, finishing, otherwise, preprocessing the filtered data, and performing feature extraction on the preprocessed data; training the extracted characteristic values by using a neural network to obtain a training model; inputting data to test the model; and obtaining a final network model, classifying the heart rate degree of the patient and evaluating.
Further, the EMG muscle electric sensor adopts a dual-output mode and outputs an EMG pulse signal and a muscle electric original signal.
Further, the specific implementation steps for muscle force evaluation by an EMG electromyograph sensor are as follows: acquiring arm data of an acquirer, acquiring original data during hand rehabilitation training and amplifying the data; performing analog-to-digital conversion on the amplified signal, and transmitting the amplified signal to an upper computer; filtering the data, eliminating isolated noise points by adopting median filtering, and then obtaining effective data by adopting band-pass filtering; detecting the muscle strength degree according to the working principle of the sensor; and grading and scoring the muscle strength degrees according to the corresponding evaluation table.
Further, the main control unit is arranged on the lower arm part of the glove body, and whether the glove is opened or not is monitored through manual setting of the button.
Further, the motor-driven adjusting device is used as an air pump to inflate or deflate the pneumatic muscle to control the action of the pneumatic muscle.
The invention achieves the following beneficial effects: according to the technical scheme, aiming at the hand function of the patient, the scheme suitable for carrying out rehabilitation monitoring and evaluation on the patient with the hand function is provided by analyzing the relevant signals of the hand, the relevant signals are collected by the sensor, the measured signals are detected and graded or classified, and the recovery condition of the hand of the patient is judged.
Drawings
Fig. 1 is a schematic structural diagram of the wearable device for upper limb rehabilitation and assessment in the embodiment of the present invention.
Fig. 2 is a block diagram of a hand heart rate monitoring and evaluating structure according to an embodiment of the present invention.
FIG. 3 is a block diagram of an arm muscle force monitoring and evaluating structure according to an embodiment of the present invention.
FIG. 4 is a block diagram of a training selection mode and monitoring device according to an embodiment of the present invention.
In the figure, 1-a glove body, 2-pneumatic muscles, 3-a heart rate sensor, 4-a main control unit, 5-an EMG electromyography sensor and 6-a pneumatic muscle driving and adjusting device.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
Referring to fig. 1, a wearable device body 1 for upper limb rehabilitation and assessment and a rehabilitation training and monitoring device in an embodiment of the present invention; the glove body 1 adopts an elastic lengthening equestrian glove to cover all parts of the upper arm, the lower arm and the upper limb of the palm of a user. The rehabilitation training and monitoring device is sleeved and fixed on the glove body 1 in the position shown in figure 1 and comprises a heart rate sensor 3, pneumatic muscles 2, an EMG (electromagnetic EMG) sensor 5, a pneumatic muscle driving and adjusting device 6 and a main control unit 4.
The heart rate sensor 3 in the wearable device for monitoring and evaluating the upper limb rehabilitation in the embodiment of the invention adopts an AD8232 sensor, the AD8232 is single-conductor and low in cost, and is used for measuring the electric signal of the heart rate of the heart, and the electric signal can be used for drawing an electrocardiogram or electrocardiogram and used as the output of analog quantity. The electrocardiosignal is very tiny and is easy to have external interference, and the AD8232 single-lead heart rate monitor can help to obtain a definite signal of PR and QT intervals through an amplifier.
The split charging position of the heart rate sensor 3 is as shown in an example in fig. 1, signals collected by the heart rate sensor 3 are transmitted to the most terminal of the collecting device, namely a controller, through I2C, and the controller selects msp430 (without being limited to the selection) to carry out collection control on each sensor of the lower computer; due to interference in all aspects in the acquisition process, after the controller transmits data to the PC end of the upper computer, the stored data is filtered by selecting a dynamic Kalman filtering method, so that optimal estimation is obtained; and performing frequency domain analysis on the filtered data to judge whether the intensity is overlarge, if not, performing feature extraction on the data, and selecting proper features to perform neural network training, so that a proper training model is further obtained, and a foundation is laid for later evaluation.
The electromyographic acquisition sensor in the embodiment of the invention adopts an EMG electromyographic sensor, adopts a double-output mode (EMG pulse signals and electromyographic original signals), and is made of materials with long service life and high sensitivity so as to realize the height control of the rehabilitation equipment. The split positions of the electromyographic acquisition units are shown in an example of fig. 1, signals acquired by the EMG electromyographic sensors are transmitted to a final end msp430 of the acquisition device through an I2C, and because the acquired signals are analog signals and have small amplitude, the acquired signals need to be uploaded to a PC end of an upper computer through a controller for amplification and analog-to-digital conversion for subsequent needs; filtering the stored data by using MATLAB to obtain optimal data; and obtaining the grade of the muscle force of the patient according to the relation between the sensor and the corresponding electric signal.
Referring to fig. 1, the overall structure diagram of the wearable device for upper limb rehabilitation monitoring and assessment in the embodiment of the present invention is acquired by the signal acquisition device at the same time, and as shown in fig. 2 to 3, after performing relevant preprocessing work through the PC computer, the signal acquisition device performs grading and grading analysis, and finally, the rehabilitation level of the patient is obtained comprehensively according to the following table.
TABLE 1 Heart Rate and intensity mapping
| Recovered maximum heart rate | Type of intensity |
| >80％Hmax | High strength |
| 60-80％Hmax | Moderate intensity |
| <60％Hmax | Low strength |
Maximum heart rate Hmax 220-age.
TABLE 2 muscle force rating score
| Rank of | Name (R) | Standard of merit | Is equal to the normal muscle strength% |
| 0 | 0 | No detectable muscle contraction | 0 |
| 1 | Micro-scale | With slight contraction and no joint movement | 10 |
| 2 | Difference (D) | In the state of weight reductionCan do the full range movement of the joint under the state | 25 |
| 3 | Shang Ke | Can resist the full range of motion of the joint caused by gravity, but can not resist resistance | 50 |
| 4 | Good effect | Can resist gravity and certain resistance to move | 75 |
| 5 | Is normal | Can resist gravity and certain resistance to move | 100 |
This wearable device is applicable to the rhythm of the heart aassessment of recovered monitoring of hand function infirm and aassessment, and the concrete step of realizing is as follows: collecting heart rate data of a wrist of a collector, acquiring original data during hand rehabilitation training and amplifying the data; transmitting the amplified data to an upper computer, filtering the amplified data, performing frequency domain analysis on the filtered data, judging whether the intensity is overlarge, if so, finishing, otherwise, preprocessing the filtered data, and performing feature extraction on the preprocessed data; training the extracted characteristic values by using a neural network to obtain a training model; inputting data to test the model; and obtaining a better network model, classifying and evaluating the heart rate degree of the patient.
This wearable device is applicable to the recovered monitoring and aassessment of hand function infirm, and the specific realization step of muscle power aassessment is as follows: acquiring arm data of an acquirer, acquiring original data during hand rehabilitation training and amplifying the data; performing analog-to-digital conversion on the amplified signal, and transmitting the amplified signal to an upper computer; filtering the data, eliminating isolated noise points by adopting median filtering, and then obtaining effective data by adopting band-pass filtering; detecting the muscle strength degree according to the working principle of the sensor; and grading and scoring the muscle strength degrees according to the corresponding evaluation table.
This wearable device still is applicable to the passive rehabilitation training of the initiative of hand function infirm, and concrete realization step is as follows: performing active or passive division, if active, performing antagonistic different-grade motions with the rehabilitation evaluation device, and timing and scoring the performed antagonistic training; if passive, the pneumatic muscle driving and adjusting device is started to charge/discharge the air pump of the pneumatic muscle, and then the rehabilitation robot makes different-level movements of compliance and performs timing and scoring for the compliance training.
The wearable device for monitoring and evaluating upper limb rehabilitation comprises a manual monitoring on/off strategy: and whether each signal acquisition and rehabilitation training project is started or not is monitored through manual setting of the buttons.
The above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above embodiment, but equivalent modifications or changes made by those skilled in the art according to the present disclosure should be included in the scope of the present invention as set forth in the appended claims.
Claims (8)
1. The utility model provides a wearable device of recovered and aassessment of upper limbs which characterized in that:
the wearable device comprises a glove body and a rehabilitation training and monitoring device, wherein the rehabilitation training and monitoring device is arranged on the glove body;
the rehabilitation training and monitoring device comprises a heart rate sensor, pneumatic muscles, a pneumatic muscle driving and adjusting device, an EMG (electromyography) sensor and a main control unit, wherein the heart rate sensor is arranged on the wrist part of the glove body, the pneumatic muscles are arranged on the finger parts of the glove body, the pneumatic muscle driving and adjusting device is arranged on the back of the hand part of the glove body and is connected with the pneumatic muscles for control, the EMG electromechanical sensor is arranged on the upper arm part of the glove body, and the main control unit is connected with other devices and is arranged on the glove body;
the main control unit is communicated with the upper computer, sends data collected by each sensor, and receives a pneumatic muscle control command to the pneumatic muscle driving and adjusting device.
2. The wearable device for upper limb rehabilitation and assessment according to claim 1, wherein: the glove is an elastic mitten comprising an upper arm, a lower arm and a palm portion.
3. The wearable device for upper limb rehabilitation and assessment according to claim 1, wherein: the AD8232 sensor is adopted, an amplifier is used for helping to obtain electrocardiosignals of PR and QT intervals, and an electrocardiogram is drawn to serve as the output of analog quantity.
4. The wearable device for upper limb rehabilitation and assessment according to claim 3, wherein: the specific implementation steps of heart rate assessment by a heart rate sensor are as follows: collecting heart rate data of a wrist of a collector, acquiring original data during hand rehabilitation training and amplifying the data; transmitting the amplified data to an upper computer, filtering the amplified data, performing frequency domain analysis on the filtered data, judging whether the intensity is overlarge, if so, finishing, otherwise, preprocessing the filtered data, and performing feature extraction on the preprocessed data; training the extracted characteristic values by using a neural network to obtain a training model; inputting data to test the model; and obtaining a final network model, classifying the heart rate degree of the patient and evaluating.
5. The wearable device for upper limb rehabilitation and assessment according to claim 1, wherein: the EMG muscle electric sensor adopts a double-output mode and outputs an EMG pulse signal and a muscle electric original signal.
6. The wearable device for upper limb rehabilitation and assessment according to claim 5, wherein: the specific implementation steps of the muscle feeding force evaluation through the EMG muscle electric sensor are as follows: acquiring arm data of an acquirer, acquiring original data during hand rehabilitation training and amplifying the data; performing analog-to-digital conversion on the amplified signal, and transmitting the amplified signal to an upper computer; filtering the data, eliminating isolated noise points by adopting median filtering, and then obtaining effective data by adopting band-pass filtering; detecting the muscle strength degree according to the working principle of the sensor; and grading and scoring the muscle strength degrees according to the corresponding evaluation table.
7. The wearable device for upper limb rehabilitation and assessment according to claim 1, wherein: the main control unit is arranged on a lower arm part of the glove body and is used for monitoring whether to be opened or not through manual setting of a button.
8. The wearable device for upper limb rehabilitation and assessment according to claim 1, wherein: the pneumatic muscle driving and adjusting device is used as an air pump to inflate or deflate the pneumatic muscle to control the action of the pneumatic muscle.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101181176A (en) * | 2007-12-10 | 2008-05-21 | 华中科技大学 | Apparel type robot for healing hand function and control system thereof |
| CN104173124A (en) * | 2014-08-29 | 2014-12-03 | 电子科技大学 | Upper limb rehabilitation system based on biological signals |
| CN109568083A (en) * | 2018-12-15 | 2019-04-05 | 华南理工大学 | A kind of upper limb rehabilitation robot training system of multi-modal interaction |
| US20200206567A1 (en) * | 2018-12-27 | 2020-07-02 | MAN & TEL Co., Ltd. | Training equipment to improve the ability of cognition and memory and the muscle power of upper and lower limb and training method thereof |
| CN112022619A (en) * | 2020-09-07 | 2020-12-04 | 西北工业大学 | Multi-mode information fusion sensing system of upper limb rehabilitation robot |
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- 2021-07-19 CN CN202110815014.9A patent/CN113545750A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101181176A (en) * | 2007-12-10 | 2008-05-21 | 华中科技大学 | Apparel type robot for healing hand function and control system thereof |
| CN104173124A (en) * | 2014-08-29 | 2014-12-03 | 电子科技大学 | Upper limb rehabilitation system based on biological signals |
| CN109568083A (en) * | 2018-12-15 | 2019-04-05 | 华南理工大学 | A kind of upper limb rehabilitation robot training system of multi-modal interaction |
| US20200206567A1 (en) * | 2018-12-27 | 2020-07-02 | MAN & TEL Co., Ltd. | Training equipment to improve the ability of cognition and memory and the muscle power of upper and lower limb and training method thereof |
| CN112022619A (en) * | 2020-09-07 | 2020-12-04 | 西北工业大学 | Multi-mode information fusion sensing system of upper limb rehabilitation robot |
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