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Wearable freezing gait intelligent monitoring and walking aid device

A technology of intelligent monitoring and gait, which is applied in diagnostic recording/measurement, appliances to help people walk, medical science, etc., can solve the problem of not being able to effectively improve FOG symptoms, intelligent control of laser emission, rhythm and coordination of gait See issues such as effective improvement

Active Publication Date: 2018-07-24
SHANGHAI NINTH PEOPLES HOSPITAL AFFILIATED TO SHANGHAI JIAO TONG UNIV SCHOOL OF MEDICINE
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] However, the laser walking aid device disclosed in the above-mentioned patent does not intelligently control the laser emission, and cannot monitor the occurrence of frozen gait of the patient
The device is manually turned on. Once it is turned on, the laser line is always on, which is likely to cause visual fatigue to the patient, weaken the patient's attention when walking, and cannot effectively improve the symptoms of FOG.
Because the occurrence of FOG is related to the environment and the situation, that is, FOG usually occurs in short-term or instantaneous situations such as patients starting, turning, narrow space, approaching the end point, and emotional tension, and FOG rarely occurs during the patient’s walking process, so there are reports in the literature , this continuous laser does not play a role in the patient's walking process, on the contrary, it easily interferes with the patient's normal walking
Secondly, the study also found that continuous laser suggestion does not have the function of rehabilitation training, that is, when patients use this type of laser walking aid equipment, the patient's gait characteristic parameters, such as gait rhythm and coordination, are not effective. improve
[0005] To sum up, there is no wearable intelligent laser monitoring that can automatically detect the occurrence of patients' frozen gait through motion pattern recognition technology, control the time and frequency of laser emission in real time, and achieve the dual effects of improving patients' frozen gait symptoms and rehabilitation training. with mobility aids

Method used

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  • Wearable freezing gait intelligent monitoring and walking aid device
  • Wearable freezing gait intelligent monitoring and walking aid device
  • Wearable freezing gait intelligent monitoring and walking aid device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0066] refer to figure 1 , in this embodiment, the correction to generate the frozen gait detection system specifically includes:

[0067] Step 1, Data Acquisition

[0068] Acquire the patient's three-axis acceleration data through a nine-axis sensor (including a three-axis accelerometer, a three-axis gyroscope, and a three-axis geomagnetic sensor), that is, the acceleration data of the X, Y, and Z axes, and the collected data is saved to the SD card by the microcontroller middle.

[0069] While collecting the acceleration data of the patient, the walking process of the patient is photographed synchronously.

[0070] Step 2, freeze gait offline recognition

[0071] The patient's acceleration data is imported from the SD card to the PC computer, and the developed frozen gait intelligent recognition algorithm is applied on the computer to identify and classify the patient's frozen gait. This step specifically includes:

[0072] 1) FOG encoding and label setting, constructin...

Embodiment 2

[0116] In this embodiment, on the basis of Embodiment 1, step 2 also includes:

[0117] 4) Determination of sliding time window parameters (external parameters)

[0118] In part 2) of step 2, when extracting FOG features, the acceleration data is processed in each time window, and the sliding time window is set as an adjustable parameter. The larger the sliding time window, the higher the prediction accuracy, but it will be It brings a large system delay, so it is necessary to make a compromise adjustment to the parameter setting of the sliding time window to obtain a higher-precision FOG recognition result.

[0119] The present invention presets n incremental parameters for the sliding time window, and obtains the FOG recognition result under each sliding time window parameter by referring to the formula (4), and obtains the FOG prediction time series matrix y2.

[0120] According to Table 1, the confusion matrix of the two classifications was constructed, and the accuracy, ...

Embodiment 3

[0123] Different from Embodiment 1 or 2, in this embodiment, in the second) part of step 2, M patient data sets are selected, one is selected as a test set each time, and the remaining M-1 are used as a training set, which can be obtained The best general prediction model applicable to all patients can improve the generalization ability of the model and reduce the generalization error when validating new data.

[0124] The FOG prediction model obtained by the method of the present invention is transplanted to the microcontroller, and when the frozen gait of the patient is detected, that is, when the prediction output is 1, the microcontroller sends a control signal to the laser emitting system to turn on the laser light source. When the end of the frozen gait is detected immediately, the output of the FOG prediction model is -1, and the microcontroller sends a control signal to the laser light emitting system to turn off the laser light source.

[0125] refer to Figure 4 , w...

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Abstract

The invention provides a wearable freezing gait intelligent monitoring and walking aid device. The device comprises a triaxial accelerometer, a triaxial gyroscope, a triaxial geomagnetic sensor, a microcontroller and a power source, wherein the triaxial accelerometer, the triaxial gyroscope and the triaxial geomagnetic sensor are used for collecting the triaxial acceleration of an X axle, Y axle and Z axle; the microcontroller is used for detecting the state of freezing gait; and the power source provides power for the microcontroller. According to the wearable freezing gait intelligent monitoring and walking aid device, an FOG intelligent recognition and classification algorithm is developed for the freezing gait which is a special gait obstacle disease, an individual difference error caused by setting a critical threshold for judging acceleration is overcome, and thereby the accuracy degree of FOG recognition is improved. According to the wearable freezing gait intelligent monitoringand walking aid device, compared with an existing continuous laser guidance mode, the number of the freezing gait of a patient is significantly reduced under the rhythmic laser guidance, the freezingduration time is shortened, the rhythmicity and coordination of the gait are significantly improved (p (0.05), and the effect of rehabilitation training is achieved when the symptom of the freezing gait is improved.

Description

technical field [0001] The invention relates to equipment in the field of intelligent medical assistance, in particular to a wearable intelligent monitoring and walking aid equipment for freezing gait. Background technique [0002] Freezing of gait (FOG) is a common abnormal gait symptom in Parkinson's disease (PD). Cross-sectional studies have shown that 30%-60% of PD patients are accompanied by FOG. In other neurodegenerative diseases, such as multiple system atrophy and progressive supranuclear palsy, the incidence of FOG is higher than that of PD. FOG has the characteristics of sudden occurrence, short duration, situational triggering and unpredictable, which can easily cause patients to fall and seriously affect the quality of life of patients. However, in terms of the treatment of frozen gait, the effect of drugs on the symptoms of frozen gait is not ideal, and even for some patients, drugs can aggravate the symptoms of FOG. Therefore, exploring new methods for non-d...

Claims

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Application Information

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IPC IPC(8): A61B5/11A61H3/00
CPCA61B5/112A61B5/4082A61B5/6801A61B2562/0219A61H3/00
Inventor 顾冬云李波唐亮
Owner SHANGHAI NINTH PEOPLES HOSPITAL AFFILIATED TO SHANGHAI JIAO TONG UNIV SCHOOL OF MEDICINE
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