CN-122006215-A - Control method, device and medium of intelligent hand dynamic balance training device

CN122006215ACN 122006215 ACN122006215 ACN 122006215ACN-122006215-A

Abstract

The application discloses a control method, a device and a medium of an intelligent hand dynamic balance training device, which relate to the technical field of rehabilitation instruments, acquire pressure data, inclination angles and surface electromyographic signals acquired by the hand dynamic balance training device, respectively determine the pressure dispersion degree of the pressure data, the absolute value of the inclination angles and the electromyographic vibration index of the surface electromyographic signals, determine a balance deviation index according to the pressure dispersion degree, the absolute value of the inclination angles and the electromyographic vibration index, determine a balance state based on the balance deviation index, evaluate the hand balance state from dynamics, kinematics and physiology by combining the pressure data, the kinematic data and the surface electromyographic signals, improve the accuracy and the comprehensiveness of hand balance evaluation, determine a training mode according to the balance state, adjust the control current of a magnetorheological damper and/or the auxiliary torque of a servo motor based on the training mode, and realize self-adaptive adjustment resistance or assistance so as to ensure the safety and the effectiveness of training.

Inventors

HE CHUAN
LIU LINKUI
YANG DENGHUI
WEN DANDAN
FAN BIANRU

Assignees

河南省智慧康养设备产业研究院有限公司

Dates

Publication Date: 20260512
Application Date: 20260130

Claims (10)

1. The control method of the intelligent hand dynamic balance training device is characterized by comprising the following steps of: acquiring pressure data acquired by a pressure sensor of a hand dynamic balance training device, an inclination angle acquired by a gyroscope and a surface electromyographic signal acquired by a surface electromyographic sensor; respectively determining the pressure dispersion of the pressure data, the absolute value of the inclination angle and the electromyographic shock index of the surface electromyographic signal; Determining a balance shift index according to the pressure dispersion, the absolute value of the inclination angle and the myoelectric shock index, and determining a balance state based on the balance shift index; and determining a training mode according to the balance state, and adjusting control current of the magnetorheological damper and/or auxiliary torque of a servo motor based on the training mode, wherein the magnetorheological damper is used for providing resistance in the training process, and the servo motor is used for providing assistance in the training process.
2. The method of claim 1, wherein determining the pressure dispersion of the pressure data comprises: calculating the pressure dispersion according to a first preset formula; The first preset formula is: ; Wherein, the In order to provide a degree of pressure dispersion, Is the first The pressure data detected by the pressure sensors, Is the arithmetic mean of the four pressure data.
3. The method of claim 1, wherein determining the electromyographic shock index of the surface electromyographic signal comprises: Calculating the myoelectric shock index according to a second preset formula; The second preset formula is: ; Wherein, the Is an index of myoelectric shock and vibration, For integration of the surface electromyographic signals over a frequency range of 4Hz to 8Hz, For integration of the surface electromyographic signals over the full frequency band from 0Hz to the maximum frequency, Is the power spectral density of the surface electromyographic signals.
4. The method of claim 1, wherein determining a balance shift index based on the pressure dispersion, the absolute value of the inclination angle, and the myoelectric shock index comprises: calculating the balance offset index according to a third preset formula; The third preset formula is: ; Wherein, the In order to balance the offset index, As a coefficient of pressure dispersion, As a coefficient of the absolute value of the inclination angle, Is a coefficient of the myoelectric shock index, In order to provide a degree of pressure dispersion, As the absolute value of the inclination angle, Is the myoelectric shock index.
5. The method of claim 1, wherein determining a balance state based on the balance offset index comprises: determining a fatigue index based on the surface electromyographic signal; Determining a safety threshold according to a base threshold, an adjustment coefficient and the fatigue index; If the balance offset index is less than or equal to the safety threshold, the balance state is a first state; if the balance offset index is greater than the safety threshold and less than or equal to the safety threshold of a preset multiple, the balance state is a second state; And if the balance offset index is larger than the safety threshold value of the preset multiple, the balance state is a third state.
6. The method of claim 5, wherein determining a fatigue index based on the surface electromyographic signals comprises: Calculating the pressure dispersion according to a fourth preset formula; The fourth preset formula is: ; Wherein, the Is that The fatigue index at the moment of time, The root mean square value of the surface electromyographic signals in the initial state, Is that Surface electromyographic signal root mean square value at time.
7. The method according to claim 6, wherein determining a training pattern according to the balance state, and adjusting a control current of the magnetorheological damper and/or an assist torque of the servo motor based on the training pattern, comprises: If the balance state is the first state, determining that the training mode is an active mode, and determining a target control current of the magnetorheological damper according to the current pressure dispersion and the current inclination angle in the active mode training process so as to adjust the control current of the magnetorheological damper to the target control current; if the balance state is the third state, determining that the training mode is a passive mode, and determining a target auxiliary torque output by the servo motor according to the current balance deviation index and the current myoelectric shock index in the passive mode training process so as to adjust the auxiliary torque of the servo motor to the target auxiliary torque; and if the balance state is the second state, determining that the training mode is a mixed mode, determining a duration ratio of a resistance mode to a booster mode according to the current balance deviation index in the mixed mode training process, and controlling the start and stop of the magnetorheological damper and the servo motor according to the duration ratio.
8. The control method of an intelligent hand dynamic balance training apparatus according to any one of claims 1 to 7, further comprising: generating a virtual scene by utilizing a VR engine; Adjusting the background color of the virtual scene and the vibration frequency of a handle of the hand dynamic balance training device according to the current balance offset index; and controlling the voice broadcasting device to play the action adjusting instruction according to the current inclination angle, and adjusting the broadcasting frequency of the action adjusting instruction according to the current balance offset index.
9. The control device of the intelligent hand dynamic balance training device is characterized by comprising a memory, a control unit and a control unit, wherein the memory is used for storing a computer program; A processor for implementing the steps of the control method of the intelligent hand dynamic balance training apparatus according to any one of claims 1 to 8 when executing the computer program.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the control method of the intelligent hand dynamic balance training apparatus according to any one of claims 1 to 8.

Description

Control method, device and medium of intelligent hand dynamic balance training device Technical Field The application relates to the technical field of rehabilitation instruments, in particular to a control method, a control device and a medium of an intelligent hand dynamic balance training device. Background The dynamic balance capability of the hands is the basis for fine operations (such as writing, holding and operating tools), is very important for rehabilitation of patients suffering from cerebral apoplexy, parkinsonism, spinal cord injury and the like, and is also the key for improving the operation stability of professionals such as athletes, surgeons and the like. Conventional hand balance training has often relied on manual guidance by therapists or simple instruments (e.g., grip, balance plate) using fixed resistance. At present, a few hand rehabilitation robots or training devices exist in the market, and the defects that only kinematic data (such as angles) or dynamic data (such as pressure) are collected, physiological mechanisms of hand balance cannot be comprehensively and accurately estimated, a training mode is single, self-adaptive resistance or assistance adjustment cannot be carried out, and the safety and the effectiveness of training cannot be considered. Thus, how to improve the accuracy and comprehensiveness of hand balance assessment, and how to adaptively adjust resistance or assistance to ensure the safety and effectiveness of training is a problem that needs to be solved by those skilled in the art. Disclosure of Invention The application aims to provide a control method, a control device and a control medium of an intelligent hand dynamic balance training device, which are used for solving the problems that the existing training equipment cannot comprehensively and accurately evaluate the physiological mechanism of hand balance and cannot perform self-adaptive resistance or power-assisted adjustment. In order to solve the technical problems, the application provides a control method of an intelligent hand dynamic balance training device, which comprises the following steps: acquiring pressure data acquired by a pressure sensor of a hand dynamic balance training device, an inclination angle acquired by a gyroscope and a surface electromyographic signal acquired by a surface electromyographic sensor; respectively determining the pressure dispersion of the pressure data, the absolute value of the inclination angle and the electromyographic shock index of the surface electromyographic signal; Determining a balance shift index according to the pressure dispersion, the absolute value of the inclination angle and the myoelectric shock index, and determining a balance state based on the balance shift index; and determining a training mode according to the balance state, and adjusting control current of the magnetorheological damper and/or auxiliary torque of a servo motor based on the training mode, wherein the magnetorheological damper is used for providing resistance in the training process, and the servo motor is used for providing assistance in the training process. In an alternative embodiment, determining the pressure dispersion of the pressure data comprises: calculating the pressure dispersion according to a first preset formula; The first preset formula is: ; Wherein, the In order to provide a degree of pressure dispersion,For the pressure data detected by the ith pressure sensor,Is the arithmetic mean of the four pressure data. In an alternative embodiment, determining the electromyographic shock index of the surface electromyographic signal comprises: Calculating the myoelectric shock index according to a second preset formula; The second preset formula is: ; Wherein, the Is an index of myoelectric shock and vibration,For integration of the surface electromyographic signals over a frequency range of 4Hz to 8Hz,For integration of the surface electromyographic signals over the full frequency band from 0Hz to the maximum frequency,Is the power spectral density of the surface electromyographic signals. In an alternative embodiment, determining a balance shift index from the pressure dispersion, the absolute value of the tilt angle, and the electromyographic shock index includes: calculating the balance offset index according to a third preset formula; The third preset formula is: ; Wherein, the In order to balance the offset index,As a coefficient of pressure dispersion,As a coefficient of the absolute value of the inclination angle,Is a coefficient of the myoelectric shock index,In order to provide a degree of pressure dispersion,As the absolute value of the inclination angle,Is the myoelectric shock index. In an alternative embodiment, determining the balance state based on the balance offset index includes: determining a fatigue index based on the surface electromyographic signal; Determining a safety threshold according to a base threshold, an adjustment coefficient and