CN111631731B - Near-infrared brain function and touch force/motion information fusion assessment method and system - Google Patents
Near-infrared brain function and touch force/motion information fusion assessment method and system Download PDFInfo
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Abstract
The invention relates to an evaluation method and system for fusion of near-infrared brain function and touch force/motion information. The method comprises the following steps: arranging a light source and a probe of the near-infrared brain function detection equipment; arranging a finger angle acquisition module, an arm positioning acquisition module and a hand pressure acquisition module; selecting a specific training scene and a specific task in a virtual scene module, and monitoring a hand joint bending signal and forearm spatial motion signals; monitoring a hand joint bending signal, a forearm and forearm spatial motion signal and a hand grip strength signal in a real rehabilitation training scene; transmitting the signals acquired in the steps to an evaluation analysis module; and the evaluation analysis module performs brain function index calculation, touch force/movement information analysis calculation and near-infrared brain function and touch force/movement information fusion evaluation calculation according to the received signals, and analyzes to obtain evaluation indexes of upper limb movement capacity and brain-upper limb synergistic capacity.
Description
Technical Field
The invention relates to the field of rehabilitation aids, in particular to a dynamic quantitative evaluation method and system for fusion of near-infrared brain function and touch force/motion information, which are used for evaluating the motor capacity of upper limbs and the cooperative capacity of brain limbs.
Background
More than 200 million people suffer from stroke each year in our country, and about 2/3 of stroke survivors will leave different degrees of dysfunction. Meanwhile, accidents or diseases such as brain trauma and cerebral palsy can also cause brain function damage, and further cause limb movement dysfunction. These limb dysfunctions bring heavy care costs and burdens to patients, families and society, and high-quality and efficient rehabilitation training is the most important means for solving the current problems and is also a necessary choice for helping patients to recover life independence and return to society. The existing rehabilitation technical field forms a brain-limb cooperative rehabilitation mode, provides a scientific and ordered rehabilitation strategy for patients by adopting a method of effectively integrating brain function regulation and limb movement therapy, and promotes the plasticity change of the central nervous system and the recovery of limb movement function. In the process of brain-limb cooperative rehabilitation, effective rehabilitation evaluation on patients is the basis for formulating a rehabilitation training strategy.
Related technologies and methods exist for the rehabilitation assessment of upper limbs. Chinese patent CN 104850216 a relates to a glove with a pressure tactile sensor, which utilizes a pressure sensing element, an accelerometer element and a gyroscope element to make an electronic glove or a bracelet capable of sensing the downward pressure of fingers and the current angle, posture and motion of a palm of a user wearing the electronic glove, and can be used for hand motion data monitoring and motion capability assessment, but lack brain function parameter measurement; chinese patent CN 209377571U relates to a tactile stimulation device and a tactile brain atlas measurement system, which provides tactile stimulation through a telescopic driving piece, and utilizes a nuclear magnetic machine to measure the tactile brain atlas, and lacks the measurement of motion information; chinese patent CN 110364239A relates to a limb movement rehabilitation training system based on pseudo-touch, which realizes the pseudo-touch by observing the speed change of a cursor following a target object in a user interface through a visual method, helps patients to carry out rehabilitation training, focuses more on exercising and evaluating cognitive ability, and is also lack of the measurement of movement information; chinese patent CN 110192861A relates to an intelligent auxiliary brain function real-time detection system, which utilizes AR (augmented reality) equipment and a force feedback device to stimulate patients with brain dysfunction, detects the brain function active condition in real time through magnetic resonance, judges the brain function damaged condition, focuses on brain function evaluation, and lacks evaluation means for crossing brain function and motion information; chinese patent CN 104706499A relates to an upper limb cranial nerve rehabilitation training system and a training method, which has a mechanical structure capable of driving the upper limb of a trainer to move and having a force feedback function, carries out rehabilitation training by combining a virtual reality scene, and also lacks an evaluation means for crossing brain function and movement information; chinese patent CN 107577343 a relates to an attention training and evaluating device based on haptic feedback and electroencephalogram signal analysis, which trains a subject through haptic feedback, synchronously records electroencephalogram signals, combines the performance level of behaviours and physiological indexes, although it has an evaluation means of crossing brain function and behavioral performance, focuses on cognitive training and lacks the measurement, analysis and application of motion parameters.
In summary, in the field of rehabilitation assessment of upper limbs, a quantitative assessment technology and a quantitative assessment method for dynamic brain function in the limb exercise training process and a dynamic quantitative assessment method and a dynamic quantitative assessment system for fusing brain function and kinetic information are lacking at present, which are urgently needed for improving the rehabilitation effect of patients.
Disclosure of Invention
Based on the above problems in the prior art, the present invention is to provide a dynamic quantitative evaluation method and system for fusion of near-infrared brain function and touch force/motion information. The dynamic quantitative evaluation method and the system can fuse near-infrared cerebral blood oxygen signal parameters, upper limb multi-degree-of-freedom motion parameters and grip strength data information, provide tactile feedback based on a virtual reality scene, and realize dynamic quantitative evaluation of upper limb motion capability and brain-upper limb cooperative capability.
In order to achieve the above object, one aspect of the present invention provides a method for evaluating fusion of near-infrared brain function and touch force/motion information, comprising the steps of:
1) arranging a light source and a probe of the near-infrared brain function detection equipment according to the near-infrared brain function detection light source probe template;
2) arranging a finger angle acquisition module, an arm positioning acquisition module and a hand pressure acquisition module, wherein the positions of a forearm inertial sensor and a large arm inertial sensor of the arm positioning acquisition module and the positions of a flexible glove of the finger angle acquisition module are positioned on the same straight line;
3) selecting a specific training scene and task in a virtual scene module, monitoring hand joint bending signals by using a finger angle acquisition module, and monitoring forearm and forearm spatial motion signals by using an arm positioning acquisition module;
4) in a real rehabilitation training scene, a finger angle acquisition module is used for monitoring hand joint bending signals, an arm positioning acquisition module is used for monitoring forearm and large arm space motion signals, and a hand pressure acquisition module is used for monitoring hand grip strength signals;
5) transmitting the signals acquired in the steps 1) and 3) or 4) to an evaluation analysis module;
6) and the evaluation analysis module performs brain function index calculation, touch force/movement information analysis calculation and near-infrared brain function and touch force/movement information fusion evaluation calculation according to the received signals, and analyzes to obtain evaluation indexes of upper limb movement capacity and brain-upper limb synergistic capacity.
According to one embodiment of the invention, in the virtual reality scene of step 3), the tactile feedback module can generate vibration tactile sensation at the finger tip of the user when the virtual object is touched.
In another embodiment of the invention, the near infrared cerebral blood oxygen signal transmitted in step 6) is processed according to the following steps:
according to continuous complex wavelet transform calculation, time domain average calculation, wavelet phase coherence calculation and effect connection calculation based on Bayes inference, obtaining a wavelet amplitude WA, a wavelet phase coherence WPCO, a coupling strength CS and a coupling direction CD, and according to a lateral deviation index calculation rule, dividing the difference of brain function indexes of a certain hemisphere and a contralateral hemisphere by the sum of brain function indexes of the certain hemisphere and the contralateral hemisphere, namely a formula:
an laterality coefficient lWA based on the wavelet amplitude WA, a laterality coefficient lwcpco based on the wavelet phase coherence WPCO, and a laterality coefficient lCS based on the coupling strength CS are calculated.
In a further embodiment of the invention, the hand joint bending signals, forearm and forearm spatial motion signals and hand grip force signals transmitted in 5) are processed according to the following steps:
performing first-order and second-order differential operation on the hand joint bending signal, the forearm and forearm spatial motion signal and the hand grip strength signal in a task training time period to obtain signal change speed and acceleration;
performing interpolation fitting calculation on the hand joint bending signal, the forearm and forearm spatial motion signal and the hand grip strength signal within a task training time period to obtain corresponding interpolation fitting signals;
according to the smoothness calculation method:
and calculating smoothness r, wherein f (n) is an original signal, and F (n) is an interpolation fitting signal to obtain smoothness indexes which represent the smoothness of the hand bending, the spatial motion of the forearm and the big arm and the change of the hand grip.
In yet another embodiment of the present invention, the evaluation calculation of the fusion of near-infrared brain function and touch/motion information is performed according to the following steps:
within a task training time period, performing normalization calculation on the near-infrared cerebral blood oxygen signals, the hand joint bending signals, the forearm and forearm spatial motion signals and the hand grip strength signals acquired by each acquisition channel, and converting the signals into scalar signals;
resampling and calculating scalar signals corresponding to the hand joint bending signals, the forearm and forearm spatial motion signals and the hand grip strength signals, so that the signal frequency of the signals is consistent with the near-infrared cerebral blood oxygen signals;
calculating a Pearson correlation coefficient of a scalar signal of the resampled signal and the near-infrared cerebral blood oxygen signal, and representing the correlation degree of the touch force/motion information and the near-infrared brain function;
and calculating the coupling strength CSm and the coupling direction CDm between the resampling signal and the scalar signal of the near-infrared cerebral blood oxygen signal based on the Bayesian inference effect connection calculation method, and representing the causal relationship between the touch force/motion information and the near-infrared brain function.
In another aspect of the present invention, there is provided a system for evaluating fusion of near-infrared brain function and touch force/motion information, comprising:
the near-infrared brain function acquisition module is used for acquiring near-infrared brain blood oxygen signals of corresponding brain areas and transmitting the acquired near-infrared brain blood oxygen signals to the evaluation analysis module;
the touch force/motion data glove module is used for acquiring hand joint bending signals, forearm and forearm spatial motion signals and hand grip force signals of a user in a virtual reality scene, and acquiring hand joint bending signals, forearm and forearm spatial motion signals and hand grip force signals in a real rehabilitation training scene, and transmitting the signals to the evaluation and analysis module;
and the evaluation analysis module is used for carrying out evaluation analysis calculation according to the near-infrared cerebral blood oxygen signals transmitted by the near-infrared brain function acquisition module, the hand joint bending signals transmitted by the touch force/motion data glove module, the forearm and forearm spatial motion signals and the hand grip force signals.
In one embodiment of the present invention, the near-infrared brain function acquisition module includes:
the light source probe template is used for setting the position of a light source probe for near infrared monitoring and the arrangement of an acquisition channel;
a near-infrared information acquisition module for acquiring near-infrared cerebral blood oxygen signals, an
And the brain function data transmission module is used for transmitting the near-infrared brain blood oxygen signals acquired by the near-infrared information acquisition module to the evaluation analysis module.
In one embodiment of the present invention, a touch force/motion data glove module comprises:
the hand pressure acquisition module is used for acquiring hand grip strength signals in a real rehabilitation training scene and consists of pressure sensor arrays distributed at palms and fingers on the inner side of the data glove;
the finger angle acquisition module is used for acquiring hand joint bending signals in a virtual reality scene and a real rehabilitation training scene;
the arm positioning acquisition module is used for acquiring spatial motion signals of the forearm and the forearm in a virtual reality scene and a real rehabilitation training scene;
the virtual reality scene module is used for reproducing upper limb movement actions in a virtual practical training scene according to the hand joint bending signals acquired by the finger angle acquisition module and the forearm and large arm space movement signals acquired by the arm positioning acquisition module to form virtual reality interaction with a user;
and the tactile feedback module is used for providing vibrotactile sensation at the finger end of the user when the virtual finger end contacts an object in the virtual scene in the simulated practical training scene.
In another embodiment of the present invention, the touch force/motion data glove module further comprises a glove data transmission module for transmitting the hand joint bending signal acquired by the finger angle acquisition module and the forearm and forearm spatial motion signal acquired by the arm positioning acquisition module to the virtual reality scene module, and for transmitting the hand joint bending signal, the forearm and forearm spatial motion signal and the hand grip force signal to the evaluation and analysis module.
According to yet another embodiment of the present invention, the haptic feedback module of the single data glove is comprised of a haptic feedback device located at the finger tip.
The invention also provides a dynamic quantitative evaluation method for fusion of near-infrared brain function and touch force/motion information, which comprises the following steps:
1) establishing connection between a user and a near-infrared brain function acquisition module, and arranging a light source and a probe of a near-infrared brain function detection device according to a near-infrared brain function detection light source probe template, wherein the acquired brain area comprises but is not limited to a motor area cortex and a forehead cortex;
2) establishing connection between a user and a touch force/motion data glove module, wearing the flexible gloves to two hands, wearing the forearm inertial sensor to the outer side of the back of the forearm, wearing the big arm inertial sensor to the outer side of the back of the big arm, enabling the positions of the forearm inertial sensor and the big arm inertial sensor to be in the same straight line with the flexible gloves, and wearing the torso inertial sensor to the middle of the waist and abdomen;
3.1) selecting a specific training scene and task in the virtual scene module, carrying out interactive upper limb training in the virtual scene by a user according to the training scene and the task, monitoring a hand joint bending signal by a finger angle acquisition module, monitoring a forearm and forearm space motion signal by an arm positioning acquisition module, and generating a vibration touch at a finger end by a touch feedback module when a virtual object is touched in the virtual scene;
or 3.2) in a real rehabilitation training scene, the user performs conventional upper limb training, the finger angle acquisition module monitors hand joint bending signals, the arm positioning acquisition module monitors forearm and forearm spatial motion signals, and the hand pressure acquisition module monitors hand grip strength signals;
4) in the training process, the data transmission module of the near-infrared brain function acquisition module transmits a near-infrared brain blood oxygen signal to the evaluation analysis module, and the data transmission module of the touch force/motion data glove module transmits a hand joint bending signal, a forearm and forearm spatial motion signal and a hand grip force signal to the evaluation analysis module;
5) and the evaluation analysis module performs brain function index calculation, touch force/movement information analysis calculation and near-infrared brain function and touch force/movement information fusion evaluation calculation according to the received signals, and analyzes to obtain evaluation indexes of upper limb movement capacity and brain-upper limb synergistic capacity.
According to an embodiment of the present invention, in step 5), the near-infrared cerebral blood oxygen signal transmitted in step 4) is processed according to the following steps:
performing continuous complex wavelet transform on the near-infrared cerebral blood oxygen signals acquired by each acquisition channel, wherein the frequency range of the wavelet transform is 0.01-0.08Hz, the wavelet transform comes from a neural activity signal frequency band, a wavelet coefficient matrix is obtained, and a frequency domain wavelet coefficient matrix is obtained by averaging in a time domain;
calculating the modulus of the frequency domain wavelet coefficient matrix, and performing integral operation in the range of 0.01-0.08Hz to obtain the wavelet amplitude WA of each channel;
calculating the phase of the frequency domain wavelet coefficient matrix to obtain a frequency domain wavelet phase matrix, and performing WPCO (wavelet phase coherence) calculation on the near-infrared cerebral blood oxygen signals of every two channels to obtain a cerebral function connection index;
calculating the coupling strength CS and the coupling direction CD between each two adjacent channels of the near-infrared cerebral blood oxygen signals according to the obtained frequency domain wavelet phase matrix and based on an effect connection calculation method of Bayesian inference to obtain a cerebral effect connection index;
according to the calculation rule of the lateral deviation index, dividing the difference of the brain function index of a certain hemisphere and the contralateral hemisphere by the sum of the brain function index of a certain hemisphere and the contralateral hemisphere, namely
The laterality coefficients lWA based on the wavelet amplitude WA, the laterality coefficients lwnco based on the wavelet phase coherence WPCO and the laterality coefficients lCS based on the coupling strength CS are calculated.
According to an embodiment of the invention, in step 5), the hand joint bending signal, the forearm and forearm spatial motion signal and the hand grip force signal transmitted in step 4) are processed according to the following steps:
performing first-order and second-order differential operation on the hand joint bending signal, the forearm and large arm spatial motion signal and the hand grip strength signal in a task training time period to obtain signal change speed and acceleration corresponding to the hand joint bending signal, the forearm and large arm spatial motion signal and the hand grip strength signal;
performing interpolation fitting calculation on the hand joint bending signal, the forearm and forearm spatial motion signal and the hand grip strength signal within a task training time period, wherein the interpolation fitting calculation includes but is not limited to least square interpolation fitting calculation and cubic spline difference value fitting calculation, and obtaining a corresponding interpolation fitting signal;
from the hand joint curvature signal, the forearm and forearm spatial motion signal, the hand grip signal and the corresponding interpolated fit signal, according to a smoothness calculation method:
wherein, f (n) is an original signal, F (n) is an interpolation fitting signal, and smoothness of the hand joint bending signal, the forearm and large arm spatial motion signal and the hand grip force signal is calculated to obtain a smoothness index which represents smoothness of the hand bending, forearm and large arm spatial motion and hand grip force change.
According to an embodiment of the invention, in step 5), the evaluation calculation of the fusion of the near-infrared brain function and the touch force/motion information is performed according to the following steps:
within a task training time period, performing normalization calculation on the near-infrared cerebral blood oxygen signals, the hand joint bending signals, the forearm and forearm spatial motion signals and the hand grip strength signals acquired by each acquisition channel, and converting the signals into scalar signals;
resampling and calculating scalar signals corresponding to the hand joint bending signals, the forearm and forearm spatial motion signals and the hand grip strength signals, so that the signal frequency of the signals is consistent with the near-infrared cerebral blood oxygen signals;
calculating a Pearson correlation coefficient of scalar signals of the hand joint bending signals, the forearm and forearm spatial motion signals and the hand grip force signals after resampling and scalar signals of the near-infrared cerebral blood oxygen signals, and representing the correlation degree of touch force/motion information and near-infrared brain functions;
and calculating the coupling strength CSm and the coupling direction CDm between the scalar signals of the hand joint bending signal, the forearm and forearm spatial motion signal and the hand grip force signal after resampling and the scalar signals of the near-infrared cerebral blood oxygen signal based on an effect connection calculation method of Bayesian inference, and representing the causal relationship between touch force/motion information and near-infrared brain function.
The invention also provides a dynamic quantitative evaluation system for fusing near-infrared brain function and touch force/motion information, which comprises:
the near-infrared brain function acquisition module is used for establishing the connection between a user and the near-infrared brain function acquisition equipment, acquiring near-infrared brain blood oxygen signals of corresponding brain areas according to a specific light source probe template, and transmitting the acquired near-infrared brain blood oxygen signals to the evaluation analysis module;
the touch force/motion data glove module is used for establishing connection between a user and the touch force/motion data glove, acquiring hand joint bending signals, forearm and forearm spatial motion signals and hand grip force signals of the user in a virtual reality scene, and hand joint bending signals, forearm and forearm spatial motion signals and hand grip force signals in a real rehabilitation training scene, and transmitting the signals to the evaluation and analysis module;
and the evaluation analysis module is used for carrying out evaluation analysis calculation according to the near-infrared cerebral blood oxygen signals transmitted by the near-infrared brain function acquisition module, the hand joint bending signals transmitted by the touch force/motion data glove module, the forearm and forearm spatial motion signals and the hand grip force signals.
According to an embodiment of the present invention, the near-infrared brain function acquisition module includes:
the light source probe template is used for setting the position of a light source probe for near infrared monitoring and the arrangement of an acquisition channel;
the near-infrared information acquisition module is used for acquiring a near-infrared cerebral blood oxygen signal;
and the brain function data transmission module is used for transmitting the collected near-infrared brain blood oxygen signals to the evaluation analysis module.
According to one embodiment of the present invention, a touch force/motion data glove module comprises:
the hand pressure acquisition module is used for acquiring hand grip strength signals in a real rehabilitation training scene and is composed of pressure sensor arrays distributed on palms and fingers on the inner side of the data glove;
the finger angle acquisition module is used for acquiring hand joint bending signals in a virtual reality scene and a real rehabilitation training scene, wherein the finger angle module of a single data glove is composed of 14 bending sensor arrays which are positioned between 5 metacarpophalangeal joints, 5 near interphalangeal joints and 4 metacarpophalangeal joints on the back outer side of the data glove;
the arm positioning acquisition module is used for acquiring spatial motion signals of the forearms and the big arms in a virtual reality scene and a real rehabilitation training scene, wherein the arm positioning module on one side consists of 3 inertial sensors positioned on the outer side of the backs of the forearms, the outer side of the backs of the big arms and the middle of the waist and abdomen;
the virtual reality scene module is used for reproducing upper limb movement actions in a virtual training scene constructed by the Unity 3D platform according to the hand joint bending signals acquired by the finger angle acquisition module and the forearm and large arm space movement signals acquired by the arm positioning acquisition module to form virtual reality interaction with a user;
the tactile feedback module is used for providing vibrotactile sensation at the real finger end when the virtual finger end contacts an object in a virtual scene in a quasi-training scene constructed by a Unity 3D platform, wherein the tactile feedback module of a single data glove is composed of 5 tactile feedback devices positioned at the 5 finger ends;
the glove data transmission module is used for transmitting hand joint bending signals acquired by the finger angle acquisition module and forearm and large arm spatial motion signals acquired by the arm positioning acquisition module to the virtual reality scene module, and is also used for transmitting hand joint bending signals, the forearm and large arm spatial motion signals and the hand grip force signals to the evaluation analysis module.
The beneficial effects of the invention are:
1) the brain function state in the limb movement training process can be quantitatively evaluated in real time;
2) the dynamic quantitative evaluation of the upper limb motor function and the brain-limb cooperation capability of the brain function and dynamics information fusion can be realized;
3) the near infrared technology equipment and the touch force/motion data glove technology equipment have good wearability and portability, and are convenient for monitoring brain function and dynamics information in real time;
4) visual feedback and tactile feedback in the training process are provided through a virtual reality interaction technical means, the training efficiency and the training quality are improved, and the rehabilitation assessment and the rehabilitation training are combined.
The foregoing summary is provided for the purpose of description only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will be readily apparent by reference to the drawings and following detailed description.
Drawings
In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.
Fig. 1 is a schematic diagram illustrating a dynamic quantitative evaluation system for fusion of near-infrared brain function and touch force/motion information according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a near-infrared light source probe according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a hand pressure acquisition module and a touch force feedback module according to an embodiment of the invention;
fig. 4 is a schematic diagram of a feedback module of the finger angle acquisition module according to the embodiment of the invention.
FIG. 5 is a schematic diagram of the construction and wearing pattern of a touch force/motion data glove module according to an embodiment of the present invention;
FIG. 6 is a flow chart of brain function index calculation according to an embodiment of the present invention;
FIG. 7 is a flow chart of touch force/motion information analysis calculations according to an embodiment of the present invention;
fig. 8 is a flowchart of a near-infrared brain function and touch force/movement information fusion evaluation calculation according to an embodiment of the present invention.
Detailed Description
In the following, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.
The dynamic quantitative evaluation method and system for fusion of near-infrared brain function and touch force/motion information according to the present invention are described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the dynamic quantitative evaluation system for fusion of near-infrared brain function and touch force/motion information of the present invention generally includes three parts, namely, a near-infrared brain function information acquisition module, a touch force/motion data glove module, and an evaluation and analysis module.
The near-infrared brain function information acquisition module is used for establishing connection between a user and the near-infrared brain function acquisition equipment, acquiring near-infrared brain blood oxygen signals of corresponding brain areas according to a specific light source probe template, and transmitting the acquired near-infrared brain blood oxygen signals to the evaluation analysis module.
As shown in fig. 1, the near-infrared brain function acquisition module includes a light source probe template, a near-infrared information acquisition module, and a brain function data transmission module.
The light source probe template is used for setting the position of a light source probe for near infrared monitoring and the arrangement of a collecting channel. As shown in fig. 2, the light source probe templates are covered on the left and right motor areas and the left and right prefrontal cortex detection areas, a near-infrared monitoring channel is formed between each light source and the probe, and the near-infrared cerebral blood oxygen signals corresponding to the cortex areas are monitored.
The near-infrared information acquisition module is used for acquiring near-infrared cerebral blood oxygen signals. The brain function data transmission module is used for transmitting the near-infrared brain blood oxygen signals acquired by the near-infrared information acquisition module to the evaluation analysis module.
The touch force/motion data glove module is used for establishing connection between a user and the touch force/motion data glove, collecting hand joint bending signals, forearm and forearm spatial motion signals and hand grip force signals of the user in a virtual reality scene, and hand joint bending signals, forearm and forearm spatial motion signals and hand grip force signals in a real rehabilitation training scene, and transmitting the signals to the evaluation and analysis module.
As shown in fig. 1, the touch force/motion data glove module includes a hand pressure acquisition module, a finger angle acquisition module, an arm positioning acquisition module, a virtual scene module, a tactile feedback module, and a data transmission module.
The hand pressure acquisition module is used for acquiring a hand grip signal in a real rehabilitation training scene. As shown in fig. 3, the hand pressure acquisition module consists of an array of pressure sensors 301 distributed on the inner palm and fingers of the data glove.
The finger angle acquisition module is used for acquiring hand joint bending signals in a virtual reality scene and a real rehabilitation training scene. As shown in fig. 4, for a single-sided data glove, the finger angle acquisition module is composed of 14 bending sensor arrays including 5 metacarpophalangeal joint bending sensors 401, 5 proximal interphalangeal joint bending sensors 402 and 4 bending sensors 403 between metacarpophalangeal joints.
The arm positioning acquisition module is used for acquiring forearm and forearm spatial motion signals in a virtual reality scene and a real rehabilitation training scene. As shown in fig. 5, for a single-sided data glove, the arm positioning and collecting module is composed of an inertial sensor 501 located on the outer side of the forearm back, an inertial sensor 502 located on the outer side of the upper arm back, and an inertial sensor 503 located in the middle of the waist and abdomen, wherein a three-axis acceleration sensor, a three-axis gyroscope sensor, and a three-axis magnetometer are integrated in the inertial sensor to measure the acceleration, the angular rate, and the attitude and heading angle of the corresponding body part. The inertial sensor 501 and the inertial sensor 502 measure the acceleration, the angular velocity and the attitude heading angle of the forearm and the forearm by taking the inertial sensor 503 as a reference, and represent the spatial track information of the forearm and the forearm by a quaternion method to obtain the spatial motion signals of the forearm and the forearm.
As shown in fig. 5, the virtual reality scene module 504 is configured to reproduce upper limb movement in a virtual reality training scene constructed by the Unity 3D platform according to the hand joint bending signal acquired by the finger angle acquisition module and the forearm and forearm spatial movement signal acquired by the arm positioning acquisition module, so as to form virtual reality interaction with the user.
The tactile feedback module is used for providing vibration tactile sensation at the real finger end when the virtual finger end contacts an object in a virtual scene in a simulated training scene constructed by the Unity 3D platform. As shown in fig. 3, for a single-sided data glove, the haptic feedback module consists of 5 haptic feedback devices 302 located at the ends of 5 fingers.
The glove data transmission module 505 is configured to transmit the hand joint bending signal acquired by the finger angle acquisition module and the forearm and forearm spatial motion signal acquired by the arm positioning acquisition module to the virtual reality scene module 504, and is further configured to transmit the hand joint bending signal and the forearm and forearm spatial motion signal and the hand grip strength signal to the evaluation analysis module.
And the evaluation analysis module is used for carrying out evaluation analysis calculation according to the near-infrared cerebral blood oxygen signals transmitted by the near-infrared brain function acquisition module, the hand joint bending signals, the forearm and forearm spatial motion signals and the hand grip strength signals transmitted by the touch force/motion data glove module.
The dynamic quantitative evaluation method of fusion of near-infrared brain function and touch force/motion information according to the present invention is described below.
The method comprises the following steps:
1) and establishing the connection between the user and the near-infrared brain function acquisition module. As shown in fig. 2, the light source and the probe of the near-infrared brain function detection device are arranged according to the near-infrared brain function detection light source probe template, and the collected brain area includes, but is not limited to, the cortex of the motor area and the cortex of the prefrontal lobe;
2) a user connection to the touch force/motion data glove module is established. As shown in fig. 5, the flexible gloves are worn on both hands, the forearm inertial sensor is worn on the outer side of the back of the forearm, the positions of the forearm inertial sensor and the forearm inertial sensor are in a straight line with the flexible gloves, and the torso inertial sensor is worn on the middle of the waist and abdomen;
3.1) selecting a specific training scene and task in the virtual reality scene module 504, performing interactive upper limb training in the virtual reality scene by a user according to the training scene and the task, monitoring hand joint bending signals by the finger angle acquisition module, monitoring spatial motion signals of a forearm and the forearm by the arm positioning acquisition module, and generating vibration touch at the finger end by the touch feedback module when a virtual object is touched in the virtual reality scene;
or 3.2) in a real rehabilitation training scene, the user performs conventional upper limb training, the finger angle acquisition module monitors hand joint bending signals, the arm positioning acquisition module monitors forearm and forearm spatial motion signals, and the hand pressure acquisition module monitors hand grip strength signals;
4) in the training process, the brain function data transmission module transmits the near-infrared cerebral blood oxygen signals to the evaluation analysis module, and the glove data transmission module 505 transmits hand joint bending signals, forearm and forearm spatial motion signals and hand grip strength signals to the evaluation analysis module;
5) and the evaluation analysis module performs brain function index calculation, touch force/motion information analysis calculation and evaluation calculation of fusion of near-infrared brain function and touch force/motion information according to the near-infrared cerebral blood oxygen signal, the hand joint bending signal, the forearm and upper arm space motion signal and the hand grip force signal, and analyzes to obtain evaluation indexes of upper limb motion capability and brain-upper limb synergistic capability.
In the above step 5), as shown in fig. 6, the brain function index is calculated according to the following steps.
First, brain activation degree calculation and brain function connection strength calculation based on wavelet transform.
And (3) carrying out continuous complex wavelet transformation on the near-infrared cerebral blood oxygen signals acquired by each acquisition channel, wherein the frequency range of the wavelet transformation is 0.01-0.08Hz, and obtaining a wavelet coefficient matrix G (s, t):
wherein t is a time parameter, g (u) is a near-infrared cerebral blood oxygen signal, Ψs,t(u) is a wavelet basis function, the present embodiment uses a Morlet wavelet basis function for continuous wavelet analysis, s is a scale sequence of wavelet transform, where the relationship between the scale s and the frequency f is:
wherein fc is the wavelet transform center frequency, 1Hz is adopted, and the time-frequency resolution requirement can be better met; delta t is a set sampling period, and 0.1s is taken;
averaging the obtained wavelet coefficient matrix G (s, t) on the time domain to obtain a frequency domain wavelet coefficient matrix;
calculating the modulus of the frequency domain wavelet coefficient matrix, and performing integral operation in the range of 0.01-0.08Hz to obtain the wavelet amplitude WA of each channel, wherein the WA is used for representing the brain activation degree;
and calculating the phase of the frequency domain wavelet coefficient matrix to obtain a frequency domain wavelet phase matrix phi (f, t), and calculating the wavelet phase coherence WPCO of the near-infrared cerebral blood oxygen signals of every two channels to obtain a cerebral function connection index. Taking channels x, y as an example, the difference between their phase information is Δ φxy(f, t), converting cos Δ φxy(f, t) and sin Δ φxy(f, t) is averaged in the time domain to obtain<cosΔφxy(f)>And<sinΔφxy(f)>the calculation formula is as follows:
in the formula, L is 1, 2. The wavelet phase coherence WPCO is thus obtained as:
WPCO is used to characterize brain functional junction strength;
second, the effective joint strength calculation based on bayesian inference.
And calculating a coupling coefficient matrix c of a phase coupling model between every two adjacent channels of the near-infrared cerebral blood oxygen signals according to the coupling coefficient matrix c to calculate the coupling strength CS and the coupling direction CD to obtain a cerebral effect connection index. With a wavelet phase matrix phiiAnd phijFor example, from phiiTo phijCoupling strength CS ofi,jDefined as an inferred parameter c derived from phase dynamicskEuclidean norm of:
coupling strength CSi,jShows a vibrator phiiIs opposite to the oscillator phijIs determined by the overall estimation of the amount of influence exerted by the frequency of (c). The coupling direction is defined as:
if CDi,jIf it is positive, CSj,i＞CSi,jThe coupling direction is phij→φi(ii) a Otherwise, CDi,jIf it is negative, then CSj,i＜CSi,jThe coupling direction is phii→φj. The coupling strength CS and the coupling direction CD are obtained through calculation, and the brain effect connection index is obtained.
Third, a laterality index calculation based on the laterality calculation rule.
According to the laterality calculation rule, namely:
dividing the difference of the brain function indexes of a certain hemisphere and the contralateral hemisphere by the sum of the brain function indexes of the certain hemisphere and the contralateral hemisphere, and calculating a lateral deviation coefficient lWA based on the wavelet amplitude WA, a lateral deviation coefficient lWPCO based on the wavelet phase coherence WPCO and a lateral deviation coefficient lCS based on the coupling strength CS.
In step 5), as shown in fig. 7, the touch force/motion information analysis calculation is performed according to the following steps:
performing first-order and second-order differential operation on the hand joint bending signal, the forearm and large arm spatial motion signal and the hand grip strength signal in a task training time period to obtain signal change speed and acceleration corresponding to the hand joint bending signal, the forearm and large arm spatial motion signal and the hand grip strength signal;
performing interpolation fitting calculation on the hand joint bending signal, the forearm and forearm spatial motion signal and the hand grip strength signal within a task training time period, wherein the interpolation fitting calculation includes but is not limited to least square interpolation fitting calculation and cubic spline difference value fitting calculation to obtain a corresponding interpolation fitting signal;
according to the hand joint bending signal, the forearm and big arm spatial motion signal, the hand grip force signal and the corresponding interpolation fitting signal, calculating the smoothness of the hand joint bending signal, the forearm and big arm spatial motion signal and the hand grip force signal according to a smoothness calculation method to obtain a smoothness index, and representing the smoothness of the hand bending, the forearm and big arm spatial motion and the change of the hand grip force.
Smoothness r is calculated by the following equation:
wherein, f (n) is an original hand joint bending signal, forearm and forearm spatial motion signal and hand grip signal, and F (n) is an interpolation fitting signal of the hand joint bending signal, the forearm and forearm spatial motion signal and the hand grip signal.
In step 5), as shown in fig. 8, the evaluation calculation of the fusion of the near-infrared brain function and the touch force/motion information is performed according to the following steps:
within a task training time period, the near-infrared cerebral blood oxygen signals, the hand joint bending signals, the forearm and forearm space motion signals and the hand grip strength signals collected by each collecting channel are normalized and converted into scalar signals f (n)1；
Scalar signal f (n) corresponding to the hand joint bending signal, the forearm and forearm space motion signal and the hand grip strength signal1Performing resampling calculation to make the signal frequency consistent with the near-infrared cerebral blood oxygen signal, and obtaining resampling signal f (n)2；
Resampling signals f (n) for calculating hand joint bending signals, forearm and big arm space motion signals and hand grip strength signals2The Pearson correlation coefficient P of scalar signals of the near-infrared cerebral blood oxygen signals represents the correlation degree of the touch force/motion information and the near-infrared brain functions;
calculating a hand joint bending signal, a hand and forearm space motion signal and a hand grip strength signal after resampling by using an effect connection calculation method based on Bayesian inference f (n)2The coupling strength CSm and the coupling direction CDm between the signal and the near-infrared cerebral blood oxygen signal scalar signal represent the causal relationship between the touch force/motion information and the near-infrared brain function.
It should be noted that the above-mentioned embodiments are only specific embodiments of the present invention, and are not intended to limit the technical scope of the present invention, and the present invention is not limited thereto, and although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that any person skilled in the art can modify or easily conceive of the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features of the present invention, and the modifications, changes or substitutions should not make the essence of the corresponding technical solutions depart from the spirit and scope of the technical solutions of the present invention, and shall be covered by the protective scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. An evaluation method for fusion of near-infrared brain function and touch force/motion information is characterized by comprising the following steps of:
1) arranging a light source and a probe of a near-infrared brain function detection device according to a near-infrared brain function detection light source probe template so as to acquire a near-infrared brain blood oxygen signal;
2) arranging a finger angle acquisition module, an arm positioning acquisition module and a hand pressure acquisition module, wherein the positions of a forearm inertial sensor and a large arm inertial sensor of the arm positioning acquisition module are positioned on the same straight line with the flexible gloves of the finger angle acquisition module;
3) selecting a specific training scene and task in a virtual scene module, monitoring hand joint bending signals by using a finger angle acquisition module, and monitoring forearm and forearm spatial motion signals by using an arm positioning acquisition module;
4) in a real rehabilitation training scene, a finger angle acquisition module is used for monitoring hand joint bending signals, an arm positioning acquisition module is used for monitoring forearm and large arm space motion signals, and a hand pressure acquisition module is used for monitoring hand grip strength signals;
5) transmitting the signals acquired in the steps 1) and 3) or 1) and 4) to an evaluation analysis module;
6) the evaluation analysis module carries out brain function index calculation, touch force/movement information analysis calculation and near-infrared brain function and touch force/movement information fusion evaluation calculation according to the received signals, analyzes and obtains evaluation indexes of upper limb movement ability and brain-upper limb cooperative ability,
performing evaluation calculation of near-infrared brain function and touch force/motion information fusion according to the following steps:
within a task training time period, performing normalization calculation on the near-infrared cerebral blood oxygen signals, the hand joint bending signals, the forearm and forearm spatial motion signals or the hand grip strength signals acquired by each acquisition channel, and converting the signals into scalar signals;
resampling and calculating scalar signals corresponding to the hand joint bending signals, the forearm and forearm spatial motion signals or the hand grip force signals to enable the signal frequency to be consistent with the near-infrared cerebral blood oxygen signals;
calculating a Pearson correlation coefficient of a scalar signal of the resampled signal and the near-infrared cerebral blood oxygen signal, and representing the correlation degree of the touch force/motion information and the near-infrared brain function;
and calculating the coupling strength CSm and the coupling direction CDm between the resampling signal and the scalar signal of the near-infrared cerebral blood oxygen signal based on the Bayesian inference effect connection calculation method, and representing the causal relationship between the touch force/motion information and the near-infrared brain function.
2. The method for fusion of near-infrared brain function and touch force/motion information as claimed in claim 1, wherein, in the virtual reality scenario of step 3), the haptic feedback module can generate vibrotactile sensation at the user's finger tip when touching the virtual object.
3. The method for fusion of near-infrared brain function and touch/motion information assessment according to claim 1, wherein the near-infrared cerebral blood oxygen signal transmitted in step 5) is processed according to the following steps:
according to continuous complex wavelet transform calculation, time domain average calculation, wavelet phase coherence calculation and effect connection calculation based on Bayes inference, obtaining a wavelet amplitude WA, a wavelet phase coherence WPCO, a coupling strength CS and a coupling direction CD, and according to a lateral deviation index calculation rule, dividing the difference of brain function indexes of a certain hemisphere and a contralateral hemisphere by the sum of brain function indexes of the certain hemisphere and the contralateral hemisphere, namely a formula:
an laterality coefficient lWA based on the wavelet amplitude WA, a laterality coefficient lwcpco based on the wavelet phase coherence WPCO, and a laterality coefficient lCS based on the coupling strength CS are calculated.
4. The near-infrared brain function and touch/motion information fusion assessment method according to claim 1, wherein the hand joint bending signal, forearm and forearm spatial motion signal or hand grip force signal transmitted in step 5) is processed according to the following steps:
performing first-order and second-order differential operation on the hand joint bending signal, the forearm and forearm spatial motion signal or the hand grip strength signal in a task training time period to obtain signal change speed and acceleration;
performing interpolation fitting calculation on the hand joint bending signal, the forearm and forearm spatial motion signal or the hand grip strength signal within one task training time period to obtain a corresponding interpolation fitting signal;
according to the smoothness calculation method:
and calculating smoothness r, wherein f (n) is an original signal, and F (n) is an interpolation fitting signal to obtain smoothness indexes which represent the smoothness of the hand bending, the spatial motion of the forearm and the big arm and the change of the hand grip.
5. A near-infrared brain function and touch force/motion information fusion assessment system, comprising:
the near-infrared brain function acquisition module is used for acquiring near-infrared brain blood oxygen signals of corresponding brain areas and transmitting the acquired near-infrared brain blood oxygen signals to the evaluation analysis module;
the touch force/motion data glove module is used for acquiring hand joint bending signals, forearm and forearm spatial motion signals and hand grip force signals of a user in a virtual reality scene, and acquiring hand joint bending signals, forearm and forearm spatial motion signals and hand grip force signals in a real rehabilitation training scene, and transmitting the signals to the evaluation and analysis module;
an evaluation analysis module for performing evaluation analysis calculation according to the near-infrared cerebral blood oxygen signal transmitted by the near-infrared brain function acquisition module and the hand joint bending signal, the forearm and forearm spatial motion signal or the hand grip force signal transmitted by the touch force/motion data glove module, wherein the evaluation analysis module performs brain function index calculation, touch force/motion information analysis calculation and near-infrared brain function and touch force/motion information fusion evaluation calculation according to the received signals, and analyzes to obtain evaluation indexes of upper limb motion capability and brain-upper limb cooperative capability,
performing evaluation calculation of near-infrared brain function and touch force/motion information fusion according to the following steps:
within a task training time period, performing normalization calculation on the near-infrared cerebral blood oxygen signals, the hand joint bending signals, the forearm and forearm spatial motion signals or the hand grip strength signals acquired by each acquisition channel, and converting the signals into scalar signals;
resampling and calculating scalar signals corresponding to the hand joint bending signals, the forearm and forearm spatial motion signals or the hand grip force signals to enable the signal frequency to be consistent with the near-infrared cerebral blood oxygen signals;
calculating a Pearson correlation coefficient of scalar signals of the resampled signals and the near-infrared cerebral blood oxygen signals, and representing the correlation degree of the touch force/motion information and the near-infrared cerebral function;
and calculating the coupling strength CSm and the coupling direction CDm between the resampling signal and the scalar signal of the near-infrared cerebral blood oxygen signal based on the Bayesian inference effect connection calculation method, and representing the causal relationship between the touch force/motion information and the near-infrared brain function.
6. The near-infrared brain function and touch force/motion information fused assessment system according to claim 5, wherein the near-infrared brain function acquisition module comprises:
the light source probe template is used for setting the position of a light source probe for near infrared monitoring;
a near-infrared information acquisition module for acquiring near-infrared cerebral blood oxygen signals, an
And the brain function data transmission module is used for transmitting the near-infrared brain blood oxygen signals acquired by the near-infrared information acquisition module to the evaluation analysis module.
7. The near-infrared brain function and touch force/movement information fused assessment system according to claim 5, wherein the touch force/movement data glove module comprises:
the hand pressure acquisition module is used for acquiring hand grip strength signals in a real rehabilitation training scene and consists of pressure sensor arrays distributed at palms and fingers on the inner side of the data glove;
the finger angle acquisition module is used for acquiring hand joint bending signals in a virtual reality scene and a real rehabilitation training scene;
the arm positioning acquisition module is used for acquiring spatial motion signals of the forearm and the forearm in a virtual reality scene and a real rehabilitation training scene;
the virtual reality scene module is used for reproducing upper limb movement actions in a virtual practical training scene according to the hand joint bending signals acquired by the finger angle acquisition module and the forearm and large arm space movement signals acquired by the arm positioning acquisition module to form virtual reality interaction with a user;
the tactile feedback module is used for providing vibration tactile sensation at the finger end of the user when the virtual finger end contacts an object in the virtual scene in the simulated training scene.
8. The near-infrared brain function and touch force/motion information fused assessment system according to claim 7, wherein the touch force/motion data glove module further comprises a glove data transmission module for transmitting the hand joint bending signal collected by the finger angle collection module and the forearm and forearm spatial motion signal collected by the arm positioning collection module to the virtual reality scene module, and for transmitting the hand joint bending signal, the forearm and forearm spatial motion signal and the hand grip force signal to the assessment analysis module.
9. The near-infrared brain function and touch force/motion information fusion assessment system according to claim 7, wherein the haptic feedback module of the single data glove is comprised of haptic feedback devices located at the tips of the fingers.
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