Hand rehabilitation training device

Abstract

The invention discloses a hand rehabilitation training device, which belongs to the technical field of medical rehabilitation, and comprises a wearable glove base body, a main body structure, a wrist structure, a multi-mode driving module, a nickel-titanium alloy wire driving unit, a pneumatic driving unit, a sensor detection module, a bending sensor, a myoelectricity sensor and a pressure sensor, the multi-mode driving module comprises a sensor detection module and a wrist structure, the electroencephalogram interaction module comprises a non-invasive EEG electrode and a head band, the self-adaptive control module comprises a control box arranged on the wrist structure, and the self-adaptive control module is electrically connected with the multi-mode driving module, the sensor detection module and the electroencephalogram interaction module through wires and communicates with terminal equipment through a built-in wireless module; according to the hand rehabilitation training device with the structure, flexible adaptation, multi-mode driving, electroencephalogram-sensing closed-loop feedback and intelligent regulation and control of hand rehabilitation training are achieved through cooperative work of multiple modules, and training accuracy and individuation are improved.

Description

Technical Field

[0001] This invention relates to the field of medical rehabilitation technology, and in particular to a hand rehabilitation training device. Background Technology

[0002] Hand dysfunction is a common sequela of neurological diseases such as stroke and spinal cord injury, severely impacting patients' ability to live independently in daily life. Clinical practice shows that scientific and standardized rehabilitation training can effectively promote the recovery of hand motor function, with active or passive training using rehabilitation assistive devices being an important means.

[0003] Existing rehabilitation training devices for patients with hand dysfunction have the following shortcomings: Traditional devices mostly use a single drive method (such as motor or pneumatic) to control joint movement, which is difficult to meet the differentiated needs of the metacarpophalangeal joint (MCP), proximal interphalangeal joint (PIP), and distal interphalangeal joint (DIP) in terms of torque, accuracy, and coordination; the sensor system is limited to angle and pressure monitoring, lacking integrated analysis of electromyographic and electroencephalographic signals, and cannot comprehensively assess muscle status and motor intention; the interaction method relies on preset programs and cannot capture the user's active movement intention (such as "clenching a fist" or "grasping") in real time through brain-computer interface; and the devices generally suffer from problems such as scattered drive units, bulky wear, and inconvenient maintenance.

[0004] To address the aforementioned issues, developing a hand rehabilitation training device that integrates multimodal driving, multidimensional sensing, advanced intent recognition, and a unified structure is key to improving rehabilitation outcomes and enhancing the patient experience. Summary of the Invention

[0005] To address the problems mentioned in the background section, the present invention provides a hand rehabilitation training device, comprising:

[0006] A wearable glove base, adapted to the shape of a human hand, includes a main structure for wrapping the palm and a wrist structure extending to the wrist, wherein the main structure and the wrist structure are integrally formed by a flexible material;

[0007] The multimodal drive module includes a nickel-titanium alloy wire drive unit and a pneumatic drive unit. The nickel-titanium alloy wire drive unit is fixed to the inner side of the main structure along the finger joint axis through an insulating sleeve, and its two ends extend into the control box of the wrist structure and are electrically connected to the control box. The independent airbags of the pneumatic drive unit are set at the metacarpophalangeal joints and proximal interphalangeal joints of the main structure. The independent airbags are connected to the air pump assembly of the wrist structure through a micro air tube. The coordinated drive of the nickel-titanium alloy wire drive unit and the pneumatic drive unit adopts a stiffness allocation algorithm based on an impedance control model, and dynamically adjusts the ratio of the contraction force of the nickel-titanium alloy wire to the airbag pressure through the joint angle error.

[0008] The sensor detection module includes a bending sensor, an electromyography (EMG) sensor, and a pressure sensor. The bending sensor is disposed at the knuckle joint of the main structure, the EMG sensor is disposed on the forearm strap of the wrist structure, and the pressure sensor is disposed on the inner side of the main structure.

[0009] The EEG interaction module includes non-invasive EEG electrodes and a headband. The non-invasive EEG electrodes are worn on the motor cortex area of ​​the user's head through the headband and are detachably connected to the control box through wires.

[0010] An adaptive control module includes a control box disposed on the wrist structure. An indicator light is provided on the surface of the control box. The control box includes a signal processing unit, a drive control unit, and a wireless communication unit. The signal processing unit fuses the multi-source signals from the sensor detection module based on a Kalman filter framework, eliminating drift of the bending sensor and noise from the electromyography (EMG) sensor signals through multivariate state estimation. The drive control unit dynamically adjusts the cooperative drive ratio of the nickel-titanium alloy wire drive unit and the pneumatic drive unit using a fuzzy PID control algorithm based on the fused signal and the pattern recognition commands output by the EEG interaction module. The wireless communication unit is used for data interaction and command reception with external terminal devices.

[0011] Preferably, the wrist structure has a receiving cavity for accommodating the air pump assembly and the control box, and the outside of the receiving cavity is provided with a removable protective cover.

[0012] Preferably, the insulating sleeve of the nickel-titanium alloy wire drive unit is fixed to the inner side of the main structure through a fiber layer, and the electrical connection port between the insulating sleeve and the control box is sealed through a waterproof connector.

[0013] Preferably, the independent airbag is fixed to the main structure at a joint position by silicone material, and a pressure regulating valve is provided at the connection between the micro air tube and the air pump assembly.

[0014] Preferably, the bending sensor is arranged along the active axis of the finger joints of the main structure, the electromyography sensor is fixed to the forearm strap with Velcro, the forearm strap is detachably connected to the wrist structure, and the pressure sensor is located at the fingertips and the inner side of the palm of the main structure.

[0015] Preferably, the wire is detachably connected to the control box via a magnetic interface.

[0016] Preferably, the signal processing unit of the non-invasive EEG electrode is configured to perform preprocessing functions such as noise removal and baseline correction through bandpass filtering, extract motion-related frequency band features using wavelet transform, and output corresponding control commands based on the support vector machine algorithm.

[0017] Therefore, the hand rehabilitation training device of the present invention, which adopts the above structure, has the following beneficial effects:

[0018] (1) By working in tandem with the multimodal pneumatic drive, the flexible characteristics of the pneumatic drive can be used to protect the joints, and the rapid response of the nickel-titanium alloy wire can provide precise driving force to meet the training needs of patients with different muscle strength levels.

[0019] (2) Integrating bending sensors, electromyography sensors and EEG interaction modules to achieve multi-dimensional signal fusion of "joint angle - muscle activity - movement intention", improving the device's recognition accuracy and response speed of user action commands, and enhancing the immersive experience of active training.

[0020] (3) The glove base and wrist structure are integrally molded with flexible materials, and the functional components are integrated into the wrist cavity, which reduces the problem of tangled tubing, improves wearing comfort and structural stability, and facilitates daily training.

[0021] The technical solutions in the embodiments of the present invention will now be clearly and completely described in conjunction with the accompanying drawings. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the overall structure of a hand rehabilitation training device according to the present invention;

[0023] Figure 2 This is a schematic diagram of the module composition of a hand rehabilitation training device according to the present invention;

[0024] Reference numerals: 1. Main structure; 2. Wrist structure; 3. Independent airbag; 4. Miniature trachea; 5. Receptacle; 6. Protective cover; 7. Non-invasive EEG electrode. Detailed Implementation

[0025] The following detailed description of embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0026] Example

[0027] like Figure 1 and Figure 2 As shown, a wearable glove base for hand rehabilitation training is adapted to the shape of a human hand. It includes a wearable glove base, a multimodal driving module, a sensor detection module, an EEG interaction module, and an adaptive control module. The modules work together to complete hand rehabilitation training.

[0028] The wearable glove base, which serves as the physical support for each functional module, includes a main structure 1 for wrapping the palm and a wrist structure 2 extending to the wrist. The main structure 1 and the wrist structure 2 are integrally molded from a flexible material. The wrist structure 2 has a receiving cavity 5 for accommodating the air pump assembly and the control box, and a removable protective cover 6 is provided on the outside of the receiving cavity 5.

[0029] A multimodal drive module, used to output driving force for rehabilitation training to the finger joints, includes a nickel-titanium alloy wire drive unit and a pneumatic drive unit. The nickel-titanium alloy wire provides active contraction force, and the pneumatic unit provides flexible pressure. The nickel-titanium alloy wire drive unit is fixed to the inner side of the main structure 1 along the finger joint axis through an insulating sleeve. The insulating sleeve of the nickel-titanium alloy wire drive unit is fixedly connected to the inner fiber layer of the main structure 1. Both ends of the nickel-titanium alloy wire extend into the control box and are electrically connected to the control box. The independent airbags 3 of the pneumatic drive unit are set at the metacarpophalangeal joint and proximal interphalangeal joint of the main structure 1. The independent airbags 3 are connected to the air pump assembly of the wrist structure 2 through a micro air tube 4. The micro air tube 4 is connected to the air pump assembly, and the air pump assembly is connected to the control box through a pipeline. The coordinated drive of the nickel-titanium alloy wire drive unit and the pneumatic drive unit adopts a stiffness allocation algorithm based on an impedance control model, and dynamically adjusts the ratio of the contraction force of the nickel-titanium alloy wire and the airbag pressure through the joint angle error.

[0030] The sensor detection module is used to collect data on finger bending angle, forearm electromyography (EMG) signals, and hand contact pressure in real time. It includes a bending sensor, an EMG sensor, and a pressure sensor. The bending sensor is located at the finger joint connection of the main structure 1 to detect finger bending and is arranged along the movement axis of the finger joints of the main structure 1. The EMG sensor is fixed to the forearm strap of the wrist structure 2 by Velcro. The forearm strap is detachably connected to the wrist structure 2. The pressure sensor is located on the main structure 1 to detect contact pressure, specifically at the fingertips and the inner side of the palm of the main structure 1.

[0031] The EEG interaction module is used to collect EEG signals from the user's motor cortex and establish a brain-computer command transmission link. It includes a non-invasive EEG electrode 7 and a headband. The non-invasive EEG electrode 7 is worn on the user's motor cortex area through the headband and is connected to the control box of the wrist structure 2 through wires. One end of the wire of the EEG interaction module is detachably connected to the control box. The signal processing unit of the non-invasive EEG electrode 7 is configured to perform preprocessing functions such as noise removal and baseline correction through bandpass filtering, extract motor imagery-related frequency band features using wavelet transform, and output corresponding control commands based on the support vector machine algorithm.

[0032] An adaptive control module is used to receive and process multi-sensor data and EEG signals, intelligently regulate the multimodal drive module, and realize communication and interaction with the terminal. It includes a control box set in the wrist structure 2, which is electrically connected to the multimodal drive module, sensor detection module, and EEG interaction module through wires, and communicates with the terminal device through a built-in wireless module. The control box has indicator lights on its surface. The control box includes a signal processing unit, a drive control unit, and a wireless communication unit. The signal processing unit performs fusion processing on the multi-source signals of the sensor detection module based on the Kalman filter framework, and eliminates the drift of the bending sensor and the noise of the EMG sensor signal through multivariate state estimation. The drive control unit dynamically adjusts the cooperative drive ratio of the nickel-titanium alloy wire drive unit and the pneumatic drive unit through a fuzzy PID control algorithm according to the fused signal and the pattern recognition command output by the EEG interaction module. The wireless communication unit is used for data interaction and command reception with the external terminal device.

[0033] Working principle: The main structure 1 of the wearable glove base wraps around the palm, and the wrist structure 2 extends to the wrist. The non-invasive EEG electrodes 7 of the brain-electroencephalogram (EEG) interaction module collect electrical signals from the head motor cortex through the headband and transmit them to the wrist control box via wires. The bending sensor (at the knuckle joint), electromyography (EMG) sensor (forearm strap), and pressure sensor (main structure 1) of the sensor detection module simultaneously collect data on knuckle angle, EMG, and hand pressure and input them to the control box. The control box acts as an adaptive center, interpreting the EEG intent and sensor feedback, and, in conjunction with the training scheme wirelessly transmitted from the terminal, regulates the multimodal drive module: nickel-titanium alloy wire drive unit. The shape memory effect of the contraction is triggered by electric current (the insulating sleeve is fixed to the inside of the main structure 1 along the knuckle axis, and the two ends are connected to the control box). The pneumatic drive unit regulates the air pump assembly through the control box, and inflates and deflates the independent airbags 3 at the corresponding positions of the finger joints in the main structure 1 through the micro air tube 4. The two work together to drive the flexion and extension of the fingers. The control box records training data based on the built-in storage unit, and the indicator light shows the working status. At the same time, it interacts with the terminal through the wireless module, and finally realizes the hand rehabilitation training of "brain electroencephalogram / myomyogram intention perception - multimodal drive execution - multidimensional sensor feedback - intelligent closed-loop regulation", which takes into account the needs of active nerve activation and passive joint movement.

[0034] Therefore, the present invention provides a hand rehabilitation training device with the above-mentioned structure, which achieves flexible adaptation, multimodal driving, EEG-sensor closed-loop feedback and intelligent regulation of hand rehabilitation training through the coordinated work of wearable glove base, multimodal driving, multidimensional sensor detection, EEG interaction and adaptive control module, thereby improving the accuracy and personalization of training.

[0035] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A hand rehabilitation training device, characterized in that, include: A wearable glove base, adapted to the shape of a human hand, includes a main structure for wrapping the palm and a wrist structure extending to the wrist, wherein the main structure and the wrist structure are integrally formed by a flexible material; The multimodal drive module includes a nickel-titanium alloy wire drive unit and a pneumatic drive unit. The nickel-titanium alloy wire drive unit is fixed to the inner side of the main structure along the finger joint axis through an insulating sleeve, and its two ends extend into the control box of the wrist structure and are electrically connected to the control box. The independent airbags of the pneumatic drive unit are set at the metacarpophalangeal joints and proximal interphalangeal joints of the main structure. The independent airbags are connected to the air pump assembly of the wrist structure through a micro air tube. The coordinated drive of the nickel-titanium alloy wire drive unit and the pneumatic drive unit adopts a stiffness allocation algorithm based on an impedance control model, and dynamically adjusts the ratio of the contraction force of the nickel-titanium alloy wire to the airbag pressure through the joint angle error. The sensor detection module includes a bending sensor, an electromyography (EMG) sensor, and a pressure sensor. The bending sensor is disposed at the knuckle joint of the main structure, the EMG sensor is disposed on the forearm strap of the wrist structure, and the pressure sensor is disposed on the inner side of the main structure. The EEG interaction module includes non-invasive EEG electrodes and a headband. The non-invasive EEG electrodes are worn on the motor cortex area of ​​the user's head through the headband and are detachably connected to the control box through wires. An adaptive control module includes a control box disposed on the wrist structure. An indicator light is provided on the surface of the control box. The control box includes a signal processing unit, a drive control unit, and a wireless communication unit. The signal processing unit fuses the multi-source signals from the sensor detection module based on a Kalman filter framework, eliminating drift of the bending sensor and noise from the electromyography (EMG) sensor signals through multivariate state estimation. The drive control unit dynamically adjusts the cooperative drive ratio of the nickel-titanium alloy wire drive unit and the pneumatic drive unit using a fuzzy PID control algorithm based on the fused signal and the pattern recognition commands output by the EEG interaction module. The wireless communication unit is used for data interaction and command reception with external terminal devices.

2. The hand rehabilitation training device according to claim 1, characterized in that: The wrist structure has a cavity for accommodating the air pump assembly and the control box, and the outer side of the cavity has a removable protective cover.

3. The hand rehabilitation training device according to claim 1, characterized in that: The insulating sleeve of the nickel-titanium alloy wire drive unit is fixed to the inside of the main structure through a fiber layer, and the electrical connection port between the insulating sleeve and the control box is sealed through a waterproof connector.

4. The hand rehabilitation training device according to claim 1, characterized in that: The independent airbag is fixed to the joint position of the main structure by silicone material, and a pressure regulating valve is provided at the connection between the micro air tube and the air pump assembly.

5. A hand rehabilitation training device according to claim 1, characterized in that: The bending sensor is arranged along the active axis of the finger joints of the main structure. The electromyography sensor is fixed to the forearm strap with Velcro. The forearm strap is detachably connected to the wrist structure. The pressure sensor is located at the fingertips and the inner side of the palm of the main structure.

6. The hand rehabilitation training device according to claim 1, characterized in that: The wire is detachably connected to the control box via a magnetic interface.

7. The hand rehabilitation training device according to claim 1, characterized in that: The signal processing unit of the non-invasive EEG electrode is configured to perform preprocessing functions such as noise removal and baseline correction through bandpass filtering, extract motion-related frequency band features using wavelet transform, and output corresponding control commands based on the support vector machine algorithm.

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