System for detecting exercise intention of user and controlling functional electrical stimulation (FES) according to same

Abstract

The present application relates to a rehabilitation system based on brain-computer interface (BCI) and functional electrical stimulation (FES) technology. Specifically, the present application relates to a system for detecting a user's exercise intention and promoting recovery of lower extremity functions through electrical stimulation according to same, and relates to a rehabilitation and assistance system for impaired persons suffering from paralysis or complete paralysis of the lower extremities. According to the present application, neural signals due to motor imagery of a user are analyzed in real time, and electrical stimulation is applied to a specific muscle group on the basis thereof to improve motor function.

Description

A system that detects the user's movement intention and controls functional electrical stimulation (FES) accordingly.

[0001] This application relates to a rehabilitation system based on brain-computer interface (BCI) and functional electrical stimulation (FES) technology. Specifically, this application relates to a system that detects a user's movement intention and applies electrical stimulation accordingly to restore lower extremity function. This system relates to a rehabilitation and assistance system for individuals with complete or partial lower extremity paralysis. This system analyzes neural signals generated by the user's movement imagination in real time and, based on these signals, applies electrical stimulation to specific muscle groups to improve motor function.

[0002] Cross-reference to related applications

[0003] This application claims priority to Republic of Korea Patent Application No. 10-2024-0121490, filed September 6, 2024, the entire contents of which are incorporated herein by reference.

[0004] [Description of Nationally Supported Research and Development]

[0005] This study was conducted with the support of the Future Promising Convergence Technology Pioneer Project (Project Name: Development of Non-invasive BCI-FES Based Digital Spinal Tract Source Technology to Enhance Motor Function in Spinal Cord Injuries with Complete Lower Extremity Paralysis: Demonstration of Voluntary Standing and First Steps from Complete Lower Extremity Paralysis to Incomplete Paralysis, Project Identification Number: 2710006358) funded by the Ministry of Science and ICT and hosted by the Korea Institute of Science and Technology.

[0006] Worldwide, patients with complete or partial paralysis of the lower extremities face significant limitations in their mobility and independence in daily life. Functional electrical stimulation (FES) technology has been developed to address these needs, activating muscles through electrical stimulation to induce movement in paralyzed limbs. While existing FES techniques can induce limited movement by electrically stimulating specific muscle groups, they often fail to reflect the user's voluntary intentions and rely on preset stimulation patterns.

[0007] Meanwhile, brain-computer interface (BCI) technology, which analyzes a user's brainwaves to identify their intentions and transmits these to external devices, has been the subject of extensive research recently. In particular, rehabilitation training systems utilizing BCIs provide users with neural signal-based control methods, enabling a more active and personalized approach compared to conventional passive rehabilitation methods.

[0008] However, existing BCI-based rehabilitation systems primarily rely on visual feedback, limiting their ability to provide adequate feedback on whether the user is properly executing their motor imagery. Furthermore, technologies for accurately detecting a user's motor intentions and providing real-time electrical stimulation based on this information are still in their infancy. In particular, for patients with complete lower extremity paralysis, effective rehabilitation is hampered by a lack of FES control technology to translate imagined motor movements into actual movements.

[0009] Accordingly, the present application was conceived to address the aforementioned issues, and aims to provide an FES control system that detects a user's exercise intentions in real time and, based on this, improves lower extremity function. This application aims to integrate BCI and FES technologies to more intuitively understand the user's imagined exercise movements, thereby supporting effective lower extremity rehabilitation.

[0010] By collecting and analyzing the user's brainwave signals in real time, the system accurately detects the user's intentions based on their imagined movement and provides visual and tactile feedback based on the detected intentions, enabling the user to effectively perform the imagined movements. By providing optimized electrical stimulation to specific muscle groups based on the user's intentions, the system maximizes the rehabilitation effect for patients with complete lower extremity paralysis. To provide personalized rehabilitation, a system is developed that learns the user's neural signal patterns and adjusts stimulation parameters in real time based on these findings. By providing the user with continuous and repetitive rehabilitation training, the goal is to achieve long-term recovery of lower extremity function.

[0011] According to one embodiment of the present application, a system for detecting a user's movement intention and controlling functional electrical stimulation (FES) based thereon comprises: a task generation unit for providing a movement imagery task to a user, generating and managing the task, and evaluating the accuracy of the imagined movement; an brainwave detection sensor unit for collecting brainwave signals generated during the user's movement imagery in real time; an intention recognition detection unit for analyzing brainwave signals collected from the brainwave detection sensor unit to detect the user's movement intention; a visual feedback unit for providing visual feedback of changes in brainwaves on a display based on movement intention information detected from the intention recognition detection unit, thereby allowing the user to visually confirm whether or not the user is performing movement imagery correctly; a tactile biofeedback unit for providing tactile stimulation according to movement imagery based on movement intention information transmitted from the intention recognition detection unit, thereby assisting the user to repeatedly perform movement imagery; an FES control unit for analyzing movement intention by linking the results of the intention recognition detection unit with a database, and generating a command to stimulate a specific muscle group based on the analyzed results; It may include a device control unit that executes a stimulation command transmitted from the above FES control unit to transmit electrical stimulation to a specific muscle group and comprehensively controls a task generation unit, an brain wave detection sensor unit, an intention recognition detection unit, a visual feedback unit, a tactile biofeedback unit, and an FES control unit.

[0012] In one embodiment, the task generation unit generates a motor imagery task according to the user's rehabilitation goal, sets conditions including at least one of a target movement, a performance time, and a number of repetitions for each motor imagery task, and supports task performance by linking with a visual feedback unit and a tactile biofeedback unit to provide optimal feedback to the user according to the set conditions, and records and analyzes the user's task performance results to automatically adjust the difficulty of the motor imagery task in the future or generate a new task.

[0013] In one embodiment, the brain wave detection sensor unit collects brain wave signals in real time through electrodes attached to the user's scalp, removes noise from the collected brain wave signals, performs filtering and amplification processes to increase the accuracy of the signals, and is designed to detect and analyze brain wave changes in a specific frequency band that occur according to the user's imagined movement. The brain wave detection sensor unit may include electrodes that are non-invasive for consistent signal collection in different environments and that can be adjusted according to the user's movement or skin condition.

[0014] In one embodiment, the brain wave detection sensor unit is configured in the form of an EEG cap that can be worn on the head by a user, and can be configured to adhere to the user's scalp so that electrodes are stably attached, thereby enabling brain wave signals to be accurately collected in real time.

[0015] In one embodiment, the system may further include a communication unit that communicates to transmit brain wave signals collected from the brain wave detection sensor unit to the task generation unit and the intention recognition detection unit.

[0016] In one embodiment, the visual feedback unit provides the user's exercise intention as a visual cue, and the visual cue may be configured in the form of a graphic, image, or animation displayed on a display in real time during the exercise imagination process so that the user can effectively imagine the desired exercise movement.

[0017] In one embodiment, the visual feedback unit analyzes changes in brain wave signals in real time according to the user's movement intention, and displays a gauge bar on the display based on the analyzed result, and the gauge bar provides visual feedback in the form of increasing or decreasing in proportion to the intensity or accuracy of the movement imagined by the user, and when the user successfully imagines the target movement, the gauge bar is guided to reach a specific threshold, thereby allowing the user to visually confirm and recall the specific movement intention.

[0018] In one embodiment, the tactile biofeedback unit may provide a tactile stimulus including vibration to the user's skin based on the result of the intention recognition detection unit, thereby assisting the user to perform repetitive and consistent motor imagery.

[0019] In one embodiment, the intention recognition detection unit receives brain wave signals collected from the brain wave detection sensor unit in real time, analyzes neural signal patterns related to specific exercise movements imagined by the user, extracts features of the brain wave signals in time domains and frequency domains, utilizes a machine learning algorithm or a deep learning model to detect the user's exercise intention, learns neural signal patterns that appear differently for each user, thereby enabling personalized exercise intention detection, and transmits data generated according to the detected exercise intention to a visual feedback unit, a tactile biofeedback unit, and a device control unit, so that the system can provide feedback and stimulation according to the user's exercise imagination results.

[0020] In one embodiment, the FES control unit generates a control signal for applying electrical stimulation to a specific muscle group according to a movement intention signal received from the intention recognition detection unit, and the control signal includes parameters of the electrical stimulation including pulse width, frequency, amplitude, and duty cycle, and is set to ensure effective stimulation of the corresponding muscle group, and the stimulation parameters can be adjusted by referring to the motion data of a normal person stored in a database or the physical characteristics of the individual user in order to provide customized stimulation for each user, and after the stimulation is completed, the response of the muscle is monitored based on electromyography (EMG) signals and force sensor data, fatigue and stimulation effect can be evaluated, and the stimulation parameters can be adjusted in real time to maintain an optimal stimulation effect.

[0021] In one embodiment, the FES control unit monitors muscle fatigue and activity through FES stimulation, and analyzes the median frequency of electromyography (EMG) signals and force sensor data to adjust stimulation parameters, thereby optimizing muscle stimulation according to fatigue.

[0022] In one embodiment, the database stores a muscle activation pattern set based on motion data of a normal person, and the FES control unit can stimulate a muscle by adjusting at least one of pulse width, frequency, amplitude, and duty cycle according to the pattern.

[0023] In one embodiment, the FES control unit transmits a stimulation signal to the device control unit to sequentially stimulate muscle groups required for movements including at least one of standing, walking, and sitting, and the signal may include stimulation parameters optimized based on movement data of a normal person stored in a database.

[0024] In one embodiment, during the standing movement, in the step of rising from a chair, a muscle group including at least one of the Rectus Abdominis, the Internal Oblique, the External Oblique, the Iliopsoas, the Rectus Femoris, the Hamstrings, and the Tibialis Anterior may be stimulated, in the step of raising the trunk, a muscle group including at least one of the Quadriceps, the Gastrocnemius, and the Soleus may be stimulated, and in the step of completely raising the body, a muscle group including at least one of the Gluteus Maximus, the Erector Spinae, the Quadriceps, the Gastrocnemius, and the Hamstrings may be stimulated.

[0025] According to one embodiment of the present application, a system for detecting a user's exercise intention and controlling functional electrical stimulation (FES) based on the user's intention detects and analyzes the user's exercise intention in real time, thereby providing customized electrical stimulation tailored to the individual's neural signal patterns. This enables the implementation of an optimal rehabilitation program tailored to each user's physical characteristics and rehabilitation goals.

[0026] By providing real-time feedback (visual and tactile) based on the user's imagined movement, more intuitive and effective rehabilitation training is possible. This allows users to clearly recognize whether their imagined movement is being performed correctly, accelerating the recovery of lower extremity function.

[0027] This application effectively stimulates the muscles necessary for lower extremity function recovery by providing optimized electrical stimulation to specific muscle groups. By monitoring the user's muscle fatigue and activity in real time and adjusting stimulation parameters, it minimizes muscle fatigue while maintaining maximum stimulation effects.

[0028] This application utilizes a non-invasive brainwave sensor to accurately detect exercise intentions while minimizing user discomfort. Furthermore, it is designed to be easily worn by users, enabling consistent rehabilitation training in a variety of environments.

[0029] By providing repetitive exercise visualization and feedback, users can increase their persistence in rehabilitation training. Specifically, setting clear goals through visual feedback and providing rewards through tactile feedback can enhance user motivation, leading to long-term rehabilitation benefits.

[0030] The effects of the present application are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description of the claims.

[0031] FIG. 1 is an overview showing the limitations of existing lower limb rehabilitation systems and the necessity of a system for detecting a user's movement intention and controlling functional electrical stimulation (FES) accordingly according to one embodiment of the present application to solve the limitations.

[0032] FIG. 2 is a block diagram of a system for detecting a user's movement intention and controlling functional electrical stimulation (FES) accordingly according to one embodiment.

[0033] FIG. 3 is a system configuration diagram showing a process of detecting a user's movement intention and activating a leg function based on the intention using a brain-computer interface (BCI) and a functional electrical stimulation (FES) system according to one embodiment.

[0034] FIG. 4 is a system configuration diagram showing a rehabilitation training process through voluntary force control and corresponding sensory feedback and virtual reality (VR) feedback using a brain-computer interface (BCI) and a functional electrical stimulation (FES) system according to one embodiment.

[0035] FIG. 5 is a system configuration diagram showing a process in which a patient with complete lower extremity paralysis voluntarily performs a standing movement using a brain-computer interface (BCI) and a functional electrical stimulation (FES) system according to one embodiment.

[0036] The terms used in this specification have been selected from widely used and commonly accepted terms, taking functionality into consideration. However, these terms may vary depending on the intentions or practices of those skilled in the art, the emergence of new technologies, etc. Furthermore, in certain cases, the applicant may arbitrarily select terms, in which case their meanings will be described in the relevant explanatory section of the specification. Therefore, it should be noted that the terms used in this specification should be interpreted based on their substantive meaning and the overall content of the specification, rather than simply their names.

[0037] Additionally, the embodiments described herein may have aspects that are entirely hardware, partially hardware and partially software, or entirely software. As used herein, "unit," "device," or "system" refer to hardware, a combination of hardware and software, or a computer-related entity such as software. For example, a unit, device, or system may refer to hardware that constitutes part or all of a platform and / or software such as an application for operating the hardware.

[0038] The embodiments are described in detail with reference to the attached drawings and the contents described in the attached drawings, but the scope of the claims is not limited or restricted by the embodiments.

[0039] FIG. 1 is an overview showing the limitations of existing lower limb rehabilitation systems and the necessity of a system for detecting a user's movement intention and controlling functional electrical stimulation (FES) accordingly according to one embodiment of the present application to solve the limitations.

[0040] Referring to Figure 1, the image on the right illustrates various assistive technologies for patients with complete paraplegia. The image on the left depicts a patient with complete paraplegia, who voluntarily imagines lower extremity movements but is unable to translate these intentions into physical movements. The patient is seated in a wheelchair and attempts to stand or move through motor imagery, but due to nerve damage, these imaginations are unable to be realized. While various rehabilitation devices and assistive devices are available for patients with complete paraplegia, these devices fall short of fully realizing the patient's voluntary motor intentions.

[0041] In other words, the need for technology to replace damaged spinal nerves has emerged. Existing nerve regeneration and restoration methods are inadequate or nonexistent, and efforts are needed to address these issues through noninvasive techniques such as D-Spinal Tract. To address this, the present application utilizes a technology that integrates a brain-computer interface (BCI) and functional electrical stimulation (FES) to replace damaged neural pathways and restore lower extremity function based on the user's voluntary motor intention.

[0042] FIG. 2 is a block diagram of a system for detecting a user's movement intention and controlling functional electrical stimulation (FES) accordingly (hereinafter, "movement intention detection and FES control system") according to one embodiment.

[0043] Referring to FIG. 2, the movement intention detection and FES control system may include a task generation unit (20); a brain wave detection sensor unit (21); a communication unit (22); an intention recognition detection unit (23); a visual feedback unit (24); a tactile biofeedback unit (25); an FES control unit (26); and a device control unit (27).

[0044] The task generation unit (20) provides the user with a motor imagery task, creates and manages the task, and can evaluate the accuracy of the imagined movement. The task generation unit (20) generates a motor imagery task for the user to perform. This task is designed according to the user's rehabilitation goals and defines specific movements that the user must imagine. In addition, conditions such as target movements, performance time, and number of repetitions can be set for the generated motor imagery task. These conditions can be adjusted according to the user's current status and rehabilitation goals. The task generation unit (20) works in conjunction with the visual feedback unit (24) and the tactile biofeedback unit (25) to provide appropriate feedback while the user performs the task. This allows for real-time confirmation of whether the user's imagined movement is being performed correctly. The task generation unit (20) supports the user in effectively performing the task. For example, it can provide appropriate feedback to the user during task performance to enhance movement imagery or adjust the difficulty of the task. The task generation unit (20) records and analyzes the results of the task performed by the user. Based on this, the difficulty of the next task is automatically adjusted or a new task is created to maximize the user's rehabilitation effect.

[0045] The brainwave detection sensor unit (21) collects brainwave signals in real time through electrodes attached to the user's scalp. These signals reflect neural activity occurring when the user imagines a specific exercise and serve as a core data source for exercise intention detection and the FES control system. The collected brainwave signals undergo filtering and amplification processes to remove noise and increase signal accuracy. This signal processing process enables the acquisition of accurate brainwave data related to the user's exercise intention. The brainwave detection sensor unit (21) is designed to detect brainwave changes in a specific frequency band that occur according to the user's imagined exercise movements. For example, it detects specific frequency bands, such as alpha waves and beta waves, that occur when imagining a specific exercise and analyzes their correlation with exercise intention. This sensor is non-invasively attached to the user's scalp and is designed to be worn for long periods of time without discomfort. In one embodiment, the brainwave detection sensor unit (21) may be configured as an EEG cap that can be worn on the user's head. Furthermore, the position and pressure of the electrodes can be adjusted based on the user's movements or skin condition, enabling consistent signal collection. The brainwave detection sensor unit (21) is designed to collect signals stably in various environments, thereby enabling reliable brainwave data to be obtained even when the user moves or there is a change in the external environment.

[0046] The communication unit (22) transmits data between various components within the system (e.g., brainwave detection sensor unit (21), intention recognition detection unit (23), FES control unit (26), etc.). For example, tasks such as transmitting signal data collected from the brainwave detection sensor unit (21) to the intention recognition detection unit (23) or transmitting stimulation commands generated from the FES control unit (26) to the device control unit (27) are included. Since the movement intention detection and FES control system must operate in real time, the communication unit (22) must ensure rapid and stable data transmission. This allows the movement intention imagined by the user to be immediately analyzed and the corresponding electrical stimulation to be applied at an accurate time. The communication unit (22) can support both wired and wireless communication methods. When wireless communication is used, the user's mobility and convenience can be increased, and wired communication can enhance the stability of data transmission. In one embodiment, an appropriate communication method can be selected depending on the usage environment of the system. The communication unit (22) enables the system to connect to external devices (e.g., computers, smart devices, cloud servers, etc.). This allows users to monitor data in real time or analyze and store rehabilitation training results. Furthermore, remote monitoring and control are enabled through connection to external devices. The communication unit (22) ensures that all components within the system operate in precise synchronization. For example, once analysis of the user's exercise intention based on brainwave data is completed, the data is immediately transmitted to the FES control unit (26), enabling timely stimulation.

[0047] The intention recognition detection unit (23) analyzes the brain wave signal transmitted from the brain wave detection sensor unit (21) in real time. This signal reflects the electrical activity of the brain caused by the exercise movement imagined by the user, and the intention recognition detection unit (23) extracts meaningful patterns from these signals to detect the exercise intention. The intention recognition detection unit (23) extracts features of the brain wave signal in the time domain and the frequency domain. For example, it analyzes signal changes in a specific frequency band or signal amplitude changes over time to identify neural signal patterns related to the movement imagined by the user. The intention recognition detection unit (23) analyzes the brain wave signal using a machine learning algorithm or a deep learning model. This algorithm learns neural signal patterns that appear differently for each user and enables personalized exercise intention detection based on this. This approach continuously learns the user's neural signal patterns and can be gradually adjusted to increase accuracy. The intention recognition detection unit (23) detects the intention of a specific exercise movement imagined by the user based on the analyzed brain wave signals. For example, when a user imagines a movement of raising a hand, a brain wave pattern related to this is detected, and the intention recognition detection unit (23) recognizes the intention of the movement based on this. The detected movement intention is transmitted to the visual feedback unit (24) and the tactile biofeedback unit (25) to provide feedback on whether the movement imagined by the user is being performed properly. In addition, this information is transmitted to the FES control unit (26) and used to generate the necessary electrical stimulation for the corresponding muscle group. The intention recognition detection unit (23) learns each user's individual neural signal pattern and detects the movement intention more accurately based on this. This is important for increasing the effectiveness of rehabilitation training and providing the user with a customized rehabilitation experience.

[0048] The visual feedback unit (24) analyzes changes in brain wave signals in real time according to the user's imagined exercise intention and visually displays the results. This feedback plays an important role in confirming whether the user is correctly performing the exercise imagination. The visual feedback unit (24) provides various visual cues (e.g., graphics, images, animations) so that the user can effectively imagine the target exercise movement. These cues specifically remind the user of the imagined movement and enable the user to adjust the imagined movement according to the visual instructions. The visual feedback unit (24) can display a gauge bar on the display that increases or decreases in proportion to the intensity or accuracy of the user's imagined movement. This gauge bar guides the user to reach a specific threshold when the user successfully imagines the target movement, thereby allowing the user to visually confirm and adjust the specific exercise intention. The visual feedback unit (24) can operate in synchronization with other feedback units (e.g., a tactile biofeedback unit (25). For example, when a user correctly performs motor imagery, tactile feedback can be provided together with visual feedback to maximize the feedback effect. Visual feedback plays an important role in motivating the user to continuously perform rehabilitation training. Visual feedback allows the user to visually confirm his or her progress and gives him or her a sense of satisfaction and accomplishment for achieving the goal. The visual feedback unit can adjust the form and intensity of the feedback according to the user's individual neural signal pattern and rehabilitation goal. This contributes to providing optimal feedback according to the user's current condition and rehabilitation stage. The visual feedback unit (24) allows the user to visually monitor his or her rehabilitation process. This allows the user to check in real time how his or her motor imagery performance is progressing, and to correct or improve the imagined movement if necessary.

[0049] The tactile biofeedback unit (25) provides vibration, pressure, or other forms of tactile stimulation to the skin according to the user's exercise intention, allowing the user to confirm whether their imagination is being carried out correctly. This tactile stimulation plays a crucial role in enhancing the accuracy of motor imagery. By providing positive feedback through tactile stimulation whenever the user performs a correct motor imagery, it helps the user engage in repetitive and consistent motor imagery. This encourages the user to continuously repeat the imagined movement, strengthening the connection between the brain and muscles and maximizing the rehabilitation effect. The tactile biofeedback unit (25) can operate in synchronization with other feedback units, such as the visual feedback unit (24). For example, if the user accurately performs the motor imagery, visual and tactile feedback are provided simultaneously, allowing the user to more clearly recognize and adjust the target movement. The tactile biofeedback unit (25) can adjust the form, intensity, and frequency of the tactile stimulation according to each user's different neural responses and rehabilitation goals. This enables personalized rehabilitation training and provides optimized feedback to each user. During long-term rehabilitation training, the tactile biofeedback unit (25) provides continuous feedback to help users steadily progress toward their rehabilitation goals. Continuous tactile stimulation reinforces the user's exercise intentions and, through repeated training, contributes to the recovery of motor function. Tactile stimulation is a highly intuitive form of feedback, allowing the user to feel it immediately and respond quickly. This is particularly useful in situations where visual feedback is insufficient or the user has difficulty processing visual information. The tactile biofeedback unit (25) can provide various forms of stimulation. For example, various tactile stimuli, such as light vibration, gradual pressure, and microcurrents across the skin surface, stimulate the user's senses and induce accurate motor imagery.

[0050] The FES control unit (26) generates an electrical stimulation signal to stimulate a specific muscle group based on the movement intention signal received from the intention recognition detection unit (23). This signal is used to convert the user's imagined movement into actual muscle movement. The FES control unit (26) can precisely control the intensity and pattern of stimulation applied to the muscles by adjusting the parameters of the stimulation signal (e.g., pulse width, frequency, amplitude, duty cycle, etc.). This is important for providing optimal stimulation according to each user's physical characteristics and exercise goals. The FES control unit (26) can adjust the stimulation parameters based on the physical characteristics of each user or by referencing the movement data of a normal person by linking with a stored database. This data maximizes the effect of the stimulation and enables personalized rehabilitation. The FES control unit (26) monitors electromyography (EMG) signals or force sensor data in real time to evaluate the muscle response. Through this, it analyzes muscle fatigue or the stimulation effect and adjusts the stimulation parameters in real time as needed to maintain an optimal stimulation state. The FES control unit (26) can stimulate multiple muscle groups sequentially or simultaneously. For example, performing complex movements such as standing, walking, and sitting requires precise stimulation control for multiple muscle groups, and the FES control unit (26) effectively performs this. The FES control unit (26) provides stimulation tailored to each user's rehabilitation stage and needs. Stimulation patterns set differently for each user contribute to maximizing the rehabilitation effect and minimizing fatigue caused by unnecessary stimulation. The FES control unit (26) may include a safety mechanism to prevent excessive current or unnecessary stimulation to ensure the safety of the stimulation signal. This prevents skin damage or muscle fatigue in the user, thereby supporting long-term rehabilitation. The FES control unit (26) works in conjunction with other system components (e.g., device control unit, communication unit) to ensure integrated and consistent operation of the overall rehabilitation system.This allows the user's imagined movement intention to be quickly and accurately converted into muscle stimulation.

[0051] The device control unit (27) executes the stimulation command transmitted from the FES control unit (26), transmits electrical stimulation to a specific muscle group, and coordinates the organic operation of each component within the system, such as the task generation unit (20), the brain wave detection sensor unit (21), the intention recognition detection unit (23), the visual feedback unit (24), the tactile biofeedback unit (25), and the FES control unit (26). It collects data from each department and manages the consistent operation of the entire system based on this. The device control unit (27) processes all data generated within the system, performs necessary calculations, and transmits the processed data to each department. The device control unit (27) synchronizes the operations of each component so that the system can operate in real time. For example, when the user's imagined movement intention is detected, it immediately transmits a stimulation command to the FES control unit (26) and instructs the visual and tactile feedback units to provide feedback. The device control unit (27) manages the interface with the user. The user interface (UI) controls the system so that users can easily operate it and receive feedback. This provides an intuitive and easy-to-use environment for users. The device control unit (27) maintains the stability of the system and detects and responds to errors when they occur. For example, if there is excessive electrical stimulation or signal interference, the device control unit (27) immediately recognizes this and takes safe measures. The device control unit manages the system's initial settings and adjusts them as needed. Based on the user's rehabilitation goals or personalized requirements, it adjusts each component of the system to operate optimally. The device control unit (27) manages communication with external devices (e.g., computers, smartphones, cloud servers, etc.). This allows data collected during the rehabilitation process to be transmitted externally or the system to be controlled remotely.The device control unit (27) performs functions such as managing the power consumption of the system, monitoring the battery status, and switching to power saving mode when necessary, thereby ensuring that the system operates efficiently.

[0052] The task generation unit (20), brainwave detection sensor unit (21), communication unit (22), intention recognition detection unit (23), visual feedback unit (24), tactile biofeedback unit (25), FES control unit (26), device control unit (27) and additional components mentioned above are expressed separately to make it easy to understand the functions and roles of the movement intention detection and functional electrical stimulation (FES) control system, so that each component does not necessarily have to be implemented independently by a separate device or program. That is, all components may be implemented by one processor equipped in one computer, or may be independently implemented by multiple computers or processors. In addition, the visual feedback unit (24) may be interpreted in a comprehensive sense including a display device such as a display and elements for connecting it to a computer, and the brainwave detection sensor unit (21) may likewise be interpreted in a comprehensive sense including a measurement tool and elements for connecting it to a computer.

[0053] FIG. 3 is a system configuration diagram showing a process of detecting a user's movement intention and activating a leg function based on the intention using a brain-computer interface (BCI) and a functional electrical stimulation (FES) system according to one embodiment.

[0054] Referring to Figure 3, the user (subject) wears an EEG cap, which detects brainwave (EEG) signals generated from the user's scalp in real time. The user is guided to imagine or perform a specific movement (e.g., standing up from a chair) following visual cues displayed on the screen. These visual cues help the user clearly recognize and focus on the imagined movement. The EEG signals detected by the EEG cap are transmitted to a BCI computer. The BCI computer analyzes these signals to recognize the specific movement the user imagines or intends. This step determines the user's movement intention by analyzing the user's EEG signal patterns. The movement intention information analyzed by the BCI computer is transmitted to the FES system. The FES system applies electrical stimulation to specific muscle groups to translate the user's imagined movement into actual movement. For example, if the user imagines standing up, electrical stimulation is applied to the leg muscles, activating them. Referring to Figure 3, electrodes attached to the user's legs are activated by stimulation from the FES system, and this stimulation is triggered by the BCI computer (FES ON trigger). The MCU serves as the system's central control unit and can be included in the device control unit (27). The MCU processes data between the BCI computer and the FES system and comprehensively manages the operation of the entire system. The MCU processes data in real time to provide rapid and accurate stimulation and controls the FES stimulation to ensure proper delivery. Users can verify whether their movement intentions are being properly reflected through visual cues provided by the system. This allows users to train repeatedly and improve their motor skills through continuous stimulation and feedback.

[0055] FIG. 4 is a system configuration diagram showing a rehabilitation training process through voluntary force control and corresponding sensory feedback and virtual reality (VR) feedback using a brain-computer interface (BCI) and a functional electrical stimulation (FES) system according to one embodiment.

[0056] Referring to Figure 4, the figure illustrates the voluntary force control process, whereby a user generates a movement command through their brain waves and, based on this, stimulates their muscles through functional electrical stimulation (FES). During this process, the user imagines their own movement intention and receives electrical stimulation to activate their muscles accordingly. The user wears an EEG cap to measure their brain waves. These EEG signals originate in the user's motor area and are transmitted to a computer (PC) and microcontroller unit (MCU) via a brain-computer interface (BCI). The BCI recognizes the user's movement intention and converts it into an electrical stimulation command (FES Motor Command) to be transmitted to the muscles. The movement command transmitted from the BCI is then executed through the FES system. During this process, the FES activates the muscles by setting an appropriate stimulation intensity and pattern based on the user's movement intention. When the muscles respond to the stimulation and move, sensory feedback regarding this movement is transmitted to the sensory area of ​​the brain. The right side of Figure 4 depicts the process in which the user receives visual feedback through virtual reality (VR). The VR system allows users to monitor in real time how their exercise movements are implemented in the virtual environment. VR feedback provides intuitive visual information when the user imagines or performs the exercise movements, maximizing the effectiveness of rehabilitation training. The entire system is integrated and controlled by a PC and MCU. This can be implemented through the device control unit (27), which manages data between the BCI and FES systems and appropriately adjusts sensory and VR feedback to support the user's repeated and correct exercise movements.

[0057] FIG. 5 is a system configuration diagram showing a process in which a patient with complete lower extremity paralysis voluntarily performs a standing movement using a brain-computer interface (BCI) and a functional electrical stimulation (FES) system according to one embodiment.

[0058] Referring to Fig. 5, Fig. 5 describes a process of performing a voluntary stand-up movement using a brain-computer interface (BCI) and a functional electrical stimulation (FES) system, starting from a state where a user is sitting in a wheelchair. This process aims to detect the user's movement intention and, based on this, stimulate specific muscle groups to perform the stand-up movement. The FES control unit (26) sequentially stimulates the muscle groups required for the user to perform the stand-up movement. At this time, a stimulation signal is transmitted to the device control unit (27), and the signal includes stimulation parameters optimized based on movement data of a normal person stored in a database. As a result, the user can perform the stand-up movement more naturally and efficiently.

[0059] In one example, in order for a user to stand up from a chair, the FES control unit (26) may cause a muscle group including at least one of the user's rectus abdominis, internal oblique, external oblique, iliopsoas, rectus femoris, hamstrings, and tibialis anterior to be stimulated. In this step, muscles for lifting the upper body are activated.

[0060] In one example, during the trunk rise phase, the FES control unit (26) may cause muscle groups including at least one of the user's quadriceps, gastrocnemius, and soleus muscles to be stimulated while the upper body rises. These muscles provide stability to the lower body and ankles and help the trunk fully rise.

[0061] In one example, during a phase where the body is fully standing up, the FES control unit (26) may cause muscle groups including at least one of the user's gluteus maximus, erector spinae, quadriceps femoris, gastrocnemius, and hamstrings to be stimulated. In this phase, the major muscles that enable the user's body to fully stand up are activated.

[0062] The user transmits their movement intentions to the system via a brain-computer interface (BCI). This signal is transmitted to the FES system, which stimulates the appropriate muscle groups to actually perform the sit-to-stand movement. This interaction is a crucial element in helping the user voluntarily stand up. The right side of Figure 5 depicts a user training while standing using a sit-to-stand assist device. This demonstrates that voluntary sit-to-stand movements can strengthen lower extremity functions and allow the user to continue their rehabilitation training.

[0063] The system for detecting a user's movement intention and controlling functional electrical stimulation (FES) based on the detected movement intention according to an embodiment can also be performed in a step-by-step manner by a processor equipped in a computer. Each step can be implemented by a single processor equipped in a single computer, or can be implemented independently by multiple computers or processors. The method can be implemented as an application or in the form of program commands that can be executed by various computer components and recorded on a computer-readable recording medium. The computer-readable recording medium can include program commands, data files, data structures, etc., either singly or in combination.

[0064] Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tape, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, and flash memory.

[0065] Although the above has been described with reference to embodiments, it will be understood by those skilled in the art that various modifications and changes can be made to the present application without departing from the spirit and scope of the present application as set forth in the claims below.

[0066] According to one embodiment of the present application, a system for detecting a user's exercise intention and controlling functional electrical stimulation (FES) based on the user's intention detects and analyzes the user's exercise intention in real time, thereby providing customized electrical stimulation tailored to the individual's neural signal patterns. This enables the implementation of an optimal rehabilitation program tailored to each user's physical characteristics and rehabilitation goals.

[0067] By providing real-time feedback (visual and tactile) based on the user's imagined movement, more intuitive and effective rehabilitation training is possible. This allows users to clearly recognize whether their imagined movement is being performed correctly, accelerating the recovery of lower extremity function.

[0068] This application effectively stimulates the muscles necessary for lower extremity function recovery by providing optimized electrical stimulation to specific muscle groups. By monitoring the user's muscle fatigue and activity in real time and adjusting stimulation parameters, it minimizes muscle fatigue while maintaining maximum stimulation effects.

Claims

1. A system for detecting a user's movement intention and controlling functional electrical stimulation (FES) accordingly. A task generation unit that provides a motor imagination task to a user, creates and manages the task, and evaluates the accuracy of the imagined movement; A brain wave detection sensor unit for collecting brain wave signals generated in real time while the user is imagining movement; An intention recognition detection unit that analyzes brain wave signals collected from the brain wave detection sensor unit to detect the user's intention to exercise; A visual feedback unit that provides visual feedback of brain wave changes on a display based on movement intention information detected from the above intention recognition detection unit, and allows the user to visually confirm whether the user is performing movement imagination correctly; A tactile biofeedback unit that provides tactile stimulation according to movement imagination based on movement intention information transmitted from the above-mentioned intention recognition detection unit, thereby assisting the user to repeatedly perform movement imagination; An FES control unit that analyzes exercise intention by linking the results of the above intention recognition detection unit with a database and generates a command to stimulate a specific muscle group based on the analyzed results; and A system characterized by comprising a device control unit that executes a stimulation command transmitted from the FES control unit to transmit electrical stimulation to a specific muscle group and comprehensively controls a task generation unit, an brain wave detection sensor unit, an intention recognition detection unit, a visual feedback unit, a tactile biofeedback unit, and an FES control unit.

2. In paragraph 1, The above task creation unit, Create motor imagery tasks tailored to the user's rehabilitation goals. For each motor imagery task, set conditions that include at least one of the target movement, performance time, and number of repetitions. It supports task performance by linking with visual feedback and tactile biofeedback to provide optimal feedback to users according to set conditions. A system characterized by recording and analyzing the user's task performance results and automatically adjusting the difficulty of future motor imagination tasks or creating new tasks.

3. In paragraph 1, The above brain wave detection sensor part, Brainwave signals are collected in real time through electrodes attached to the user's scalp. Filtering and amplification processes are performed to remove noise from the collected brainwave signals and increase the accuracy of the signals. It is designed to detect and analyze brain wave changes in specific frequency bands that occur according to the user's imagined exercise movements. A system characterized by including non-invasive electrodes for consistent signal collection in different environments and adjustable according to the user's movements or skin condition.

4. In paragraph 3, The above brain wave detection sensor part is configured in the form of an EEG cap that can be worn on the head by the user. The electrodes are attached securely to the user's scalp, A system characterized by being able to accurately collect brain wave signals in real time.

5. In paragraph 1, A system characterized in that it further includes a communication unit that communicates to transmit brain wave signals collected from the brain wave detection sensor unit to a task generation unit and an intention recognition detection unit.

6. In paragraph 1, The above visual feedback section provides the user's exercise intention as a visual cue, A system characterized in that the above visual cue is configured in the form of a graphic, image or animation displayed on a display in real time during the exercise imagination process so that the user can effectively imagine the target exercise movement.

7. In paragraph 6, The above visual feedback section, Analyze changes in brainwave signals in real time according to the user's exercise intention, Based on the analyzed results, a gauge bar is displayed on the display. The above gauge bar provides visual feedback in the form of increasing or decreasing in proportion to the intensity or accuracy of the user's imagined movement. A system characterized in that a gauge bar is guided to reach a specific threshold when a user successfully imagines a target action, thereby allowing the user to visually confirm and recall a specific action intention.

8. In paragraph 1, The above tactile biofeedback unit, Provides tactile stimulation including vibration to the user's skin based on the result of the above intention recognition detection unit, A system characterized by assisting a user to perform repetitive and consistent motor imagery.

9. In paragraph 1, The above intention recognition detection unit, The brain wave signals collected from the above brain wave detection sensor unit are input in real time, and the neural signal patterns related to specific motor movements imagined by the user are analyzed. Extract features of brainwave signals in time domain and frequency domain, Machine learning algorithms or deep learning models are used to detect the user's exercise intention. By learning neural signal patterns that appear differently for each user, it enables personalized movement intention detection. A system characterized in that data generated according to detected movement intention is transmitted to a visual feedback unit, a tactile biofeedback unit, and a device control unit, so that the system can provide feedback and stimulation according to the user's movement imagination results.

10. In paragraph 1, The above FES control unit, Generate a control signal to apply electrical stimulation to a specific muscle group according to the movement intention signal received from the above intention recognition detection unit, The control signal is set to ensure effective stimulation of the corresponding muscle group, including parameters of the electrical stimulation including pulse width, frequency, amplitude, and duty cycle. To provide customized stimulation for each user, stimulation parameters can be adjusted by referring to the movement data of normal people stored in the database or the physical characteristics of the individual user. A system characterized in that after stimulation is completed, the muscle response is monitored based on electromyography (EMG) signals and force sensor data, fatigue and stimulation effect are evaluated, and stimulation parameters are adjusted in real time to maintain an optimal stimulation effect.

11. In paragraph 10, The above FES control unit monitors muscle fatigue and activity through FES stimulation, and analyzes the median frequency of electromyography (EMG) signals and force sensor data to adjust stimulation parameters, thereby optimizing muscle stimulation according to fatigue.

12. In paragraph 1, The above database stores muscle activation patterns established based on normal person's movement data, A system characterized in that the FES control unit stimulates the muscle by adjusting at least one of pulse width, frequency, amplitude, and duty cycle according to the pattern.

13. In paragraph 12, A system characterized in that the FES control unit transmits a stimulation signal to the device control unit to sequentially stimulate muscle groups required for movements including at least one of standing, walking, and sitting, and the signal includes stimulation parameters optimized based on movement data of a normal person stored in a database.

14. In paragraph 13, When standing up, The step of rising from a chair stimulates muscle groups including at least one of the following: Rectus Abdominis, Internal Oblique, External Oblique, Iliopsoas, Rectus Femoris, Hamstrings, and Tibialis Anterior. In the trunk-building phase, it stimulates muscle groups that include at least one of the quadriceps, gastrocnemius, and soleus. A system characterized by stimulating muscle groups including at least one of the gluteus maximus, erector spinae, quadriceps femoris, gastrocnemius, and hamstrings during the fully standing phase of the body.

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