Spinal cord electrical stimulation control method and system based on tremor detection

Abstract

The invention discloses a spinal cord electrical stimulation control method and system based on tremor detection. The method comprises the following steps: acquiring motion sensing data of a user; extracting tremor signal features of the user according to the motion sensing data and a preset feature extraction algorithm; determining an electrical stimulation parameter corresponding to the tremor signal feature according to a preset electrical stimulation parameter determination rule; according to the electrical stimulation parameters, generating an electrical stimulation control instruction corresponding to electrical stimulation equipment arranged in a spinal cord area of the user; the electrical stimulation control instruction is used for indicating current output of the electrical stimulation equipment. Therefore, accurate nerve intervention control in combination with tremor monitoring and spinal cord electrical stimulation can be realized, the physical therapy inhibition effect of electrical stimulation physical therapy measures on the tremor of the user is improved, and more efficient and accurate nerve regulation and control service is provided for the user.

Description

Technical Field

[0001] This invention relates to the field of spinal cord electrical stimulation technology, and in particular to a spinal cord electrical stimulation control method and system based on tremor detection. Background Technology

[0002] Motor dysfunction caused by neurological disorders such as essential tremor, especially postural and action tremors, severely impacts patients' quality of life. Traditional treatment methods include pharmacological interventions (such as beta-blockers and antiepileptic drugs) and invasive procedures like deep brain stimulation. However, pharmacological interventions suffer from significant individual variability and side effects; while invasive procedures are characterized by high risk and high cost, making widespread application difficult.

[0003] In recent years, non-invasive neuromodulation techniques have gained widespread attention as a safe and convenient alternative. For example, transcutaneous spinal cord stimulation (tSCS), as an emerging neuromodulation technique, has shown potential for motor function regulation. Its basic principle is to apply low-frequency electrical stimulation to specific segments of the spinal cord through electrodes placed on the body surface, thereby modulating the excitability of the central pattern generator in the spinal cord, influencing the sensorimotor integration pathway, and ultimately regulating motor output. tSCS can indirectly affect the central nervous system by acting on afferent fibers at the dorsal root entrance of the spinal cord, thereby inhibiting tremors caused by pathological oscillations.

[0004] However, most existing electrical stimulation technologies still rely on fixed or manually adjusted electrical stimulation parameters, heavily depending on the operator's skill level. Furthermore, they are unable to adapt to different institutional settings, lack individualization and adaptability, resulting in a poor user experience. Clearly, existing technologies have shortcomings that urgently need to be addressed. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a spinal cord electrical stimulation control method and system based on tremor detection, which can realize precise neuro-intervention control by combining tremor monitoring and spinal cord electrical stimulation, improve the therapeutic inhibition effect of electrical stimulation physiotherapy on users' tremors, and provide users with more efficient and precise neuromodulation services.

[0006] To address the aforementioned technical problems, the first aspect of this invention discloses a spinal cord electrical stimulation control method based on tremor detection, the method comprising: Acquire user motion sensor data; Based on the motion sensing data and the preset feature extraction algorithm, the user's tremor signal features are extracted; Based on the preset electrical stimulation parameter determination rules, the electrical stimulation parameters corresponding to the tremor signal characteristics are determined; Based on the electrical stimulation parameters, an electrical stimulation control command is generated corresponding to the electrical stimulation device located in the user's spinal cord region; the electrical stimulation control command is used to indicate the current output of the electrical stimulation device.

[0007] As an alternative implementation, in a first aspect of the invention, the motion sensing data is obtained by a motion sensor disposed in the user's hand area.

[0008] As an optional implementation, in the first aspect of the present invention, the motion sensing data is acceleration data or electrical activity data; the acceleration data includes acceleration in multiple directional dimensions.

[0009] As an optional implementation, in the first aspect of the present invention, the step of extracting the user's tremor signal features based on the motion sensing data and a preset feature extraction algorithm includes: Calculate the square mean of the accelerations in the multiple directional dimensions to obtain the composite acceleration parameters; Based on the synthesized acceleration parameters, the user's tremor signal characteristics are determined; the tremor signal characteristics include at least one of tremor frequency and tremor intensity variation parameters.

[0010] As an optional implementation, in a first aspect of the invention, determining the user's tremor signal characteristics based on the synthesized acceleration parameters includes: The synthesized acceleration parameters are subjected to high-pass filtering to obtain the processed signal; The processed signal within the preset time window is subjected to Fast Fourier Transform and Power Spectral Density Analysis to identify the power spectral peak and obtain the user's tremor frequency. And / or, The root mean square value of the processed signal within a preset time window is calculated to obtain the flutter characterization value; Determine the baseline tremor data corresponding to the user; Based on the baseline tremor data and the tremor characterization values, the tremor intensity variation parameters of the user are determined.

[0011] As an optional implementation, in the first aspect of the invention, the electrical stimulation parameters include at least one of a frequency parameter, a pulse width parameter, and a current intensity parameter.

[0012] As an optional implementation, in the first aspect of the invention, the frequency parameter is the same as the tremor frequency; The pulse width parameter is inversely proportional to the tremor frequency; The current intensity parameter is determined by the following conditions: When the vibration intensity change parameter is a vibration increase of a first preset ratio, the current intensity parameter is the current current intensity increased by a first preset parameter value; When the vibration intensity change parameter is a second preset ratio for vibration reduction, the current intensity parameter is a parameter value for the current current intensity reduced by a second preset value. When the vibration intensity change parameter is within a preset stable ratio range, the current intensity parameter remains unchanged.

[0013] As an optional implementation, in the first aspect of the present invention, the method further includes: Real-time determination of whether the tremor signal features obtained from the most recent preset number of calculations all meet the preset tremor mitigation numerical rules; If so, a stop command is sent to the electrical stimulation device.

[0014] A second aspect of this invention discloses a spinal cord electrical stimulation control system based on tremor detection, the system comprising: The acquisition module is used to acquire the user's motion sensor data; The extraction module is used to extract the user's tremor signal features based on the motion sensing data and a preset feature extraction algorithm. The determination module is used to determine the electrical stimulation parameters corresponding to the tremor signal characteristics according to the preset electrical stimulation parameter determination rules; The control module is used to generate electrical stimulation control commands corresponding to the electrical stimulation device set in the user's spinal cord region based on the electrical stimulation parameters; the electrical stimulation control commands are used to indicate the current output of the electrical stimulation device.

[0015] As an alternative implementation, in a second aspect of the invention, the motion sensing data is obtained by a motion sensor disposed in the user's hand area.

[0016] As an optional implementation, in a second aspect of the invention, the motion sensing data is acceleration data or electrical activity data; the acceleration data includes acceleration in multiple directional dimensions.

[0017] As an optional implementation, in a second aspect of the invention, the extraction module extracts the user's tremor signal features based on the motion sensing data and a preset feature extraction algorithm in the following specific manner: Calculate the square mean of the accelerations in the multiple directional dimensions to obtain the composite acceleration parameters; Based on the synthesized acceleration parameters, the user's tremor signal characteristics are determined; the tremor signal characteristics include at least one of tremor frequency and tremor intensity variation parameters.

[0018] As an optional implementation, in a second aspect of the invention, the extraction module determines the specific method by which it determines the user's tremor signal characteristics based on the synthesized acceleration parameters, including: The synthesized acceleration parameters are subjected to high-pass filtering to obtain the processed signal; The processed signal within the preset time window is subjected to Fast Fourier Transform and Power Spectral Density Analysis to identify the power spectral peak and obtain the user's tremor frequency. And / or, The root mean square value of the processed signal within a preset time window is calculated to obtain the flutter characterization value; Determine the baseline tremor data corresponding to the user; Based on the baseline tremor data and the tremor characterization values, the tremor intensity variation parameters of the user are determined.

[0019] As an optional implementation, in a second aspect of the invention, the electrical stimulation parameters include at least one of a frequency parameter, a pulse width parameter, and a current intensity parameter.

[0020] As an optional implementation, in a second aspect of the invention, the frequency parameter is the same as the tremor frequency; The pulse width parameter is inversely proportional to the tremor frequency; The current intensity parameter is determined by the following conditions: When the vibration intensity change parameter is a vibration increase of a first preset ratio, the current intensity parameter is the current current intensity increased by a first preset parameter value; When the vibration intensity change parameter is a second preset ratio for vibration reduction, the current intensity parameter is a parameter value for the current current intensity reduced by a second preset value. When the vibration intensity change parameter is within a preset stable ratio range, the current intensity parameter remains unchanged.

[0021] As an optional implementation, in a second aspect of the invention, the system is further configured to perform the following steps: Real-time determination of whether the tremor signal features obtained from the most recent preset number of calculations all meet the preset tremor mitigation numerical rules; If so, a stop command is sent to the electrical stimulation device.

[0022] A third aspect of the present invention discloses another spinal cord electrical stimulation control system based on tremor detection, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute some or all of the steps in the spinal cord electrical stimulation control method based on tremor detection disclosed in the first aspect of the present invention.

[0023] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the spinal cord electrical stimulation control method based on tremor detection disclosed in the first aspect of the present invention.

[0024] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention acquires the user's motion sensor data to extract the user's tremor signal characteristics, and determines the corresponding electrical stimulation parameters based on the tremor signal characteristics to control the electrical stimulation device to output current to the user's spinal cord area for stimulation. This enables precise neuro-intervention control by combining tremor monitoring and spinal cord electrical stimulation, improving the therapeutic and inhibitory effect of electrical stimulation on the user's tremor, and providing the user with more efficient and precise neuromodulation services. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic flowchart of a spinal cord electrical stimulation control method based on tremor detection disclosed in an embodiment of the present invention.

[0027] Figure 2 This is a schematic diagram of a spinal cord electrical stimulation control system based on tremor detection disclosed in an embodiment of the present invention.

[0028] Figure 3 This is a schematic diagram of another spinal cord electrical stimulation control system based on tremor detection disclosed in an embodiment of the present invention.

[0029] Figure 4 This is a schematic diagram of the architecture of a closed-loop adaptive neuromodulation system based on inertial sensing and transcutaneous spinal cord stimulation disclosed in an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0032] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0033] This invention discloses a spinal cord electrical stimulation control method and system based on tremor detection. By acquiring the user's motion sensor data to extract the user's tremor signal characteristics, and determining the corresponding electrical stimulation parameters based on these characteristics, the method controls the electrical stimulation device to output current to the user's spinal cord region for stimulation. This enables precise neuro-intervention control combining tremor monitoring and spinal cord electrical stimulation, improving the therapeutic effect of electrical stimulation on tremor suppression and providing users with more efficient and precise neuromodulation services. Detailed descriptions follow.

[0034] Example 1 Please see Figure 1 , Figure 1 This is a schematic flowchart of a spinal cord electrical stimulation control method based on tremor detection disclosed in an embodiment of the present invention. Figure 1 The described tremor detection-based spinal cord stimulation control method can be applied to data processing systems / data processing devices / data processing servers (including local processing servers or cloud processing servers). For example... Figure 1 As shown, this spinal cord electrical stimulation control method based on tremor detection may include the following operations: 101. Obtain the user's motion sensor data.

[0035] Optionally, motion sensing data is acquired through motion sensors located in the user's hand area.

[0036] Optionally, the motion sensing data can be acceleration data or electrical activity data.

[0037] Optionally, the acceleration data includes acceleration in multiple directional dimensions.

[0038] 102. Based on motion sensing data and a preset feature extraction algorithm, extract the user's tremor signal features.

[0039] 103. Determine the electrical stimulation parameters corresponding to the tremor signal characteristics according to the preset electrical stimulation parameter determination rules.

[0040] 104. Based on the electrical stimulation parameters, generate electrical stimulation control commands corresponding to the electrical stimulation device set in the user's spinal cord region.

[0041] Optionally, electrical stimulation control commands are used to instruct the current output of the electrical stimulation device.

[0042] As can be seen, the above-described embodiments of the invention acquire the user's motion sensing data to extract the user's tremor signal characteristics, and determine the corresponding electrical stimulation parameters based on the tremor signal characteristics to control the electrical stimulation device to output current to the user's spinal cord area for stimulation. This enables precise neuro-intervention control by combining tremor monitoring and spinal cord electrical stimulation, improves the therapeutic and inhibitory effect of electrical stimulation physiotherapy on the user's tremor, and provides the user with more efficient and precise neuromodulation services.

[0043] As an optional embodiment, the step described above, extracting the user's tremor signal features based on motion sensing data and a preset feature extraction algorithm, includes: Calculate the squared mean of accelerations in multiple directional dimensions to obtain the composite acceleration parameters; Based on the synthesized acceleration parameters, the characteristics of the user's tremor signal are determined.

[0044] Optionally, the tremor signal characteristics include at least one of the parameters of tremor frequency and tremor intensity variation.

[0045] As can be seen, through the above optional embodiments, by calculating the square mean of acceleration signals in multiple directional dimensions to obtain synthetic acceleration parameters, and then further determining tremor signal characteristics based on the synthetic acceleration parameters, it is possible to achieve synthetic analysis of velocity characteristics in multiple directional dimensions, so as to accurately characterize the user's tremor condition and assist in achieving more precise electrical stimulation therapy in the future.

[0046] As an optional embodiment, the step of determining the user's tremor signal characteristics based on the synthesized acceleration parameters in the above steps includes: The synthesized acceleration parameters are subjected to high-pass filtering to obtain the processed signal; Fast Fourier Transform and power spectral density analysis are performed on the processed signal within a preset time window to identify the power spectral peak and obtain the user's tremor frequency.

[0047] As an optional embodiment, the step of determining the user's tremor signal characteristics based on the synthesized acceleration parameters in the above steps includes: The root mean square value of the processed signal within a preset time window is calculated to obtain the flutter characterization value; Determine the user's baseline tremor data; Based on baseline tremor data and tremor characterization values, determine the tremor intensity variation parameters for the user.

[0048] Optionally, the baseline tremor data is a tremor characterization value calculated from the user's sensor data when no electrical stimulation is applied.

[0049] Optionally, the calculation method for the tremor intensity variation parameter may include: Based on the mean and standard parameters in the baseline tremor data, the tremor characterization values ​​are normalized using the z-score normalization algorithm to obtain the tremor intensity variation parameters.

[0050] Optionally, the calculation method for the tremor intensity variation parameter may include: The parameter for calculating the change in tremor intensity is calculated as follows: (tremor characterization value - baseline tremor data) / baseline tremor data × 100%.

[0051] As can be seen, through the above optional embodiments, by high-pass filtering and analysis of power spectrum peaks or by analysis based on the user's baseline data, the tremor signal characteristics such as the user's tremor frequency and tremor intensity variation parameters can be determined, so as to comprehensively and accurately characterize the user's real-time or periodic tremor, and assist in achieving more precise electrical stimulation therapy.

[0052] As an optional embodiment, the electrical stimulation device is a transcutaneous spinal cord stimulation (tSCS) device.

[0053] Specifically, its electrical stimulation parameters include at least one of frequency parameters, pulse width parameters, and current intensity parameters.

[0054] Optionally, the frequency parameter is the same as the tremor frequency.

[0055] Optionally, the pulse width parameter is inversely proportional to the tremor frequency.

[0056] Optionally, the current intensity parameter is determined by the following conditions: When the vibration intensity change parameter is the vibration increase by a first preset ratio, the current intensity parameter is the current current intensity increased by a first preset parameter value; When the vibration intensity change parameter is the vibration reduction by a second preset ratio, the current intensity parameter is the current current intensity reduced by a second preset parameter value; When the vibration intensity change parameter is within the preset stable ratio range, the current intensity parameter remains unchanged.

[0057] As can be seen, the above optional embodiments define the correspondence between different electrical stimulation parameters and tremor signal characteristics, so as to obtain more targeted electrical stimulation parameters based on tremor signal characteristics, realize precise neuro-intervention control by combining tremor monitoring and spinal cord electrical stimulation, and improve the therapeutic and inhibitory effect of electrical stimulation physiotherapy on the user's tremor.

[0058] As an optional embodiment, the method further includes the following steps: Real-time determination of whether the characteristics of the tremor signal obtained from the most recent preset number of calculations all meet the preset tremor reduction numerical rules; If so, send a stop command to the electrical stimulation device.

[0059] As can be seen, through the above optional embodiments, by stopping electrical stimulation when it is determined that the characteristics of the most recent tremor signals all meet the preset tremor reduction numerical rules, the system can automatically stop working after the tremor is suppressed, thereby achieving more intelligent and automated control of electrical stimulation therapy.

[0060] Specifically, based on the technical solution disclosed in the embodiments of this invention, a closed-loop adaptive neuromodulation system based on inertial sensing and transcutaneous spinal cord stimulation is realized. The system architecture diagram can be found in the following reference. Figure 4 During the development of this system, the following shortcomings were identified in some existing technical solutions: 1. Lack of individualization and adaptability, resulting in unstable therapeutic effects: Tremor frequency (typically in the range of 4-12 Hz) exhibits significant inter-individual variability and intra-individual fluctuations. Open-loop fixed-frequency stimulation cannot synchronize with the patient's real-time changing individualized tremor frequency, resulting in low coupling efficiency between stimulation and pathological oscillations. When the patient's tremor frequency changes with time or task, the effect of fixed-parameter stimulation will significantly decrease or even disappear, leading to unstable and unreliable therapeutic effects.

[0061] 2. Insufficient targeting of the stimulus, posing a risk of side effects: In pursuit of potential therapeutic effects, open-loop systems often employ relatively high, fixed stimulation intensities. This can easily lead to overactivation of non-target nerve fibers (such as motor fibers), causing side effects such as muscle spasms and tingling sensations. Furthermore, the inability to precisely synchronize stimulation with the tremor cycle makes it difficult to optimize the stimulation intensity to the minimum effective level while ensuring therapeutic efficacy. This impacts safety and comfort, limiting the long-term application of the device.

[0062] 3. Poor user experience, relying on subjective judgment: This system relies entirely on the user's subjective judgment to initiate and stop stimulation, failing to provide proactive intervention when tremors first occur or to automatically reduce stimulation to conserve energy after tremor suppression. This passive operating mode increases the user's cognitive burden, results in a poor experience, and makes timely and continuous state management difficult.

[0063] In summary, the core deficiency of existing tremor suppression techniques based on tSCS lies in their "open-loop" mode—that is, the lack of real-time feedback and adaptive correlation between stimuli and physiological states. This prevents them from addressing the issues of individualization, dynamism, and comfort in tremor control, severely limiting the practical application effectiveness of this technology.

[0064] This system aims to overcome all the aforementioned shortcomings by introducing real-time tremor monitoring based on inertial sensors to construct an adaptive closed-loop system capable of dynamically adjusting tSCS stimulation parameters. This achieves more precise, efficient, and comfortable symptom suppression. Specifically, its research and development objectives include: Objective 1: To achieve precise, adaptive control of tremor frequency. By integrating an IMU to monitor tremor frequency characteristics in real time, a dynamic matching mechanism between stimulation parameters and individual tremor characteristics is established. Specifically, this system aims to achieve adaptive synchronization between the tSCS stimulation frequency (adjustable from 1-100Hz) and the real-time tremor frequency (f_t, 4-12Hz), and dynamically optimize the current parameters within the 0-200mA range based on tremor intensity, overcoming the rigidity of fixed-parameter systems. Through frequency matching technology, the stimulation signal and pathological oscillations produce optimal interference effects, improving the accuracy and stability of intervention.

[0065] Objective 2: To establish a safe and efficient closed-loop control strategy. By constructing a closed-loop control architecture of "detection-stimulation-feedback," precise synchronization between stimulation parameters and tremor state is achieved. Specifically, this includes: intensity adjustment based on real-time IMU feedback (0-200mA dynamic range), timing optimization based on tremor phase recognition, and safety protection based on impedance monitoring. This system aims to maintain the stimulation intensity at the lowest effective level (typically 30-100mA) while ensuring suppression through intelligent adjustment mechanisms, thereby fundamentally preventing non-target activation and improving system safety and comfort.

[0066] Objective 3: To construct a fully automated intelligent intervention system. By integrating real-time IMU sensing with dynamic tSCS control, an end-to-end automated intervention process is established. Specifically, this includes automatic tremor identification and early warning, intelligent optimization of stimulation parameters, and real-time assessment and adjustment of treatment efficacy. This system aims to eliminate reliance on subjective judgment, enabling autonomous management of the entire process from tremor onset to suppression, providing timely, continuous, and adaptive symptom control solutions, and significantly improving user experience and adherence.

[0067] The core technological value of this system lies in upgrading open-loop stimulation to an intelligent closed-loop system. Through a real-time perception-decision-execution mechanism, it solves the fundamental problem of "disconnect between stimulation and physiological state" in existing technologies, providing a more precise, safe, and intelligent technical solution for motor function regulation.

[0068] like Figure 4 As shown, the system mainly consists of four functional subsystems: power management, main control and interaction, stimulation output, and signal acquisition.

[0069] The power management subsystem, with the BMS battery management module at its core, is responsible for the charging and discharging control of the lithium battery, safety protection, and power distribution of each unit in the system. It also provides a stable and adjustable high-voltage power rail for the subsequent stimulation output unit through a Boost circuit.

[0070] The main control and interaction subsystem is built on an MCU and is responsible for signal processing, closed-loop algorithm execution and system scheduling. It also provides user command input and visual feedback on device status through buttons and an LCD display.

[0071] The stimulation output subsystem is the core execution unit of the system, consisting of a voltage-controlled constant current source, a Wilson current mirror, an H-bridge, and a power drive module. The digital waveform generated by the MCU is converted into an analog current signal by the voltage-controlled constant current source, then amplified by the Wilson current mirror to meet the maximum output requirement of 200mA. Finally, the H-bridge circuit converts the amplified unipolar signal into a bipolar stimulation waveform that can be output to the load electrodes.

[0072] The signal acquisition subsystem uses an IMU inertial sensor as its core to monitor limb movement in real time, providing the MCU with raw data for tremor feature analysis and forming the sensing front end of the closed-loop control system.

[0073] Specifically, the workflow of this system can be briefly described as follows: The system starts up and completes the wearing process --> Electrode-skin contact impedance detection is performed to ensure reliable electrode contact --> The IMU inertial sensor continuously monitors hand movement at a sampling rate of 100Hz --> Real-time signal processing and individualized tremor frequency (f_t, 4-12Hz) identification --> Based on f_t, tSCS stimulation parameters are automatically generated (carrier 10kHz, modulation frequency f_t, current intensity 0-200mA dynamically adjustable) and stimulation output is triggered --> The IMU monitors tremor intensity changes in real time, and the system dynamically adjusts tSCS stimulation parameters (frequency fine-tuning, intensity optimization) --> An adaptive closed-loop control cycle is formed --> The system stops when the preset inhibition threshold is reached or when the user intervenes manually.

[0074] Specifically, the detailed workflow of this system will be explained in detail: (1) System initialization and security testing phase: After the device is powered on, it first performs a hardware self-test and electrode-skin contact impedance detection. By measuring the electrode circuit impedance (range 0-30kΩ), the system determines the contact status of the three electrodes actually used in the four circular interfaces. If the impedance value exceeds the safe range (>10kΩ), the system displays a warning message on the LCD screen and pauses the stimulation output to ensure safe use. After the detection is successful, the IMU sensor begins to continuously collect triaxial acceleration data of the hand at a sampling rate of 100Hz.

[0075] (2) Tremor signal processing and feature extraction: The raw acceleration data acquired by the IMU underwent multiple signal preprocessing steps: First, the synthetic vector amplitude (SVM = √(x² + y² + z²)) was calculated. Then, a 10th-order Butterworth high-pass filter (cutoff frequency 3Hz) was used to filter out the autonomous motion components, retaining the tremor-related signals. The preprocessed signals were segmented into 2.5-second time windows, and Fast Fourier Transform (FFT) and Power Spectral Density (PSD) analyses were performed on each segment. The frequency corresponding to the power spectral peak within the 4-12Hz frequency band was identified as the individualized tremor frequency f_t.

[0076] The tremor frequency identification algorithm process includes signal segmentation, FFT transformation, and peak detection to ensure the accuracy of frequency extraction.

[0077] (3) Generation and output of stimulus parameters: Based on the real-time identified tremor frequency f_t, the stimulation parameter generation module automatically calculates the tSCS stimulation parameters: the carrier frequency is fixed at a 10kHz high-frequency sine wave, the modulation frequency is set to be synchronized with f_t (adaptive within the range of 1-100Hz), the initial current intensity is set to a safe starting value (typically 30-50mA) based on the impedance detection results, and the pulse width is configured to 200-500μs. After the parameter calculation is completed, the stimulation circuit outputs a biphasic balanced pulse waveform, which forms a targeted electric field through three activation electrodes (cathode: between C5-C6 spinous processes, anode: bilateral clavicles).

[0078] The tSCS stimulation circuit uses a complementary output constant current source topology to ensure the accuracy and safety of stimulation.

[0079] (4) Closed-loop adaptive adjustment stage: After stimulation is initiated, the IMU continuously monitors changes in tremor intensity (using the root mean square (RMS) value of the acceleration signal as an indicator). The system dynamically adjusts stimulation parameters based on a comparison of real-time tremor intensity with the baseline value: when tremor intensity increases by >15%, the current intensity is increased in 1mA steps (maximum limit 200mA); when tremor intensity drops below 50% of the baseline value, the intensity is decreased in 0.5mA steps. Simultaneously, the system recalculates the tremor frequency f_t every 10 seconds to ensure the modulation frequency is synchronized with the real-time tremor characteristics.

[0080] (5) Conditions for loop execution and termination: The aforementioned "monitoring-analysis-stimulation-regulation" cycle runs continuously with a period of 100ms, forming a closed-loop control. Termination conditions include: a) tremor intensity remains below 20% of the baseline value for 30 consecutive seconds (effective suppression); b) the user manually presses the pause / stop button; c) the system detects a safety anomaly (such as impedance surge or excessive temperature). After stimulation stops, the system automatically enters a low-power monitoring mode, continuously monitoring the tremor status to prepare for the next intervention.

[0081] The entire workflow utilizes a real-time feedback adjustment mechanism to achieve complete closed-loop control from tremor detection to adaptive intervention, effectively overcoming the rigidity of parameters in traditional open-loop systems. System status information and warning prompts are displayed in real-time on an LCD screen, providing users with clear operational feedback.

[0082] Further elaborating on the technical details of this BMS battery management subsystem, its core functions are to manage system charge and discharge, monitor battery status, and provide multiple safety protections. By collecting key parameters such as battery current, voltage, and temperature in real time, it implements multiple safety mechanisms, including undervoltage protection, overcurrent protection, short-circuit protection, and overtemperature protection, ensuring that the battery always operates within a safe range.

[0083] The specific implementation scheme adopts a single-cell lithium battery management system based on the architecture of "BQ27220 (battery fuel gauge) + DW01 (protection IC) + MOSFET (such as 8205A)". In this scheme, BQ27220, as the core fuel gauge, is responsible for battery status monitoring and management, while DW01, as a dedicated protection chip, provides hardware-level safety protection. The two work together to ensure the safety and stability of the system.

[0084] The main functions of the BQ27220 battery fuel gauge include: (1) Battery status monitoring: Real-time acquisition of key parameters such as battery state of charge (SOC), terminal voltage, and operating temperature, and reporting the data to the main control MCU through the I²C interface to provide data support for power display and battery health management.

[0085] (2) Charging control: Parameters such as charging rate and charging cut-off voltage (typical value 4.2V) can be configured, and safe and efficient charging management can be achieved by working in conjunction with the charging circuit.

[0086] (3) Capacity estimation: Based on the intelligent algorithm combining coulomb counting method and open circuit voltage method, the remaining battery capacity (mAh) and cycle life status are dynamically estimated to provide users with accurate power reference.

[0087] (4) Temperature monitoring: Continuously monitor the battery temperature. When the temperature exceeds the safety threshold, immediately send an interrupt signal to the main control MCU to start the protection mechanism.

[0088] The functions implemented by the DW01 protection IC in conjunction with the 8205A MOSFET include: (1) Overcharge protection: When the battery voltage is detected to exceed 4.25V (configurable), the charging circuit is automatically cut off; when the voltage drops back to the safe range (4.05V), charging is resumed.

[0089] (2) Over-discharge protection: When the battery voltage is below 2.5V (configurable), the discharge circuit is cut off to protect the battery; normal discharge resumes after the voltage rises back to 3.0V.

[0090] (3) Overcurrent protection: Real-time monitoring of discharge current, and immediate shutdown of MOSFET when the current exceeds the 2A (configurable) setting value.

[0091] (4) Short circuit protection: It has a fast response capability and can quickly cut off the circuit within 10μs when a load short circuit is detected, effectively preventing battery damage.

[0092] Further explanation of the technical details of the MCU main control circuit subsystem: As the core control unit of the entire device, this subsystem undertakes two key functions: hardware peripheral driving and business logic scheduling. Through a carefully designed embedded software architecture, it achieves coordinated control and task management of various modules of the system.

[0093] The hardware peripheral driver functionality covers driving the LCD display, various function buttons, battery management chips, and effectively utilizing the rich peripheral resources within the MCU, including a 12-bit ADC analog-to-digital converter, 16 / 32-bit timers, and a DAC digital-to-analog converter. These peripherals provide the system with powerful signal acquisition, processing, and control capabilities.

[0094] The business logic scheduling function is responsible for the priority management and real-time scheduling of various tasks in the system, mainly including: LCD human-machine interface display update, key operation logic response processing, real-time acquisition and analysis of electrode impedance, tSCS stimulation waveform generation and output (based on DAC), battery status monitoring and processing and other key tasks.

[0095] The specific implementation scheme uses the AT32F403ACGT7 as the main control MCU. This chip is based on the high-performance ARM Cortex-M4F core with a main frequency of up to 240MHz and integrates a floating-point unit (FPU), which can meet the real-time calculation requirements of complex algorithms. Its rich peripheral resources perfectly meet the system requirements: three 12-bit ADCs are used for impedance detection and battery voltage acquisition; two 12-bit DACs are responsible for outputting analog voltage signals to generate tSCS waveforms; eight 16-bit general-purpose timers and two 32-bit timers provide precise timing control; and eight USART / UART interfaces (including a Type-C serial port) ensure reliable communication connections.

[0096] Further explanation of the technical details of the button / LCD display interaction module circuit subsystem. This subsystem constitutes the core of the device's human-machine interaction, providing users with a user-friendly operating experience through an intuitive interface and clear status display.

[0097] The button module adopts a 5-way mechanical button design, including function keys for mode switching, intensity adjustment (increase / decrease), start / pause control, and power on / off. It supports two operation modes: short press (step operation) and long press (quick adjustment), and the software debouncing algorithm ensures the accuracy and reliability of button response.

[0098] The LCD display shows important information in real time, including stimulation parameters (output intensity, stimulation frequency, operating mode, etc.), remaining battery power, and device operating status. It provides users with comprehensive device status feedback through an intuitive graphical interface.

[0099] The status indication system provides intuitive status prompts through multi-color LED indicators: red indicates a device malfunction or abnormal status, green indicates charging in progress, and blue indicates charging is complete. This visual cues effectively complement the LCD display, enhancing the user experience.

[0100] Further details the technical aspects of the transcutaneous spinal cord stimulation (tSCS) output subsystem. As the core execution unit of the device, this subsystem achieves tremor suppression through precise current output. The entire system comprises a complete closed-loop control system consisting of modules such as a voltage-controlled constant current source, a Wilson current mirror, and an H-bridge power drive.

[0101] Specifically, the workflow of this subsystem includes the following steps: Waveform generation: Pre-synthesized tSCS modulated waveforms (square wave format, stimulus frequency adjustable from 1-100Hz, carrier frequency 10kHz) are stored in the MCU Flash. During system operation, the MCU reads the corresponding waveform data according to the user-defined parameters and converts it into an analog voltage signal via the built-in DAC.

[0102] Signal conditioning: The generated analog voltage signal is conditioned by a high-precision operational amplifier (such as OPA2188AIDR) to effectively eliminate noise interference and match the input impedance requirements of subsequent circuits.

[0103] Current amplification: The conditioned voltage signal is input to a voltage-controlled constant current source based on the Howland architecture to generate an initial current signal, which is then amplified by a Wilson current mirror structure to finally meet the maximum output requirement of 200mA.

[0104] Bipolar output: The amplified unipolar current signal is converted into a bipolar waveform through an H-bridge circuit and finally output to the stimulation electrode to act on the target area.

[0105] Impedance monitoring and safety protection: The system monitors load impedance changes in real time and immediately activates the protection mechanism when an impedance abnormality (such as poor electrode contact) is detected, suspending the output and issuing an alarm.

[0106] Specifically, the selection of core components for this subsystem fully considers the characteristics of each component and the system requirements: The OPA2188AIDR high-precision operational amplifier ensures accurate signal conditioning; The FCX605TA Darlington transistor provides primary power drive capability; The CXT5401 is used to construct the Wilson current mirror and H-bridge switching circuit; The OPA2607IDR high-speed operational amplifier enables accurate acquisition of high-frequency signals.

[0107] Each component plays a specific role in the system according to its characteristics, and together they ensure the waveform accuracy, output stability and real-time monitoring reliability of tSCS technology under the conditions of 10kHz high frequency and 200mA high current output.

[0108] Specifically, the details of tremor signal monitoring and corresponding electrical stimulation parameter control in this system are explained: The system incorporates a tremor amplitude analysis algorithm, which is used to quantify tremor intensity. Its core process is as follows: (1) Data collection: Sensor configuration: Triaxial accelerometer; Sampling settings: Continuously collect hand acceleration data at a sampling rate of 100Hz (60-second recording period); Data dimensions: Simultaneously collect acceleration values ​​(Ax, Ay, Az) in three orthogonal directions: X, Y, and Z. (2) Data preprocessing: Signal selection: Prioritize Z-axis acceleration data (aligned with the direction of gravity) for flutter amplitude analysis; Standardization: The recorded tremor amplitudes were normalized using z-scores, and standardized using the mean and standard deviation of the baseline period (without stimulation). Time segmentation: The 60-second stimulus period was divided into the first half (1-30 seconds) and the second half (31-60 seconds) for separate analysis to assess the time effect; (3) Amplitude calculation and statistical analysis: Amplitude quantization: Calculate the absolute value or root mean square value of the acceleration signal as an indicator of the flutter amplitude; Relative change calculation: Compare the tremor amplitude under each stimulus condition with the baseline value and calculate the percentage change: (stimulation period amplitude - baseline amplitude) / baseline amplitude × 100%; Statistical comparison: A linear mixed model was used to analyze the significance of differences between different stimulus conditions.

[0109] The system incorporates a tremor frequency analysis algorithm to identify individualized tremor characteristic frequencies, providing parameter basis for frequency-specific stimuli. (1) Data preprocessing: Signal synthesis: synthesizing triaxial acceleration data into a motion intensity scalar. A QM Its formula is:

[0110] Filtering: High-pass filtering is used to eliminate low-frequency motion artifacts while retaining the 4-12Hz vibration characteristic frequency band; (2) Frequency feature extraction: Segmented analysis: The continuous signal is segmented into 1-second time intervals (1 second epoch). Spectral analysis: Perform Fast Fourier Transform (FFT) and Power Spectral Density (PSD) analysis on each data segment; Peak detection: Identify the frequency corresponding to the peak power spectrum in the range of 4-12Hz as the flutter frequency for that period; (3) Frequency tracking and statistics: Dynamic tracking: Continuously estimating changes in tremor frequency throughout the stimulation process; z-score standardization: Normalizes the estimated frequencies to facilitate comparisons across conditions; Trend analysis: Analyze the changing trend of tremor frequency under different stimulation conditions; 3. Stimulus-tremor synchronicity analysis algorithm (assessing the synchronicity between stimulus and tremor): (1) Phase difference calculation: Calculate the instantaneous phase difference between the acceleration signal and the stimulus modulation signal; Phase information is extracted using Hilbert transform or analytical signal methods; (2) Calculation of Phase Lock Value (PLV): The consistency of phase difference throughout the entire stimulation period is statistically analyzed; The closer the PLV value is to 1, the stronger the phase synchronization. (3) Significance test: Set a statistical threshold (e.g., baseline PLV mean + 3 times the standard deviation). Identify responder subgroups with significant phase lock.

[0111] Specifically, the implementation of the system's built-in adaptive optimization algorithm will be explained in detail: The core of this algorithm lies in converting the tremor intensity signal monitored in real time by the IMU into dynamic adjustment instructions for the stimulation parameters, thus forming a closed-loop control.

[0112] I. Overall Algorithm Framework: The adaptive optimization algorithm is based on a closed-loop control architecture of "sensing-analysis-decision-execution": Input layer: Real-time triaxial acceleration data acquired by the IMU (100Hz sampling rate); Processing layer: Tremor intensity quantification and feature extraction; Control layer: generates stimulus parameter adjustment instructions based on rule-based control strategies; Output layer: tSCS stimulation parameters are dynamically adjusted.

[0113] II. Real-time Quantization Algorithm for Tremor Intensity: First, the system needs to convert the raw IMU data into a quantifiable tremor intensity index: 1. Signal preprocessing Triaxial acceleration synthesis: Calculate the magnitude of the synthesis vector A QM : High-pass filtering: A tenth-order Butterworth filter (>3Hz) is used to eliminate autonomous motion components; Signal segmentation: Segmented according to a 1-second time window (the 60-second recording period is consistent).

[0114] 2. Tremor Intensity Calculation RMS value calculation: Calculate the root mean square value for N filtered signals within each time window; Normalization: z-score normalization was used, and the data were standardized using the RMS mean and standard deviation at baseline (without stimulation). Relative change calculation: Convert to percentage change for easier and more intuitive understanding of tremor intensity changes: III. Stimulus Parameter Adaptive Adjustment Strategy: Based on real-time tremor intensity, the system employs a dual control strategy to adjust stimulation parameters: 1. Current intensity adjustment rules (0-200mA range): Baseline establishment: Set the initial current intensity to a safe starting value (30-50mA). Incremental adjustment: A proportional control strategy is adopted, with an adjustment step of 1mA; When Δtremor > +15% (tremor intensifies): the current intensity increases by 1 mA; when 20% ≤ Δtremor ≤ +15% (tremor stabilized): Maintain current intensity; When Δtremor < 20% (tremor suppression): Current intensity reduced by 1mA; Boundary protection: Current intensity is limited to a safe range of 0-200mA.

[0115] 2. Pulse width adjustment rules (range 10-2000μs) Frequency adaptation: Pulse width PW is inversely correlated with jitter frequency f_t.

[0116] Intensity compensation: When the current intensity reaches the upper limit but the chatter suppression is insufficient, increase the pulse width (maximum 2000μs). Energy optimization: Prioritize adjusting the current intensity while ensuring therapeutic efficacy, with pulse width as an auxiliary adjustment parameter.

[0117] Specifically, a detailed description of the essential tremor (ET) solution based on IMU and tSCS in this system, including its working process, is provided below: (1) Mechanism of tSCS in solving ET: Percutaneous spinal cord stimulation (tSCS) generates a targeted electric field in spinal cord segments (C5-C6) by applying a composite electrical stimulation of a high-frequency carrier wave (10 kHz) and a low-frequency modulated wave (1-100 Hz). When the modulation frequency is synchronized with the individualized tremor frequency (f_t, 4-12 Hz), tremor conduction can be effectively suppressed. Its mechanism of action mainly includes: Spinal cord circuit modulation: By stimulating the afferent fibers at the dorsal root entrance of the spinal cord, the excitability of the central pattern generator is modulated, affecting the sensorimotor integration pathway; Neuro-oscillation interference: Using a modulated wave synchronized with the tremor frequency to interfere with the transmission of pathological oscillation signals at the spinal cord level; Synaptic plasticity regulation: Promotes the reorganization of neural circuits by inducing long-term inhibition (LTD)-like effects through regular electrical stimulation.

[0118] (2) The stimulation sites, stimulation parameters, and rationale for tSCS in addressing ET: Stimulation target: C5-C6 segment of the cervical spine; Cathode electrode: precisely placed between the C5 and C6 spinous processes, targeting the dorsal root entrance region of the spinal cord; Anode electrodes: Arranged in the bilateral clavicle regions to form a focused electric field; Stimulation parameters: Carrier frequency: 10kHz high-frequency sine wave (to ensure deep penetration of the stimulus). Modulation frequency: 1-100Hz adjustable, synchronized with real-time jitter frequency f_t; Current intensity: 0-200mA dynamic range (adaptive adjustment based on vibration amplitude); Pulse width: 10-2000μs configurable (to match different nerve fiber recruitment needs); Reasons for parameter settings: 10kHz carrier wave: Utilizing tissue filtering characteristics, deep stimulation can be achieved while avoiding overactivation of superficial nerves; Frequency synchronization: Real-time matching with individual tremor frequency to optimize the effect of neural oscillation interference; Dynamic current: Automatically adjusted based on tremor intensity feedback from the IMU, balancing therapeutic efficacy and safety; Bidirectional pulse: Charge balance design avoids tissue polarization damage; (3) The core role of IMU in closed-loop control: Tremor signal monitoring and processing: Real-time acquisition: Continuously acquires triaxial acceleration data (Ax, Ay, Az) at a sampling rate of 100Hz; Signal preprocessing: Calculate the amplitude of the synthesized vector, and use a 10th-order Butterworth high-pass filter (>3Hz) to eliminate autonomous motion interference; Feature extraction: The dominant tremor frequency f_t was identified in the range of 4-12 Hz by FFT and power spectral density analysis; Closed-loop control logic: Parameter adaptation: The tSCS modulation frequency is dynamically adjusted based on the real-time jitter frequency f_t; Intensity optimization: Adjust the output current in 1mA steps (range 0-200mA) according to the change in vibration amplitude. Safety monitoring: Real-time impedance detection (0-30kΩ range), automatically stops output in case of abnormality.

[0119] (4) Detailed description of system workflow: This system is a wearable tremor suppression system based on IMU and tSCS, achieving precise tremor control through a closed-loop control of real-time monitoring, analysis, and stimulation. The specific working process is as follows: A. System initialization phase: After the device is started, it performs a hardware self-test and electrode-skin contact impedance detection (range 0-30kΩ). If the impedance value is >10kΩ, the system will alarm via LCD and pause output; after the detection is successful, the IMU will start continuously monitoring the hand movement status at a sampling rate of 100Hz.

[0120] B. Tremor Feature Extraction Stage: The acceleration data acquired by the IMU undergoes real-time signal processing; Calculate the quadratic mean of triaxial accelerations; High-pass filtering (>3Hz) eliminates autonomous motion components; The power spectrum of the 4-12Hz frequency band is extracted by FFT transformation after segmentation into 2.5-second time windows. Identify the dominant peak frequency as the individualized tremor frequency f_t; C. Stimulus parameter generation and output stage: Based on the real-time identified f_t value, the system automatically generates tSCS stimulation parameters; The modulation frequency is set to be synchronized with f_t (1-100Hz adaptive adjustment). The initial current intensity is set to a safe starting value (30-50mA). The analog voltage signal is output through the DAC and converted into a current signal by the voltage-controlled constant current source. After being amplified by the Wilson current mirror, a bipolar stimulation waveform is generated through the H-bridge circuit; D. Closed-loop adaptive control stage: After the stimulus is initiated, the system enters a real-time regulation cycle; The IMU continuously monitors the amplitude of the tremor (RMS value of the acceleration signal). When the vibration intensity increases by more than 15%, the output current is increased in 1mA increments. Once tremor suppression is achieved (amplitude reduced to below 50% of baseline), gradually reduce the intensity of stimulation; The vibration frequency f_t is recalculated every 10 seconds to ensure parameter synchronization. Real-time impedance monitoring; automatically pauses and alarms when abnormalities occur (>5kΩ); E. Termination Conditions: Effective suppression: Tremor intensity remains below 20% of baseline for 30 consecutive seconds; User intervention: Manually press the pause / stop button; Safety protection: Abnormal situations such as impedance sudden change and temperature exceeding the limit are detected.

[0121] (5) Technological advantages and innovations: Precise targeting: Based on spinal cord segment stimulation, avoiding the risks of intracranial intervention, precise control is achieved through electric field focusing; Personalized adaptation: Real-time IMU monitoring ensures that stimulation parameters are dynamically matched with the patient's tremor characteristics; Safety closed loop: Multiple safety mechanisms (impedance monitoring, overcurrent protection) ensure safe use; Portable design: Integrated hardware architecture, supporting long-term daily use; This system achieves complete closed-loop control from tremor recognition to suppression through the close integration of real-time IMU sensing and precise tSCS intervention, providing an innovative solution for ET management.

[0122] Optionally, the sensor used in this system to monitor the user's tremor characteristics can also be implemented using electromyography (EMG). Specifically, it records the electrical activity signals of the muscles in the hand or forearm through surface electrodes. Tremors trigger rhythmic discharges in the muscles, and the tremor frequency f_t can be extracted by analyzing the frequency components of the EMG signal.

[0123] Example 2 Please see Figure 2 , Figure 2 This is a schematic diagram of a spinal cord electrical stimulation control system based on tremor detection, as disclosed in an embodiment of the present invention. Figure 2 The described tremor-detection-based spinal cord stimulation control system can be applied to data processing systems / data processing devices / data processing servers (including local processing servers or cloud processing servers). For example... Figure 2 As shown, the spinal cord electrical stimulation control system based on tremor detection may include: The acquisition module 201 is used to acquire the user's motion sensing data.

[0124] The extraction module 202 is used to extract the user's tremor signal features based on motion sensing data and a preset feature extraction algorithm.

[0125] The determination module 203 is used to determine the electrical stimulation parameters corresponding to the tremor signal characteristics according to the preset electrical stimulation parameter determination rules.

[0126] The control module 204 is used to generate electrical stimulation control commands corresponding to the electrical stimulation device set in the user's spinal cord region based on the electrical stimulation parameters.

[0127] Optionally, electrical stimulation control commands are used to instruct the current output of the electrical stimulation device.

[0128] As can be seen, the above-described embodiments of the invention acquire the user's motion sensing data to extract the user's tremor signal characteristics, and determine the corresponding electrical stimulation parameters based on the tremor signal characteristics to control the electrical stimulation device to output current to the user's spinal cord area for stimulation. This enables precise neuro-intervention control by combining tremor monitoring and spinal cord electrical stimulation, improves the therapeutic and inhibitory effect of electrical stimulation physiotherapy on the user's tremor, and provides the user with more efficient and precise neuromodulation services.

[0129] As an optional embodiment, motion sensing data is acquired through motion sensors located in the user's hand area.

[0130] As can be seen, the above optional embodiments limit the motion sensing data to the sensing characteristics of the user's hand area, so as to accurately characterize the user's hand tremor, and help to achieve precise neuro-intervention control by combining tremor monitoring and spinal cord electrical stimulation, thereby improving the therapeutic and inhibitory effect of electrical stimulation physiotherapy on the user's tremor.

[0131] As an optional embodiment, the motion sensing data is acceleration data or electrical activity data; the acceleration data includes acceleration in multiple directional dimensions.

[0132] As can be seen, the above optional embodiments limit the data content of motion sensing data to accurately characterize the user's tremor-related status, and help to achieve precise neuro-intervention control by combining tremor monitoring and spinal cord electrical stimulation, thereby improving the therapeutic and inhibitory effect of electrical stimulation physiotherapy on the user's tremor.

[0133] As an optional embodiment, the extraction module extracts the user's tremor signal features based on motion sensing data and a preset feature extraction algorithm in the following specific ways: Calculate the squared mean of accelerations in multiple directional dimensions to obtain the composite acceleration parameters; Based on the synthetic acceleration parameters, the user's tremor signal characteristics are determined; the tremor signal characteristics include at least one of the tremor frequency and tremor intensity variation parameters.

[0134] As can be seen, through the above optional embodiments, by calculating the square mean of acceleration signals in multiple directional dimensions to obtain synthetic acceleration parameters, and then further determining tremor signal characteristics based on the synthetic acceleration parameters, it is possible to achieve synthetic analysis of velocity characteristics in multiple directional dimensions, so as to accurately characterize the user's tremor condition and assist in achieving more precise electrical stimulation therapy in the future.

[0135] As an optional embodiment, the extraction module determines the specific method by which it determines the user's tremor signal characteristics based on the synthesized acceleration parameters, including: The synthesized acceleration parameters are subjected to high-pass filtering to obtain the processed signal; Fast Fourier Transform and power spectral density analysis are performed on the processed signal within the preset time window to identify the power spectral peak and obtain the user's tremor frequency; And / or, The root mean square value of the processed signal within a preset time window is calculated to obtain the flutter characterization value; Determine the user's baseline tremor data; Based on baseline tremor data and tremor characterization values, determine the tremor intensity variation parameters for the user.

[0136] As can be seen, through the above optional embodiments, by high-pass filtering and analysis of power spectrum peaks or by analysis based on the user's baseline data, the tremor signal characteristics such as the user's tremor frequency and tremor intensity variation parameters can be determined, so as to comprehensively and accurately characterize the user's real-time or periodic tremor, and assist in achieving more precise electrical stimulation therapy.

[0137] As an optional embodiment, the electrical stimulation parameters include at least one of frequency parameters, pulse width parameters, and current intensity parameters.

[0138] As can be seen, the above optional embodiments define the parameters of the electrical stimulation to comprehensively control the operation of the electrical stimulation device, assist in achieving precise neuro-intervention control by combining tremor monitoring and spinal cord electrical stimulation, and improve the therapeutic and inhibitory effect of electrical stimulation on the user's tremor.

[0139] As an optional embodiment, the frequency parameter is the same as the tremor frequency; The pulse width parameter is inversely proportional to the tremor frequency; The current intensity parameter is determined by the following conditions: When the vibration intensity change parameter is the vibration increase by a first preset ratio, the current intensity parameter is the current current intensity increased by a first preset parameter value; When the vibration intensity change parameter is the vibration reduction by a second preset ratio, the current intensity parameter is the current current intensity reduced by a second preset parameter value; When the vibration intensity change parameter is within the preset stable ratio range, the current intensity parameter remains unchanged.

[0140] As can be seen, the above optional embodiments define the correspondence between different electrical stimulation parameters and tremor signal characteristics, so as to obtain more targeted electrical stimulation parameters based on tremor signal characteristics, realize precise neuro-intervention control by combining tremor monitoring and spinal cord electrical stimulation, and improve the therapeutic and inhibitory effect of electrical stimulation physiotherapy on the user's tremor.

[0141] As an optional embodiment, the system is also used to perform the following steps: Real-time determination of whether the characteristics of the tremor signal obtained from the most recent preset number of calculations all meet the preset tremor reduction numerical rules; If so, send a stop command to the electrical stimulation device.

[0142] As can be seen, through the above optional embodiments, by stopping electrical stimulation when it is determined that the characteristics of the most recent tremor signals all meet the preset tremor reduction numerical rules, the system can automatically stop working after the tremor is suppressed, thereby achieving more intelligent and automated control of electrical stimulation therapy.

[0143] Example 3 Please see Figure 3 , Figure 3 This is another spinal cord electrical stimulation control system based on tremor detection disclosed in the embodiments of the present invention. Figure 3 The described tremor-detection-based spinal cord stimulation control system is applied in a data processing system / data processing equipment / data processing server (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 3 As shown, the spinal cord electrical stimulation control system based on tremor detection may include: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the spinal cord electrical stimulation control method based on tremor detection described in Embodiment 1.

[0144] Example 4 This invention discloses a computer read storage medium that stores a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps of the spinal cord electrical stimulation control method based on tremor detection described in Embodiment 1.

[0145] Example 5 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the spinal cord electrical stimulation control method based on tremor detection described in Embodiment 1.

[0146] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0147] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0148] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0149] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0150] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0151] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0152] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0153] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0154] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0155] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0156] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0157] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0158] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0159] Finally, it should be noted that the spinal cord electrical stimulation control method and system based on tremor detection disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A spinal cord electrical stimulation control method based on tremor detection, characterized in that, The method includes: Acquire user motion sensor data; Based on the motion sensing data and the preset feature extraction algorithm, the user's tremor signal features are extracted; Based on the preset electrical stimulation parameter determination rules, the electrical stimulation parameters corresponding to the tremor signal characteristics are determined; Based on the electrical stimulation parameters, an electrical stimulation control command is generated corresponding to the electrical stimulation device located in the user's spinal cord region; the electrical stimulation control command is used to indicate the current output of the electrical stimulation device.

2. The spinal cord electrical stimulation control method based on tremor detection according to claim 1, characterized in that, The motion sensing data is obtained through a motion sensor located in the user's hand area.

3. The spinal cord electrical stimulation control method based on tremor detection according to claim 1, characterized in that, The motion sensing data is acceleration data or electrical activity data; the acceleration data includes acceleration in multiple directional dimensions.

4. The spinal cord electrical stimulation control method based on tremor detection according to claim 3, characterized in that, The step of extracting the user's tremor signal features based on the motion sensing data and a preset feature extraction algorithm includes: Calculate the square mean of the accelerations in the multiple directional dimensions to obtain the composite acceleration parameters; Based on the synthesized acceleration parameters, the user's tremor signal characteristics are determined; the tremor signal characteristics include at least one of tremor frequency and tremor intensity variation parameters.

5. The spinal cord electrical stimulation control method based on tremor detection according to claim 4, characterized in that, Determining the user's tremor signal characteristics based on the synthesized acceleration parameters includes: The synthesized acceleration parameters are subjected to high-pass filtering to obtain the processed signal; The processed signal within the preset time window is subjected to Fast Fourier Transform and Power Spectral Density Analysis to identify the power spectral peak and obtain the user's tremor frequency. And / or, The root mean square value of the processed signal within a preset time window is calculated to obtain the flutter characterization value; Determine the baseline tremor data corresponding to the user; Based on the baseline tremor data and the tremor characterization values, the tremor intensity variation parameters of the user are determined.

6. The spinal cord electrical stimulation control method based on tremor detection according to claim 4, characterized in that, The electrical stimulation parameters include at least one of frequency parameters, pulse width parameters, and current intensity parameters.

7. The spinal cord electrical stimulation control method based on tremor detection according to claim 6, characterized in that, The frequency parameter is the same as the vibration frequency; The pulse width parameter is inversely proportional to the tremor frequency; The current intensity parameter is determined by the following conditions: When the vibration intensity change parameter is a vibration increase of a first preset ratio, the current intensity parameter is the current current intensity increased by a first preset parameter value; When the vibration intensity change parameter is a second preset ratio for vibration reduction, the current intensity parameter is a parameter value for the current current intensity reduced by a second preset value. When the vibration intensity change parameter is within a preset stable ratio range, the current intensity parameter remains unchanged.

8. The spinal cord electrical stimulation control method based on tremor detection according to claim 1, characterized in that, The method further includes: Real-time determination of whether the tremor signal features obtained from the most recent preset number of calculations all meet the preset tremor mitigation numerical rules; If so, a stop command is sent to the electrical stimulation device.

9. A spinal cord electrical stimulation control system based on tremor detection, characterized in that, The system includes: The acquisition module is used to acquire the user's motion sensor data; The extraction module is used to extract the user's tremor signal features based on the motion sensing data and a preset feature extraction algorithm. The determination module is used to determine the electrical stimulation parameters corresponding to the tremor signal characteristics according to the preset electrical stimulation parameter determination rules; The control module is used to generate electrical stimulation control commands corresponding to the electrical stimulation device set in the user's spinal cord region based on the electrical stimulation parameters; the electrical stimulation control commands are used to indicate the current output of the electrical stimulation device.

10. A spinal cord electrical stimulation control system based on tremor detection, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the spinal cord electrical stimulation control method based on tremor detection as described in any one of claims 1-8.

Images