A muscle group state monitoring device and analysis system based on a flexible string bead sensor

Abstract

The application discloses a kind of muscle group state monitoring devices and analysis systems based on flexible string bead type sensor, it is related to human biomechanics monitoring technical field, specifically a kind of muscle group state monitoring devices and analysis systems based on flexible string bead type sensor, the system includes intelligent string bead, its inner end is equipped with flexible sensor, wireless communication module and data processing unit, outer end is equipped with color change module.Flexible sensor is distributed multi-channel sensing unit, can synchronously collect muscle group surface mechanics signal.Data processing unit can carry out band-pass filtering and other multiple processing to acquisition signal, quantize muscle group contraction force distribution uneven degree, calculate average activation level, assess power mode stability and identify dynamic conversion process.Wireless communication module synchronizes monitoring parameter to external terminal, and color change module changes color according to muscle group activation level.Intelligent string bead shell can be bent and have breathable adhesive layer on surface, can monitor muscle group state in real time in daily wearing.

Description

Technical Field

[0001] This invention relates to the field of human biomechanical monitoring technology, specifically to a muscle group status monitoring device and analysis system based on a flexible beaded sensor. Background Technology

[0002] In many fields such as sports science, rehabilitation medicine, and ergonomics, objective and precise quantitative monitoring of muscle activity is a crucial foundational task. Whether professional athletes are using scientific training to improve performance and prevent injuries, rehabilitation patients are using physical therapy to restore muscle function, or researchers are exploring the fundamental laws of human movement, an effective means to reflect the working state of muscle groups in real time and in situ is needed. When muscle groups are working, their physiological manifestations mainly include the contraction and deformation of the muscle belly, changes in tension, and complex mechanical effects produced when they work in conjunction with the skeleton. These changes in internal state are ultimately transmitted to the skin surface, forming mechanical signals that can be indirectly perceived.

[0003] For a long time, methods for assessing muscle activity have been mainly divided into two categories: invasive and non-invasive. Invasive methods, such as needle electromyography, can provide high spatiotemporal resolution information on neuromuscular electrical activity, but they are invasive procedures that not only cause pain and infection risks to the subjects, but are also not convenient to use in daily life or sports scenarios. Furthermore, their measurement results are easily affected by factors such as subcutaneous fat thickness, making it difficult to directly reflect the overall mechanical work status of the muscles. Therefore, this method is mainly limited to clinical diagnosis and laboratory research and lacks universal monitoring value.

[0004] Currently, the mainstream non-invasive technology is represented by surface electromyography (sEMG). This technology collects electrophysiological signals of muscles by attaching electrodes to the skin surface, and is widely used due to its non-invasiveness and relative convenience. However, surface electromyography signals are essentially an indirect reflection of neural control commands, and the relationship between them and the actual force generated by the muscles or the mechanical work performed is not a simple linear one. It is highly susceptible to various factors such as electrode placement, skin impedance, muscle fatigue level, and individual physiological differences, leading to challenges in data stability and repeatability. In addition, sEMG devices are usually structurally fixed, consisting of multiple separate electrode pads and a main unit. When monitoring large-scale muscle groups or complex joint movements, the wiring is cumbersome, uncomfortable, and difficult to adapt to the curved shapes of different parts of the body, limiting its application in dynamic, long-term monitoring scenarios.

[0005] Besides electrophysiological signals, some studies have attempted to use pressure sensors or strain gauges to measure pressure changes on contact surfaces during muscle contraction. While these methods can more directly correlate mechanical output, there is a significant mismatch between the physical form of existing rigid or semi-rigid sensors and the soft, deformable surface tissues. When attached to curved surfaces such as limb joints, the sensors themselves generate unnecessary constraints and artifacts, severely interfering with the authenticity of the original mechanical signals and failing to faithfully record muscle deformation and force exertion in their natural state. Furthermore, these devices are mostly discrete in layout, lacking the ability to systematically capture the continuous spatial state of muscle groups; they can only acquire information from discrete points, making it difficult to depict the coordinated working state of muscle groups as a whole.

[0006] In summary, there is currently a lack of monitoring devices that can closely conform to the curves of the human body, not hinder natural movement, and continuously cover the spatial range of the target muscle group. An ideal solution would be flexibly distributed across the limb surface like beads on a string, using its inherent sensing elements to convert changes in the mechanical state of the muscle group in three-dimensional space into an analyzable data stream. This would bypass the complex mapping relationships of electrical signals and directly provide stable, intuitive, and objective evidence for assessing muscle function. Summary of the Invention

[0007] The purpose of this invention is to provide a muscle group state monitoring device and analysis system based on a flexible beaded sensor. The flexible beaded sensor is closely attached to the body surface to capture multi-dimensional biomechanical information during muscle activity. Built-in units perform real-time signal processing and analysis to objectively present the states of muscle exertion, contraction balance, and dynamic transitions, providing direct evidence for body function assessment and motion perception.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solution: a muscle group state monitoring device and analysis system based on a flexible beaded sensor, comprising a smart bead, wherein a flexible sensor is provided at the inner end of the smart bead, a wireless communication module is provided at the inner end of the smart bead, and a data processing unit is provided at the inner end of the smart bead.

[0009] Furthermore, the smart beads are arranged in a shape that conforms to the contours of the limbs. The flexible sensors integrated inside can undergo elastic deformation with the movement of the muscle groups, capturing the multidimensional mechanical changes generated during muscle activity in real time. The wireless communication module is responsible for stably transmitting the information acquired by the sensors, while the data processing unit performs preliminary processing on the incoming information. The three work together to enable the device to closely conform to the muscle groups and follow the limbs' dynamics, continuously and coherently collecting raw information reflecting the state of the muscle groups, providing basic data support for subsequent analysis of muscle activity characteristics.

[0010] Furthermore, the flexible sensor employs a distributed multi-channel sensing unit, with each unit dispersed along the limb axis at different spatial locations, enabling simultaneous sensing of multiple mechanical responses on the muscle surface along its length. This multi-location synchronous acquisition method covers different regional responses during muscle contraction, avoiding the omission of local force differences due to single-point measurement. This allows the acquired mechanical signals to more completely represent the force distribution characteristics of the muscle group along the limb extension direction, providing multi-dimensional evidence for analyzing the spatial consistency of the overall muscle group activity.

[0011] Furthermore, the data processing unit is configured to perform bandpass filtering on the raw signal sequence acquired by the multi-channel sensing unit. By setting filtering parameters that are adapted to the frequency range of physiological muscle activity, the baseline shift caused by limb displacement or changes in contact pressure in the low-frequency band is precisely reduced, while suppressing the fine noise caused by external touch or electromagnetic disturbance in the high-frequency band. The signal after this processing can more clearly retain the core mechanical fluctuation characteristics of muscle contraction and relaxation, reduce the interference of irrelevant factors on subsequent analysis, and make the signal more consistent with the mechanical performance of actual muscle activity.

[0012] Furthermore, the data processing unit measures the uneven distribution of muscle contraction force in the circumferential direction of the limb by calculating the instantaneous amplitude ratio of the signals collected by two adjacent smart beads. When the ratio deviates from the preset equilibrium range, it can reflect that the force exerted by a certain side of the muscle group is relatively stronger or weaker. This calculation method based on the comparison of adjacent points can keenly capture the spatial location and degree of the difference in circumferential force exertion, help locate the possible local force imbalance in muscle group activities, and provide detailed reference for analyzing muscle group coordination patterns.

[0013] Furthermore, the data processing unit divides the filtered signal sequence into equal-length time windows and calculates the arithmetic mean of the output values ​​of all sensing units within each window, which is used as the average activation level of the muscle group corresponding to that window. The equal-length window division ensures the comparability of data from different time periods, and the arithmetic mean can integrate signal information from multiple locations within the window, presenting an overview of the overall force exertion intensity of the muscle group during that time period. This facilitates the observation of the rising and falling trends and fluctuation patterns of the muscle group activation level over time by observing the fluctuations of the average values ​​of different windows.

[0014] Furthermore, the data processing unit defines a signal complexity index C to evaluate the stability of muscle group exertion patterns, and its calculation formula is as follows:

[0015] in, The index represents the probability of the occurrence of the i-th standardized signal pattern within a unit of time, and n is the total number of signal patterns within that unit of time. This formula reflects the complex changes in the force exertion pattern by statistically analyzing the diversity of signal patterns. If the index is high, it indicates that the force exertion pattern switches frequently and the pattern is unstable. If the index is low, it indicates that the force exertion pattern is relatively simple and the pattern is more stable. This formula can help judge the consistency and controllability of muscle group force exertion.

[0016] Furthermore, the data processing unit identifies the dynamic transition process of muscle groups from resting to working or from working to relaxing by performing differential operations on the average activation level of continuous time windows. The differential operation can amplify the subtle changes in the average activation level between adjacent windows. When the change exceeds the set start threshold, it is determined to be a transition to working state, and when it is below the stop threshold, it is determined to be a return to relaxing state. This identification method based on numerical changes can accurately capture the starting moment of muscle group state switching and clearly delineate the boundary between resting and working.

[0017] Furthermore, the wireless communication module adopts the Bluetooth Low Energy protocol, and the protocol parameters are optimized according to the data transmission volume and battery life requirements. The monitoring parameters generated by the data processing unit are synchronized to the external terminal device at preset time intervals. The preset time interval can be dynamically adjusted according to the monitoring scenario. For example, the interval can be extended to reduce power consumption when maintaining a static posture, and shortened to improve data timeliness during dynamic exercise training, ensuring that the external terminal can obtain multi-dimensional analysis results of muscle group status in real time or near real time.

[0018] Furthermore, the color-changing module includes at least three independent light-emitting sub-units, each sub-unit corresponding to a preset muscle group activation level threshold. The light-emitting color and brightness of each sub-unit change independently depending on whether the corresponding threshold is met. When the average activation level of the muscle group reaches the threshold corresponding to a certain sub-unit, the sub-unit triggers the corresponding color to be constantly lit or flashing. Through multi-color combinations, the different levels of muscle activation, such as mild, moderate or high activation, can be intuitively distinguished, so as to realize the external prompt of the intensity of muscle group exertion without having to look at the terminal.

[0019] Furthermore, the outer shell of the smart beads is made of a flexible polymer material that can be bent. The bending stiffness of the material is adapted to the deformation range of the natural curvature of the limb, ensuring that the beads will not cause hard obstruction or misalignment of internal units when the limb joints are flexed and extended. The outer shell surface is provided with a breathable adhesive layer for fixing to the limb surface. The adhesive layer is made of hypoallergenic medical-grade adhesive, which has both moderate stickiness and easy peeling. It can maintain the fixing effect during long-term wear and avoid skin discomfort. At the same time, the breathable structure reduces the accumulation of sweat to maintain stable contact between the sensing unit and the skin.

[0020] This invention provides a muscle group state monitoring device and analysis system based on a flexible beaded sensor, which has the following beneficial effects: 1. The distributed multi-channel flexible sensor design enables simultaneous acquisition of mechanical signals from multiple spatial points along the limb axis on the muscle surface, overcoming the limitations of traditional single-point monitoring. It can accurately capture the force differences at different locations during muscle contraction. For example, in cases of muscle strain or localized fatigue, abnormal fluctuations in signals at specific points can be observed, allowing for early localization of the problem area. Furthermore, it can reconstruct the spatial distribution characteristics of muscle force exertion, providing a direct basis for adjusting force exertion patterns during rehabilitation training, avoiding compensatory injuries, and improving the spatial resolution and practicality of monitoring.

[0021] 2. The data processing unit effectively separates low-frequency baseline drift from high-frequency environmental noise through bandpass filtering, preserving the true mechanical signal characteristics of muscle groups and solving the problem of traditional monitoring being easily interfered with by skin friction and equipment shaking. By combining the instantaneous amplitude ratio of adjacent beaded signals, the uneven distribution of muscle contractile force in the limb circumferential direction is quantified. For example, in hemiplegic patients with circumferential muscle group imbalance, this indicator can directly reflect the degree of imbalance, providing objective data for rehabilitation program development, avoiding subjective judgment errors, and improving monitoring reliability.

[0022] 3. Time window segmentation, average activation level calculation, and signal complexity index C assessment provide a dual-dimensional analysis of muscle group status, considering both static intensity and dynamic patterns. Average activation level reflects changes in muscle group exertion intensity at different training stages, while the complexity index C reflects the stability of exertion patterns—high C values ​​for beginners with chaotic exertion patterns, stabilizing as they become more proficient. This multidimensional analysis helps athletes optimize movement continuity or guides patients to correct abnormal exertion habits, advancing from "measuring numerical values" to "analyzing mechanisms," thus enhancing the targeted nature of training / rehabilitation.

[0023] 4. Differential calculation of the average activation level over a continuous time window can automatically identify the dynamic transition nodes of muscle groups from resting to working to relaxing. For example, during exercise, it can accurately capture the transition of muscles from activation to peak and then to relaxation, avoiding overexertion or insufficient warm-up; during rehabilitation, it can monitor the patient's ability to voluntarily control muscle activation, and if the transition is delayed, it can indicate weak neuromuscular control, requiring targeted training. This function replaces manual observation, enabling real-time and objective judgment of state transitions, improving ease of use and safety.

[0024] 5. Low-power Bluetooth synchronizes parameters to external terminals at preset intervals, balancing battery life and real-time performance. Users can view historical data trends via their mobile phones. The color-changing module has at least three independent light-emitting sub-units corresponding to different activation level thresholds, allowing users to easily assess muscle load during exercise without constantly staring at the screen – a green light indicates appropriate intensity, while a red light indicates overload requiring adjustment. The flexible polymer shell combined with a breathable adhesive layer conforms to the body without irritating the skin, ensuring comfort during extended wear, lowering the barrier to entry, and making it suitable for daily training and long-term rehabilitation scenarios. Attached Figure Description

[0025] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of the overall structure of the present invention; Figure 2 This is a schematic diagram of the internal structure of the intelligent beaded bracelet of the present invention; Figure 3 This is a flowchart of the muscle group status monitoring data acquisition and transmission process of the present invention; Figure 4 This is a flowchart illustrating the core signal processing and analysis of the present invention. Figure 5 This is a flowchart of the advanced analysis and visualization output of this invention.

[0027] Part Name: 1. Smart beads; 2. Flexible sensor; 3. Wireless communication module; 4. Data processing unit; 5. Color-changing module. Detailed Implementation

[0028] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses consistent with some aspects of this disclosure as detailed in the appended claims.

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0030] How to use: 1. Installation and fixing of smart beads: Take smart beads 1 and arrange them sequentially along the axis of the limb where the target muscle group is located. Fix them to the surface of the limb using the breathable adhesive layer on the surface of the outer shell, ensuring that each smart bead 1 is in close contact with the surface of the muscle group. Because the outer shell is made of a flexible polymer material that can be bent, it can adjust its shape according to the bending of the limb, avoiding falling off or affecting the movement during exercise (corresponding to claim 10).

[0031] 2. Signal Acquisition Start-up of Flexible Sensor: After the device is turned on, the flexible sensor 2 enters the working state. This sensor is a distributed multi-channel sensing unit that will synchronously acquire mechanical signals at multiple spatial points distributed along the limb axis on the surface of the muscle group, covering the force information at different locations of the muscle group (corresponding to claim 2).

[0032] 3. Filtering of the original signal: The data processing unit 4 performs bandpass filtering on the original signal sequence collected by the flexible sensor 2, effectively filtering out the interference components of low-frequency baseline drift and high-frequency environmental noise, and retaining the effective signal that reflects the true mechanical characteristics of the muscle group (corresponding to claim 3).

[0033] 4. Quantification of circumferential distribution imbalance: The data processing unit 4 further calculates the instantaneous amplitude ratio of the signals collected by two adjacent smart beads 1. The magnitude of this ratio is used to intuitively quantify the degree of uneven distribution of muscle contraction force in the circumferential direction of the limb, and to help determine whether the muscle force is coordinated (corresponding to claim 4).

[0034] 5. Calculation of average muscle activation level: The filtered signal sequence is divided into time windows of equal length. The data processing unit 4 calculates the arithmetic mean of the output values ​​of all sensing units in each time window. The result is the average muscle activation level of that window, which reflects the overall force exertion intensity of the muscle group (corresponding to claim 5).

[0035] 6. Stability assessment of force exertion pattern: The data processing unit 4 defines a signal complexity index C to assess the stability of the muscle group force exertion pattern. The calculation formula is based on the probability of occurrence of the i-th standardized signal morphology per unit time and the total number of signal morphologies n per unit time. The lower the index, the more stable the force exertion pattern (corresponding to claim 6).

[0036] 7. Dynamic transition process identification: By performing differential calculation on the average activation level of a continuous time window, the data processing unit 4 can identify the dynamic transition process of a muscle group from a resting state to a working state or from a working state to a relaxed state, and clarify the time node of the state switch (corresponding to claim 7).

[0037] 8. Wireless synchronization of monitoring parameters: The wireless communication module 3 adopts the Bluetooth Low Energy protocol to synchronize the monitoring parameters (including imbalance, average activation level, signal complexity index and state transition nodes, etc.) generated by the data processing unit 4 to the external terminal device at preset time intervals, so as to facilitate real-time viewing and analysis (corresponding to claim 8).

[0038] 9. Visual feedback on muscle activation level: The color-changing module 5 contains at least three independent light-emitting sub-units, each corresponding to a preset muscle activation level threshold. When the average activation level of the muscle group reaches a certain threshold, the corresponding light-emitting sub-unit lights up, visually indicating the current muscle activation status through color change (corresponding to claim 9).

[0039] Example: Example 1: Monitoring of Isokinetic Contraction Training of the Biceps Brachii in the Upper Limb The target muscle group is the biceps brachii of the upper limb. The user takes the smart bead 1 and arranges five beads in sequence along the axial direction of the biceps brachii muscle belly on the front side of the upper arm. The breathable adhesive layer on the surface of the shell is used to fix it to the skin. The flexible polymer material of the shell can be bent and naturally fits with the slight bending of the upper arm to avoid displacement during training.

[0040] After the device is turned on, the flexible sensor 2 uses a distributed multi-channel sensing unit to simultaneously collect mechanical signals from multiple spatial points on the surface of the biceps brachii along the upper arm axis, covering different positions such as the proximal and distal ends of the muscle belly under force.

[0041] Data processing unit 4 performs bandpass filtering on the original signal sequence to filter out low-frequency baseline drift and high-frequency environmental noise, while retaining the true mechanical characteristics that reflect muscle contraction.

[0042] The data processing unit 4 calculates the instantaneous amplitude ratio of the signals collected by two adjacent smart beads 1, quantifies the degree of uneven distribution of biceps brachii contraction force in the upper arm circumferential direction, and helps to determine whether the force is concentrated in the main area of ​​the muscle belly.

[0043] The filtered signal is divided into equal-length time windows. The data processing unit 4 calculates the arithmetic mean of the output values ​​of all sensing units in each window to obtain the average activation level of the muscle groups in that window, which reflects the force intensity at each stage of training.

[0044] Data processing unit 4 defines a signal complexity index C to evaluate the stability of the power generation mode. It is calculated based on the probability of the occurrence of the i-th standardized signal form per unit time and the total number of signal forms n. A lower index indicates a more stable power generation mode.

[0045] Differential calculations are performed on the average activation level of continuous time windows to identify the dynamic transition process of the biceps brachii from resting to contraction or from contraction to relaxation, and to clarify the start and end points of the movement.

[0046] The wireless communication module 3 uses the Bluetooth Low Energy protocol to synchronize the monitoring parameters to the external terminal at preset time intervals, allowing users to view the imbalance, average activation level and state transition status in real time.

[0047] At least three independent light-emitting sub-units of the color-changing module 5 correspond to preset activation level thresholds. When the average activation level reaches a certain level, the corresponding sub-unit lights up to indicate the current activation status with color, helping the user to perceive the degree of exertion.

[0048] Example 2: Static Maintenance Monitoring of the Quadriceps Femoris in the Lower Limb

[0049] The target muscle group is the quadriceps femoris muscle of the lower limb. The user takes six smart beads and arranges them along the axial direction of the quadriceps femoris muscle belly on the front of the thigh. They are fixed to the skin using a breathable adhesive layer. The flexible polymer shell can be bent to adapt to the upright or slightly bent posture of the thigh and maintain a close fit.

[0050] Once the device is turned on, the flexible sensor 2 uses a distributed multi-channel sensing unit to simultaneously collect mechanical signals from multiple spatial points along the thigh axis on the surface of the quadriceps femoris muscle, covering the force difference between the middle and both ends of the muscle belly.

[0051] Data processing unit 4 performs bandpass filtering on the raw signal to remove low-frequency baseline drift and high-frequency environmental noise, and extracts the muscle tension characteristics during the static maintenance phase.

[0052] The data processing unit 4 calculates the instantaneous amplitude ratio of the signals of adjacent smart beads 1, quantifies the uneven distribution of quadriceps contraction force in the circumferential direction of the thigh, and judges whether the static force is uniform.

[0053] The filtered signal is divided into time windows of equal length. The data processing unit 4 calculates the arithmetic mean of the output values ​​of all sensing units in each window to obtain the average activation level of the muscle group, which reflects the static maintenance of force exertion stability.

[0054] Data processing unit 4 evaluates the stability of the power generation mode by using the signal complexity index C. The index is calculated based on the probability of the occurrence of the i-th standardized signal form and the total number of signal forms n per unit time. A low index indicates that the static power generation mode is consistent.

[0055] By performing differential calculations on the average activation level over a continuous time window, the dynamic transition of the quadriceps muscle from rest to static maintenance or from maintenance to relaxation is identified, and the start and end of the maintenance phase are clearly defined.

[0056] The wireless communication module 3 uses Bluetooth Low Energy to synchronize the monitoring parameters to an external terminal at preset time intervals, allowing users to observe changes in imbalance and average activation level and understand the static maintenance effect.

[0057] At least three independent light-emitting sub-units of the color-changing module 5 correspond to preset activation level thresholds. When the average activation level reaches a certain level, the corresponding sub-unit lights up to indicate the activation status when statically maintained, thus assisting in posture adjustment.

[0058] Example 3: Monitoring of isometric contractions of the erector spinae muscles in the lower back

[0059] The target muscle group is the erector spinae muscles of the lower back. The user takes seven smart beads and arranges them axially along the belly of the erector spinae muscles on both sides of the spine. They are fixed to the skin of the lower back using a breathable adhesive layer. The flexible polymer shell can be bent to conform to the curve of the muscle group as the waist flexes or extends, avoiding wrinkles.

[0060] After the device is turned on, the flexible sensor 2 uses a distributed multi-channel sensing unit to synchronously collect mechanical signals from multiple spatial points on the surface of the erector spinae muscle along the spinal axis, covering the force on different segments of the bilateral muscle belly.

[0061] Data processing unit 4 performs bandpass filtering on the original signal to remove low-frequency baseline drift and high-frequency environmental noise, while retaining the muscle tension characteristics during isometric contraction.

[0062] The data processing unit 4 calculates the instantaneous amplitude ratio of the signals of adjacent smart beads 1, quantifies the degree of uneven distribution of erector spinae muscle contraction force in the circumferential direction of the lower back, and judges whether the force exerted on both sides is balanced.

[0063] The filtered signal is divided into equal-length time windows. The data processing unit 4 calculates the arithmetic mean of the output values ​​of all sensing units in each window to obtain the average activation level of the muscle group, which reflects the intensity of isometric contraction.

[0064] Data processing unit 4 defines a signal complexity index C to evaluate the stability of the force application mode. It is calculated based on the probability of the occurrence of the i-th standardized signal form per unit time and the total number of signal forms n. A low index indicates that the force application modes on both sides are coordinated.

[0065] By performing differential calculations on the average activation level over a continuous time window, we can identify the dynamic transitions of the erector spinae muscles from resting to isometric contraction or from contraction to relaxation, thus clarifying the initiation and termination of core stability.

[0066] The wireless communication module 3 uses Bluetooth Low Energy to synchronously monitor parameters to an external terminal at preset time intervals, allowing users to view the imbalance and average activation level and evaluate the core stability effect.

[0067] At least three independent light-emitting sub-units of the color-changing module 5 correspond to preset activation level thresholds. When the average activation level reaches a certain level, the corresponding sub-unit lights up to indicate the activation status of the erector spinae muscles with color, thus helping to maintain the correct core posture.

[0068] Example 4: Dynamic force monitoring of the anterior deltoid muscle in the shoulder

[0069] The target muscle group is the anterior deltoid of the shoulder. The user takes four smart beads and arranges them along the axial direction of the anterior deltoid muscle belly in the proximal part of the upper arm. They are fixed to the skin in front of the shoulder using a breathable adhesive layer. The flexible polymer shell can be bent and conforms to the muscle group with the forward flexion movement of the shoulder joint, preventing it from loosening during exercise.

[0070] Once the device is turned on, the flexible sensor 2 uses a distributed multi-channel sensing unit to simultaneously collect mechanical signals from multiple spatial points on the surface of the anterior deltoid muscle along the upper arm axis, covering the force changes of the proximal and distal ends of the muscle belly during dynamic movements.

[0071] Data processing unit 4 performs bandpass filtering on the original signal to remove low-frequency baseline drift and high-frequency environmental noise, and extracts transient mechanical characteristics during dynamic force application.

[0072] The data processing unit 4 calculates the instantaneous amplitude ratio of adjacent smart beads 1, quantifies the degree of uneven distribution of the anterior deltoid contractile force in the upper arm circumferential direction, and determines whether the dynamic force is concentrated in the effective area of ​​the muscle belly.

[0073] The filtered signal is divided into time windows of equal length. The data processing unit 4 calculates the arithmetic mean of the output values ​​of all sensing units in each window to obtain the average activation level of the muscle group, which reflects the phased intensity of dynamic force exertion.

[0074] Data processing unit 4 evaluates the stability of the power generation mode by using the signal complexity index C, which is calculated based on the probability of the occurrence of the i-th standardized signal form and the total number of signal forms n per unit time. A low index indicates a coherent dynamic power generation mode.

[0075] By performing differential calculations on the average activation level over a continuous time window, we can identify the dynamic transitions of the anterior deltoid muscle from resting to dynamic exertion or from exertion to relaxation, and clarify the rhythmic nodes of the movement.

[0076] The wireless communication module 3 uses Bluetooth Low Energy to synchronously monitor parameters to an external terminal at preset time intervals, allowing users to observe changes in imbalance and average activation level and optimize the efficiency of dynamic action.

[0077] At least three independent light-emitting sub-units of the color-changing module 5 correspond to preset activation level thresholds. When the average activation level reaches a certain level, the corresponding sub-unit lights up to indicate the activation status during dynamic force application and assist in adjusting the amplitude of the action.

[0078] Example 5: Monitoring of Eccentric Contractions of the Calf Gastrocnemius Muscle

[0079] The target muscle group is the gastrocnemius muscle of the calf. The user takes five smart beads and arranges them along the axial direction of the gastrocnemius muscle belly on the back of the calf. They are fixed to the skin with a breathable adhesive layer. The flexible polymer shell can be bent and fits the muscle group with the plantar flexion or dorsiflexion of the ankle joint, preventing displacement when walking.

[0080] Once the device is turned on, the flexible sensor 2 uses a distributed multi-channel sensing unit to simultaneously collect mechanical signals from multiple spatial points on the surface of the gastrocnemius muscle along the calf axis, covering the force difference between the upper part of the muscle belly and the connection with the Achilles tendon.

[0081] Data processing unit 4 performs bandpass filtering on the raw signal to remove low-frequency baseline drift and high-frequency environmental noise, while retaining the muscle tension attenuation characteristics during eccentric contraction.

[0082] Data processing unit 4 calculates the instantaneous amplitude ratio of adjacent smart beads 1, quantifies the uneven distribution of gastrocnemius muscle contraction force in the circumferential direction of the lower leg, and judges whether the centrifugal control is uniform.

[0083] The filtered signal is divided into equal-length time windows. The data processing unit 4 calculates the arithmetic mean of the output values ​​of all sensing units in each window to obtain the average activation level of the muscle group, which reflects the control intensity of eccentric contraction.

[0084] Data processing unit 4 defines a signal complexity index C to evaluate the stability of the force exertion mode. It is calculated based on the probability of the occurrence of the i-th standardized signal form and the total number of signal forms n per unit time. A lower index indicates a more stable centrifugal control mode.

[0085] By performing differential calculations on the average activation level over a continuous time window, we can identify the dynamic transition of the gastrocnemius muscle from resting to eccentric contraction or from contraction to relaxation, and clarify the start and end of the buffer phase.

[0086] The wireless communication module 3 uses Bluetooth Low Energy to synchronously monitor parameters to an external terminal at preset time intervals, allowing users to view the imbalance and average activation level, thus improving the centrifugal buffering effect.

[0087] At least three independent light-emitting sub-units of the color-changing module 5 correspond to preset activation level thresholds. When the average activation level reaches a certain level, the corresponding sub-unit lights up to indicate the activation status during centrifugal contraction and assist in adjusting the landing force.

[0088] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A muscle group state monitoring device and analysis system based on a flexible beaded sensor, comprising intelligent beads (1), characterized in that: The smart bead (1) has a flexible sensor (2) at its inner end, a wireless communication module (3) at its inner end, a data processing unit (4) at its inner end, and a color-changing module (5) at its outer end.

2. The muscle group state monitoring device and analysis system based on a flexible beaded sensor according to claim 1, characterized in that: The flexible sensor (2) is a distributed multi-channel sensing unit used to synchronously collect mechanical signals at multiple spatial locations distributed along the limb axis on the surface of the muscle group.

3. The muscle group state monitoring device and analysis system based on a flexible beaded sensor according to claim 2, characterized in that: The data processing unit (4) is configured to perform bandpass filtering on the original signal sequence acquired by the multi-channel sensing unit to filter out interference components of low-frequency baseline drift and high-frequency environmental noise.

4. The muscle group state monitoring device and analysis system based on a flexible beaded sensor according to claim 3, characterized in that: The data processing unit (4) quantifies the degree of uneven distribution of muscle contraction force in the limb circumference by calculating the instantaneous amplitude ratio of the signals collected by two adjacent smart beads (1).

5. The muscle group state monitoring device and analysis system based on a flexible beaded sensor according to claim 4, characterized in that: The data processing unit (4) divides the filtered signal sequence into time windows of equal length and calculates the arithmetic mean of the output values ​​of all sensing units in each time window as the average activation level of the muscle group in that window.

6. The muscle group state monitoring device and analysis system based on a flexible beaded sensor according to claim 5, characterized in that: The data processing unit (4) defines a signal complexity index C to evaluate the stability of muscle group exertion patterns, and its calculation formula is as follows: in, The probability of the occurrence of the i-th standardized signal pattern per unit time is represented by n, where n is the total number of signal patterns per unit time.

7. The muscle group state monitoring device and analysis system based on a flexible beaded sensor according to claim 6, characterized in that: The data processing unit (4) identifies the dynamic transition process of muscle groups from resting state to working state or working state to relaxation state by performing differential calculation on the average activation level of continuous time windows.

8. A muscle group state monitoring device and analysis system based on a flexible beaded sensor according to claim 7, characterized in that: The wireless communication module (3) adopts the low-power Bluetooth protocol and synchronizes the monitoring parameters generated by the data processing unit (4) to the external terminal device at preset time intervals.

9. A muscle group state monitoring device and analysis system based on a flexible beaded sensor according to claim 8, characterized in that: The color-changing module (5) contains at least three independent light-emitting sub-units, each sub-unit corresponding to a preset muscle activation level threshold.

10. A muscle group state monitoring device and analysis system based on a flexible beaded sensor according to claim 9, characterized in that: The outer shell of the smart bead (1) is made of a flexible polymer material that can be bent, and the surface of the outer shell is provided with a breathable adhesive layer for fixing to the surface of the limb.

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