Multi-target adaptive monitoring method and system based on physiological signal feedback

Abstract

The application provides a multi-target adaptive monitoring method and system based on physiological signal feedback, relates to the technical field of diagnostic measurement, and acquires pressure sensing information corresponding to a left foot and a right foot of a target object respectively and physiological feedback signals collected by multiple monitoring targets arranged in the left foot and the right foot; a contact state corresponding to each monitoring target is determined based on the pressure sensing information, a cross-target area coupling state is determined based on coupling detection information between different monitoring targets in the same foot; a signal credibility parameter of each monitoring target is determined according to the contact state and the cross-target area coupling state, and a same-name monitoring quantity corresponding to the left foot and the right foot respectively is generated from the physiological feedback signals based on the signal credibility parameter; bilateral differential processing is performed on the same-name monitoring quantities corresponding to the left foot and the right foot, and a physiological state evaluation index of the target object is generated. The application can improve the signal credibility in the continuous wearing and local contact disturbance scene.

Description

Technical Field

[0001] This application relates to the field of diagnostic measurement technology, and more specifically, to a multi-target adaptive monitoring method and system based on physiological signal feedback. Background Technology

[0002] With the development of wearable sensing technology, foot-worn devices can collect data such as pressure, gait, temperature, blood oxygen, pulse wave, bioelectrical or impedance data, and can be used to continuously monitor the foot or physiological state of a target subject. Compared with single-point patch devices, foot-worn devices have the advantages of better continuous wearing and the ability to simultaneously cover both feet and multiple body surface areas, making them suitable for perioperative, bedridden rehabilitation, or continuous lower limb status monitoring scenarios.

[0003] However, in the process of wearable multi-target physiological monitoring of the foot, the signal quality of multiple monitoring targets does not only depend on the sensor itself, but is also affected by changes in foot weight-bearing, body position, local pressure, target contact status, and abnormal coupling between different monitoring targets. Existing solutions usually use pressure sensing information for gait or force analysis, or only judge the signal quality of a single monitoring channel. It is difficult to distinguish between real physiological changes, target contact changes, and signal differences caused by cross-target crosstalk in scenarios of continuous wear and local contact disturbance. Especially when multiple monitoring targets are set on both feet, if the comparison between the left and right feet is directly based on the original physiological feedback signals, it is easy to mistake inconsistencies in the acquisition conditions of the two feet or local crosstalk for differences in physiological state. Therefore, it is urgent to improve the signal reliability of wearable multi-target physiological monitoring of the foot and improve the comparability and anti-interference ability of the physiological state assessment results of the left and right feet. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this application provides a multi-target adaptive monitoring method and system based on physiological signal feedback.

[0005] Firstly, this application provides a multi-target adaptive monitoring method based on physiological signal feedback, including:

[0006] The pressure sensing information corresponding to the left and right feet of the target object is obtained, as well as the physiological feedback signals collected by multiple monitoring target points placed on the left and right feet respectively;

[0007] Based on the pressure sensing information, the contact state corresponding to each of the monitoring target points is determined;

[0008] Based on the coupling detection information between different monitoring target points within the same foot, the cross-target area coupling state of the corresponding foot is determined.

[0009] Based on the contact state and the cross-target coupling state, the signal reliability parameters of each monitoring target point are determined;

[0010] Based on the signal reliability parameter, corresponding monitoring quantities for the left foot and the right foot are generated from the physiological feedback signal; bilateral differential processing is performed on the corresponding monitoring quantities for the left foot and the right foot, and physiological state assessment indicators for the target object are generated based on the bilateral differential processing results.

[0011] Optionally, determining the contact state corresponding to each of the monitoring target points includes:

[0012] The pressure sensing distributions corresponding to the left and right feet are determined based on the pressure sensing information.

[0013] Based on the positional correspondence between the target monitoring point and the pressure sensing distribution, the pressure-associated region corresponding to the target monitoring point is determined;

[0014] Based on the pressure sensing information within the pressure-associated area, the contact state of the target monitoring point is determined.

[0015] Optionally, determining the cross-target region coupling state of the corresponding foot includes:

[0016] The cross-target coupling state is used to characterize the degree of abnormal conductivity or signal crosstalk between different monitoring target points within the same foot.

[0017] Determine the inter-regional response relationship between target points within the same foot region and adjacent target points;

[0018] The cross-target coupling state is determined based on the degree of deviation of the inter-regional response relationship from the baseline response relationship of the corresponding target region.

[0019] Optionally, determining the signal reliability parameters for each of the monitoring target points includes:

[0020] When the contact state of the target monitoring point meets the contact requirements and the cross-target area coupling state corresponding to the target monitoring point meets the isolation requirements, the target monitoring point is determined to be in a reliable acquisition state.

[0021] When the contact state of the target monitoring point does not meet the contact requirements, or the cross-target area coupling state corresponding to the target monitoring point does not meet the isolation requirements, the target monitoring point is determined to be in a restricted acquisition state.

[0022] The signal reliability parameter of the target monitoring point is determined based on the reliable acquisition state or the restricted acquisition state.

[0023] Optionally, the step of generating corresponding monitoring quantities for the left foot and the right foot based on the signal confidence parameter from the physiological feedback signal includes:

[0024] Based on the signal confidence parameters of the left foot target monitoring point and the signal confidence parameters of the right foot monitoring point located in the same functional area as the left foot target monitoring point, the comparability status between the corresponding monitoring points of the left and right feet is determined.

[0025] When the comparability requirements are met in the comparability state, based on the physiological feedback signals of the monitoring target points corresponding to the left and right feet, the corresponding monitoring quantities for the left and right feet are generated respectively.

[0026] Optionally, the plurality of monitoring targets includes: a first monitoring target and a second monitoring target located in different functional areas;

[0027] The corresponding monitoring quantity is the monitoring quantity generated by the monitoring target points located in the same functional area in the left and right feet.

[0028] Optionally, the first monitoring target point corresponds to the area adjacent to the nerves of the lower limb, and the second monitoring target point corresponds to the area adjacent to the acupoints of the lower limb.

[0029] The signal confidence parameters corresponding to the first monitoring target and the second monitoring target are used to generate the same monitoring quantity for the corresponding functional area.

[0030] Optionally, generating the corresponding monitoring values ​​for the left foot and the right foot includes:

[0031] According to the different functional areas, the monitoring target points in the left foot and the right foot that are located in the same functional area are formed into a target point group with the same name.

[0032] For each of the aforementioned target groups, the bilateral effective state of the target group is determined based on the signal confidence parameters of the two monitoring targets in the target group.

[0033] When the bilateral effective state meets the requirements, based on the signal confidence parameters and physiological feedback signals of the two monitoring target points in the same target point group, the same monitoring quantities of the left foot and the right foot in the corresponding functional areas are generated respectively.

[0034] The monitoring quantities with the same name corresponding to different functional areas are used for the two-sided differential processing.

[0035] Optionally, determining the cross-target coupling state of the corresponding foot includes performing the following processes on the left foot and the right foot respectively:

[0036] Based on the changes in the pressure sensing information of the corresponding foot during a continuous monitoring period, the pressure disturbance characteristics are determined;

[0037] Within the time period corresponding to the pressure disturbance characteristics, determine the inter-regional response relationship between the first monitoring target and the second monitoring target;

[0038] Based on the follow-up characteristics of the inter-regional response relationship relative to the pressure disturbance characteristics, the target area coupling characteristics of the corresponding foot are determined;

[0039] Based on the target area coupling characteristics of the left foot and the right foot and the differences between them, the cross-target area coupling state of the left foot and the right foot is determined respectively.

[0040] The cross-target coupling state is used to characterize the abnormal conduction state or signal crosstalk state between the lower limb nerve adjacent area and the lower limb acupoint adjacent area as pressure sensitivity changes.

[0041] Secondly, this application provides a multi-target adaptive monitoring system based on physiological signal feedback, including:

[0042] The acquisition module is used to acquire pressure sensing information corresponding to the left and right feet of the target object, as well as physiological feedback signals collected by multiple monitoring target points placed on the left and right feet; based on the pressure sensing information, the contact state corresponding to each monitoring target point is determined;

[0043] The first processing module is used to determine the cross-target area coupling state of the corresponding foot based on the coupling detection information between different monitoring target points within the same foot.

[0044] The second processing module is used to determine the signal reliability parameters of each of the monitoring target points based on the contact state and the cross-target coupling state.

[0045] The generation module is used to generate corresponding monitoring quantities for the left foot and the right foot respectively from the physiological feedback signal based on the signal confidence parameter; perform bilateral differential processing on the corresponding monitoring quantities for the left foot and the right foot, and generate physiological state assessment indicators for the target object based on the bilateral differential processing results.

[0046] Compared with the prior art, this application obtains the pressure sensing information of the left and right feet of the target object and the physiological feedback signals collected from multiple monitoring target points. Based on the pressure sensing information, it determines the contact state of each monitoring target point and determines the cross-target area coupling state based on the coupling detection information between different monitoring target points in the same foot. This allows the signal reliability parameters of each monitoring target point to simultaneously reflect the local contact reliability and the inter-regional interference.

[0047] Based on this, this application generates corresponding monitoring quantities for the left and right feet based on signal reliability parameters, and then performs bilateral differential processing on the corresponding monitoring quantities. This can reduce the impact of poor contact, local pressure changes, abnormal conduction across target areas, and inconsistent acquisition conditions between the left and right feet on the evaluation results, thereby improving the signal reliability of wearable multi-target physiological monitoring of the feet in continuous wearing and local contact disturbance scenarios, and improving the comparability and anti-interference ability of the physiological state evaluation indicators of the left and right feet. Attached Figure Description

[0048] Figure 1 A flowchart of a multi-target adaptive monitoring method based on physiological signal feedback provided in this application embodiment;

[0049] Figure 2 A flowchart illustrating a method for determining the contact state corresponding to each monitoring target point, as provided in this application embodiment;

[0050] Figure 3 A flowchart illustrating a method for determining the cross-target region coupling state of a corresponding foot, provided in an embodiment of this application;

[0051] Figure 4 This is a schematic diagram of a multi-target adaptive monitoring system based on physiological signal feedback provided in an embodiment of this application. Detailed Implementation

[0052] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0053] See Figure 1 The diagram shows a flowchart of a multi-target adaptive monitoring method based on physiological signal feedback provided in an embodiment of this application, including steps S101 to S105, wherein:

[0054] S101: Obtain pressure sensing information corresponding to the left and right feet of the target object, as well as physiological feedback signals collected by multiple monitoring target points placed on the left and right feet respectively;

[0055] S102: Based on the pressure sensing information, determine the contact state corresponding to each of the monitoring target points;

[0056] S103: Based on the coupling detection information between different monitoring target points within the same foot, determine the cross-target area coupling state of the corresponding foot.

[0057] S104: Determine the signal reliability parameters of each monitoring target point based on the contact state and the cross-target coupling state;

[0058] S105: Based on the signal confidence parameter, generate corresponding monitoring quantities for the left foot and the right foot from the physiological feedback signal; perform bilateral differential processing on the corresponding monitoring quantities for the left foot and the right foot, and generate physiological state assessment indicators for the target object based on the bilateral differential processing results.

[0059] Regarding the above S101:

[0060] The target subject can be any human being requiring continuous monitoring of their lower limb physiological status, such as those in the perioperative period, bed rest recovery period, or postoperative rehabilitation observation period. The left and right feet refer to the anatomically defined left and right feet of the target subject, respectively. Multiple monitoring targets are data acquisition locations set on corresponding areas of the left and right feet. These targets can be formed by electrode contact areas, optical acquisition areas, pressure sensing areas, impedance detection areas, or combinations thereof. To facilitate comparison between the left and right feet, at least one set of monitoring targets with corresponding positions or functions can be set on each foot; for example, monitoring targets A1 and A2 can be set on the left foot, and corresponding monitoring targets B1 and B2 can be set on the right foot.

[0061] In this application, pressure sensing information is used to characterize the pressure state, compression state, or adhesion force state at the monitoring target point or in an associated area corresponding to the monitoring target point. The pressure sensing information can be collected by a pressure sensing unit located in the sole area, ankle area, lower leg area, or area adjacent to the monitoring target point. Sole weight-bearing information is one implementation of the pressure sensing information; in this implementation, the pressure sensing unit is located in the sole area to collect the weight-bearing or pressure distribution in the sole area. In another implementation, the pressure sensing unit is located in the area adjacent to the monitoring target point to collect local compression or adhesion force information of the skin-contact area where the monitoring target point is located.

[0062] In embodiments where the pressure sensing unit is located in the sole area, sole weight-bearing information and physiological feedback signals can be collected by a wearable acquisition component. This wearable acquisition component can be a sock-type support, a stocking-like support, an ankle support, a sole-attached support, or an ankle-wrap support. Taking a sock-type support as an example, it can include a weight-bearing detection unit located in the sole area, and physiological signal acquisition units located in the dorsum of the foot, ankle, lower leg, or other predetermined body surface areas. The weight-bearing detection unit can include a flexible pressure sensor, a piezoresistive sensor, a capacitive pressure sensor, or a thin-film pressure sensor; the physiological signal acquisition unit can include a photoelectric sensor, an electrode contact area, or an impedance detection electrode.

[0063] For example, each foot is equipped with a sock-like support component. Each sock-like support component has eight load-bearing sampling units on its sole area. These eight units correspond to the medial heel, lateral heel, medial arch, lateral arch, medial forefoot, lateral forefoot, big toe area, and little toe area, respectively. The data output by the load-bearing sampling units can form the corresponding sole load information. The output of each load-bearing sampling unit can be a pressure value, a load value, or a normalized load value.

[0064] For example, the load-bearing sampling unit can output an analog-to-digital conversion value of 0 to 4095. The control unit can convert the analog-to-digital conversion value into a pressure value or a normalized load-bearing value according to the calibration relationship. In implementations that do not require an absolute pressure value, the normalized load-bearing value can also be used directly for subsequent processing.

[0065] For example, A1 and B1 are located in the same functional area, and A2 and B2 are located in the same functional area. Each monitoring target point can output at least one physiological feedback signal. The physiological feedback signal can be an optical pulse wave signal reflecting local perfusion changes, an electrical response signal reflecting the state of the electrode-skin interface or local tissue response, or other acquired signals that can characterize the physiological state of the body surface at the corresponding monitoring target point.

[0066] For example, A1 and B1 can acquire optical pulse wave signals from the first functional region, while A2 and B2 can acquire electrical response signals from the second functional region; they can also acquire optical pulse wave signals and electrical response signals simultaneously at the same monitoring target point.

[0067] Regarding the acquisition parameters, different sampling frequencies can be set according to the rate of change of different signals. For example, the sampling frequency for foot bearing information can be set to 20Hz–100Hz, such as 50Hz or 60Hz; the sampling frequency for optical pulse wave signals can be set to 50Hz–200Hz, such as 100Hz; and the electrical response signal can be sampled continuously or periodically according to the detection task, for example, executing a detection window every 1 to 5 seconds, with each detection window lasting 100ms–500ms. The above sampling frequency and window length are only used to illustrate one possible implementation. In other embodiments, adjustments can be made according to sensor performance, monitoring scenario, power consumption requirements, and the state of the target object.

[0068] In addition, the control unit can configure foot identifiers, monitoring target identifiers, functional area identifiers, and acquisition time identifiers for each set of acquired data. Foot identifiers are used to distinguish between the left and right feet; monitoring target identifiers are used to distinguish between different monitoring target points within the same foot; functional area identifiers are used to identify corresponding functional areas in the left and right feet; and acquisition time identifiers are used to characterize the acquisition time or acquisition window to which the corresponding data belongs. Acquired data can be represented as: foot identifiers, monitoring target identifiers, functional area identifiers, acquisition time identifiers, physiological feedback signal segments, and foot plantar weight-bearing information corresponding to the acquisition time identifier.

[0069] For example, foot weight-bearing information and physiological feedback signals can be correlated on a window-by-window basis. The length of the window can be set from 0.5 seconds to 5 seconds, such as 1 second or 2 seconds; adjacent windows can be arranged consecutively or with a 50% overlap. If the sampling frequencies of the foot weight-bearing information and the physiological feedback signal are different, the control unit can map them to the same window based on the acquisition time identifier.

[0070] For example, after the target wears two sock-like support components (left and right), the control unit acquires the load-bearing data from eight load-bearing sampling units on the left and right soles, as well as physiological feedback signal segments corresponding to monitoring target points A1, A2, B1, and B2 within the k-th acquisition window. The control unit organizes the above data into bipedal multi-target point acquisition data within the same time window. A1 and B1 have the same functional area identifier, and A2 and B2 have the same functional area identifier.

[0071] In addition, the control unit can collect a reference data segment when the target object is unloaded or at rest to determine the zero-point output of the load-bearing sampling unit; it can also bind the left foot device and the right foot device as the left foot acquisition unit and the right foot acquisition unit respectively through the device number or connection port.

[0072] In embodiments where the pressure sensing unit is located in the vicinity of the monitoring target, a thin-film pressure sensor, a piezoresistive flexible sensor, a capacitive pressure sensor, or a flexible strain sensor can be installed in the circumferential or edge region of the first or second monitoring target, or within the skin-supporting layer. This pressure sensing unit is used to collect data on the localized adhesion pressure, the compression state of the sock, or the force changes in the skin-supporting layer at the corresponding monitoring target.

[0073] For example, when the first monitoring target A1 is located in the area adjacent to the nerves of the lower limb on the lateral side of the calf, a ring-shaped or sheet-shaped pressure sensing unit can be set around the electrode contact area of ​​A1; when the second monitoring target A2 is located in the area adjacent to acupoints on the lower limb, a strip-shaped flexible pressure sensing unit can be set near the skin-contact area of ​​A2. The control unit can acquire the local pressure sensing information corresponding to A1 and A2 within the same acquisition window and use it as input to determine the contact state of A1 and A2.

[0074] In this embodiment, the pressure-associated area corresponding to the target monitoring point can be directly the local pressure-sensing area adjacent to the target monitoring point, rather than being limited to the sole of the foot. If the local pressure sensing value in the area adjacent to the target monitoring point is within a preset contact range and the pressure fluctuation does not show any short-term abrupt change, it can be determined that the target monitoring point is in a reliable contact state; if the local pressure sensing value is too low, too high, or the short-term fluctuation is abnormal, it can be determined that the target monitoring point is in a weak contact state or an abnormal contact state.

[0075] Regarding S102 above:

[0076] The contact state is used to characterize the reliability of the adhesion between the monitoring target and the surface of the target object. The contact state is not limited to a binary result of physical contact or non-contact, and may also include state quantities used to represent contact stability, pressure adhesion degree, or risk of false contact.

[0077] For example, the contact state can be represented as "trustworthy contact state," "weak contact state," or "abnormal contact state," or it can be represented as a contact score between 0 and 1. The specific representation of the contact state can be determined based on the calculation method of the subsequent signal confidence parameters.

[0078] The pressure-associated area can be either the plantar pressure area or a localized pressure area adjacent to the monitoring target. When the pressure sensing unit is located in the plantar region, the pressure-associated area can include weight-bearing sampling areas such as the outer heel, outer arch, and outer forefoot. The control unit can configure a pressure association table for each monitoring target. This table records the monitoring target identifier, the associated pressure sensing unit identifier, the associated area type, and the association weight of each pressure sensing unit. The associated area type can include plantar associated areas and target-adjacent associated areas. For plantar associated areas, the pressure sensing unit can be a plantar weight-bearing sampling unit; for target-adjacent associated areas, the pressure sensing unit can be a localized pressure sensing unit located circumferentially around the monitoring target or in the skin-supporting layer.

[0079] In one implementation, the control unit can first determine the plantar weight distribution for the left and right feet based on the plantar weight information obtained in step S101. The plantar weight distribution can be a set of weight values ​​from multiple plantar weight sampling units within the same acquisition window.

[0080] For example, if eight load-bearing sampling units are set in the sole area of ​​the left foot sock-type load-bearing component, then the load distribution of the left foot sole within the k-th sampling window can be represented as the load-bearing data set corresponding to these eight load-bearing sampling units; the same applies to the right foot. This load-bearing data set can be represented by pressure values, load values, analog-to-digital conversion values, or normalized load-bearing values.

[0081] To establish a link between the weight-bearing distribution on the sole of the foot and the monitoring target points, a positional correspondence between the monitoring target points and the weight-bearing areas on the sole of the foot can be pre-established. This positional correspondence can be determined during equipment factory calibration, wearing initialization, or software configuration.

[0082] For example, a monitoring target point located on the lateral side of the ankle or the lower calf can be correlated with one or more weight-bearing sampling areas among the lateral heel, lateral arch, and lateral forefoot; a monitoring target point located on the medial side of the ankle or the lower calf can be correlated with one or more weight-bearing sampling areas among the medial heel, medial arch, and medial forefoot. These correlations are used to determine the weight-bearing associated areas of the target monitoring point.

[0083] For example, a first monitoring target point A1 and a second monitoring target point A2 are set on the left foot. If A1 is located in the functional area corresponding to the lateral side of the ankle, the three weight-bearing sampling areas of the left foot—the lateral side of the heel, the lateral side of the arch, and the lateral side of the forefoot—can be determined as the weight-bearing associated areas of A1. If A2 is located on the medial side of the ankle or near acupoints on the lower limb, the three weight-bearing sampling areas of the left foot—the medial side of the heel, the medial side of the arch, and the medial side of the forefoot—can be determined as the weight-bearing associated areas of A2. B1 and B2 in the right foot can be determined as corresponding weight-bearing associated areas according to the same area configuration method. The above-mentioned weight-bearing associated areas can be a set of plantar sampling units, or a virtual area based on the weighted synthesis of plantar sampling units.

[0084] After determining the load-bearing associated area of ​​the target monitoring point, the contact state of the target monitoring point can be determined based on the plantar load-bearing information within this area. As one possible implementation, the average load-bearing value, maximum load-bearing value, load-bearing variation range, or load-bearing stability index of the load-bearing associated area corresponding to the target monitoring point can be calculated within each acquisition window, and the contact state can be determined accordingly.

[0085] For example, within a 1-second acquisition window, if the load-bearing associated area of ​​a target monitoring point includes 3 load-bearing sampling units, and each load-bearing sampling unit samples at 50Hz, then 150 load-bearing sampling values ​​can be obtained within this acquisition window. The control unit can calculate the average load-bearing value and the magnitude of change based on these 150 load-bearing sampling values, and convert them into the contact state of the target monitoring point.

[0086] For example, if the normalized average load-bearing value within the load-bearing associated area corresponding to the target monitoring point is greater than or equal to 0.20, and the load variation within this area is no greater than 0.30, then the target monitoring point can be determined to be in a reliable contact state. If the normalized average load-bearing value is between 0.05 and 0.20, or the load variation is greater than 0.30, then the target monitoring point can be determined to be in a weak contact state. If the normalized average load-bearing value is less than 0.05, then the target monitoring point can be determined to be in an abnormal contact state. In actual implementation, adjustments can be made based on sensor calibration data, the tightness of the sock-like support, the weight range of the target object, and the location of the monitoring point.

[0087] In another implementation, the contact status can also be represented by a contact score. The control unit can normalize the load-bearing value within the load-bearing associated area corresponding to the target monitoring point into a load-bearing score between 0 and 1, and combine this score with the stability of the load-bearing associated area within the current acquisition window to obtain the contact score. For example, the higher the load-bearing score and the smaller the fluctuation, the higher the contact score; if the load-bearing score is too low or the fluctuation is too large, the contact score decreases. The contact score can be used as one of the inputs for subsequently determining signal reliability parameters.

[0088] It should be noted that foot weight-bearing information does not require the target subject to be standing or walking. In perioperative, bedridden, or rehabilitation observation scenarios, the target subject may be supine, lateral, passively raised, with their foot resting against the bed surface, or with foot supported. In these situations, foot weight-bearing information can still characterize the relative pressure between the foot and the bed surface, support, or wearable data acquisition device. The control unit can use this relative pressure state to determine whether the monitoring target point may have insufficient contact, excessive local pressure, or short-term loose contact.

[0089] For monitoring target points located near the ankle, lower leg, nerves, or acupoints in the lower limbs, plantar weight-bearing information can serve as proxy information reflecting foot posture and the stress state of the wearable components. In integrated sock-like or stocking-like support components, changes in pressure on the sole area cause changes in foot posture, ankle angle, elastic tension path of the sock, and local skin-contact pressure. These changes can be transmitted to the skin-contact area of ​​the ankle or lower leg, thus affecting the fit of the corresponding monitoring target point. Therefore, the control unit can infer the contact state related to the target monitoring point through the plantar weight-bearing distribution and weight-bearing associated areas.

[0090] For monitoring targets that are far from the sole of the foot or have a low correlation with sole weight-bearing, the control unit can also use sole weight-bearing information as the basic input for contact state judgment, and verify it in conjunction with local auxiliary information. The local auxiliary information may include at least one of the following: electrode-skin interface response of the corresponding monitoring target, stability of the DC component of the optical signal, local noise level, local strain of the wearable component, or contact state of the connection end. For example, when the weight-bearing associated area shows stable foot pressure, but the optical DC component of the target monitoring point experiences a sudden change or the electrode-skin interface response is abnormal, the control unit can determine that the target monitoring point is in a weak contact state or an abnormal contact state. In this way, sole weight-bearing information does not replace local contact detection in isolation, but is used together with the wearable component structure and local auxiliary information to determine the contact state of the monitoring target.

[0091] Regarding the above S103:

[0092] The cross-target coupling state is used to characterize the degree of abnormal conductivity or signal crosstalk between different monitoring target points within the same foot. This state focuses not on whether a single monitoring target point can acquire a signal, but on whether there is unwanted inter-regional coupling between different monitoring target points.

[0093] For example, in sock-type or stocking-type data acquisition components, sweat accumulation, conductive gel diffusion, localized wetting, localized pressure changes, or proximity of electrode contact areas may all cause abnormal conductive paths to form between two monitoring targets that should be independent of each other, resulting in the mixing of signal components from one monitoring target into the physiological feedback signal of the other monitoring target.

[0094] Coupling detection information can be data used to characterize the mutual influence between different monitoring target points within the same foot. This coupling detection information can originate from response data of inter-regional detection excitation, or from correlation change data between physiological feedback signals of multiple monitoring target points. To avoid limiting coupling detection to a specific circuit configuration, the coupling detection information in this application may include, but is not limited to, inter-regional response relationships, inter-regional signal synchronization change relationships, inter-regional impedance response relationships, or inter-regional detection excitation response relationships. All of these different forms of coupling detection information can be used to determine whether abnormal conduction or signal crosstalk exists between different monitoring target points.

[0095] In one possible implementation, the control unit can acquire coupled detection information using an inter-regional detection excitation method. For example, within a detection window not used for treatment, the control unit can apply a low-amplitude detection excitation signal between a target monitoring point within the same foot and adjacent monitoring points, and acquire the corresponding response signal. The detection excitation signal can be an AC detection signal below the somatosensory threshold or a short-duration pulse detection signal.

[0096] For example, the current amplitude of the detection excitation signal can be set to 10μA–200μA, the frequency can be set to 1kHz–20kHz, and the single detection window length can be set to 50ms–300ms. These can be adjusted during implementation based on the electrode area, safety limitations of the acquisition circuit, and the wearing status of the target object.

[0097] For example, the control unit can execute an inter-regional detection window between A1 and A2 and obtain the inter-regional response relationship between A1 and A2. This inter-regional response relationship can be represented as response amplitude, response phase, response waveform attenuation characteristics, or an inter-regional response vector obtained from multi-frequency point detection.

[0098] For example, the control unit can acquire the response amplitude and phase between A1 and A2 at three frequency points: 2kHz, 5kHz, and 10kHz, and combine them into an inter-regional response vector. The corresponding B1 and B2 in the right foot can also obtain the inter-regional response relationship in the same way.

[0099] To determine whether the current response relationship between regions is abnormal, a baseline response relationship can be set for the corresponding target area. The baseline response relationship can be derived from the initial wearing phase, the stable contact phase, historical normal monitoring windows, or equipment calibration data.

[0100] For example, when the target object has just put on the acquisition component and the foot weight information indicates that the contact state of the corresponding target point is stable, the control unit can acquire the inter-regional response relationship between A1 and A2 and use it as the baseline response relationship for the current monitoring cycle; it can also calculate the average or median value of the inter-regional response relationship within multiple stable acquisition windows and use it as the baseline response relationship.

[0101] The control unit can determine the cross-target coupling state based on the degree of deviation of the current inter-region response relationship relative to the corresponding target area reference response relationship. For example, if the amplitude change of the current inter-region response vector relative to the reference response vector increases significantly, or if the response phase changes of multiple frequency points show a consistent shift, it indicates that there may be abnormal conduction or signal crosstalk between the target monitoring point and adjacent monitoring points. The degree of deviation can be represented by amplitude changes, phase changes, response vector distance, or differences in response curve shape.

[0102] For example, the normalized distance between the current inter-region response vector and the reference response vector can be used as the coupling deviation, and the cross-target region coupling state can be determined based on the coupling deviation. The specific calculation method of the normalized distance can be Euclidean distance, cosine distance, or weighted difference, and this application does not limit it in this way.

[0103] In another implementation, the control unit can also utilize the correlation changes in the physiological feedback signals themselves to obtain coupling detection information. For example, within the same foot, if the physiological feedback signals of two functional areas exhibit a common drift highly consistent with the plantar weight-bearing disturbance within a short period of time, and this common drift does not conform to the baseline response relationship between the two in the stable acquisition window, then a high risk of cross-target coupling can be determined between the two monitoring target points. This method does not require additional detection stimulation, but instead uses the naturally occurring plantar weight-bearing changes during continuous monitoring as a reference disturbance source. By observing whether the response relationship between regions changes with the plantar weight-bearing disturbance, the true physiological correlation can be distinguished from coupling changes caused by contact interfaces or abnormal conduction paths.

[0104] For example, the control unit detects a change in the load-bearing information of the left foot sole within the k-th acquisition window, forming a pressure disturbance feature. Simultaneously, the inter-regional response relationship between the first monitoring target point A1 and the second monitoring target point A2 also changes synchronously within the same or adjacent acquisition windows. If this synchronous change is consistent with the pressure disturbance feature within multiple consecutive windows, while no similar change occurs in the target point group corresponding to the right foot, the control unit can determine that the cross-target coupling state between the left foot A1 and A2 is more abnormal than that of the right foot.

[0105] When determining the cross-target coupling state, it can be represented as a discrete state or a continuous score. For example, the cross-target coupling state can be represented as "isolation normal state", "coupling risk state", or "abnormal coupling state"; it can also be represented as a coupling score between 0 and 1, where the higher the coupling score, the higher the possibility of abnormal conduction or signal crosstalk between different monitoring targets.

[0106] For example, if the normalized coupling deviation is less than 0.20, it can be determined as a normal isolation state; if the normalized coupling deviation is between 0.20 and 0.50, it can be determined as a coupling risk state; if the normalized coupling deviation is greater than 0.50, it can be determined as an abnormal coupling state. The above values ​​are only examples, and in actual implementation, they can be adaptively set based on device calibration, clinical sample data, or historical normal windows.

[0107] Regarding S104 above:

[0108] The signal reliability parameter characterizes the usability of a physiological feedback signal acquired at a specific monitoring target point when generating corresponding monitoring quantities. This parameter is not simply the signal amplitude or the sensor's operating state, but rather a comprehensive reflection of the contact state of the monitoring target point and the cross-target coupling state within the foot where the target point is located. In other words, even if a monitoring target point can acquire a physiological feedback signal with a large amplitude, if its contact state is unstable, or if there is abnormal conduction or signal crosstalk between the monitoring target point and other monitoring targets, the physiological feedback signal corresponding to that monitoring target point can still be determined to have low reliability.

[0109] In one implementation, the control unit can map the contact state obtained in step S102 and the cross-target coupling state obtained in step S103 to the acquisition state of the monitoring target, and then determine the signal reliability parameter based on the acquisition state. The acquisition state may include a reliable acquisition state and a restricted acquisition state.

[0110] In another embodiment, the acquisition status may further include abnormal acquisition status, in order to mark monitoring targets with severe loose connections, disconnections, abnormal conduction, or significant signal crosstalk. The above status division is only an example; in actual implementation, a continuous scoring method can also be used to represent the signal reliability of the monitoring target.

[0111] The contact requirement indicates that the monitoring target point and the surface of the target object are in a close fit suitable for acquiring physiological feedback signals. The isolation requirement indicates that there is no abnormal conduction or signal crosstalk between the monitoring target point and other monitoring targets within the same foot that would affect the use of the current signal. The contact and isolation requirements can be determined through calibration parameters, historical stability window data, or device configuration files.

[0112] For example, when the contact state of a monitoring target is a reliable contact state, or its contact score is not less than 0.60, the monitoring target can be considered to meet the contact requirements; when the cross-target coupling state corresponding to the monitoring target is a normal isolation state, or its coupling score is not greater than 0.30, the monitoring target can be considered to meet the isolation requirements. The above 0.60 and 0.30 are only exemplary values, and can be adjusted according to sensor sensitivity, monitoring target location, tightness of the wearer, and historical calibration data in specific implementations.

[0113] For example, the contact state can include a trusted contact state, a weak contact state, and an abnormal contact state; the cross-target area coupling state can include an isolated normal state, a coupling risk state, and an abnormal coupling state. The control unit can determine the acquisition state of the monitoring target point according to the following rules: when the target monitoring target point is in a trusted contact state and the corresponding cross-target area coupling state is an isolated normal state, the target monitoring target point is determined to be in a trusted acquisition state; when the target monitoring target point is in a weak contact state, or the corresponding cross-target area coupling state is a coupling risk state, the target monitoring target point is determined to be in a restricted acquisition state; when the target monitoring target point is in an abnormal contact state, or the corresponding cross-target area coupling state is an abnormal coupling state, the target monitoring target point is determined to be in an abnormal acquisition state.

[0114] After determining the acquisition status, the control unit can determine the signal reliability parameters based on the acquisition status. As one possible implementation, the signal reliability parameters may include a channel activation identifier and a reliability score. The channel activation identifier indicates whether the physiological feedback signal of the corresponding monitoring target is allowed to enter the subsequent generation process of the same monitoring quantity; the reliability score indicates the degree of reliability of the physiological feedback signal in subsequent processing.

[0115] For example, the credibility score corresponding to the trusted acquisition state can be set to 0.80 to 1.00, the credibility score corresponding to the restricted acquisition state can be set to 0.30 to 0.80, the credibility score corresponding to the abnormal acquisition state can be set to 0 to 0.30, or the channel enable flag corresponding to the abnormal acquisition state can be set to disabled.

[0116] In another implementation, the signal reliability parameter may not directly use a fixed scoring range, but can be obtained jointly from the contact score and the coupling score. Specifically, the control unit can convert the contact state into a contact score, the cross-target area coupling state into a coupling risk score, and determine the reliability score based on the contact score and the coupling risk score. A higher contact score indicates a higher degree of adhesion reliability for the monitoring target; a higher coupling risk score indicates a higher likelihood that the monitoring target is affected by inter-regional abnormal conduction or signal crosstalk.

[0117] For example, the control unit can input the contact score and coupling risk score into a pre-configured two-dimensional mapping table and output the corresponding confidence score. This two-dimensional mapping table can be obtained through device calibration, historical normal data, or manual configuration.

[0118] For example, the two-dimensional mapping table can be configured as follows: when the contact score is between 0.80 and 1.00 and the coupling risk score is between 0 and 0.20, the confidence score is 0.90; when the contact score is between 0.50 and 0.80 and the coupling risk score is between 0.20 and 0.50, the confidence score is 0.55; when the contact score is below 0.50 or the coupling risk score is above 0.50, the confidence score is 0.20 or the channel is marked as restricted. In implementation, the mapping table can be configured according to the structure, signal type, wearing method, and target object characteristics of different monitoring targets.

[0119] In another implementation, the signal reliability parameter can be a data structure containing multiple fields. For example, the signal reliability parameter for target monitoring point A1 within the k-th acquisition window may include: foot identifier, monitoring target identifier, functional area identifier, acquisition window identifier, contact state field, cross-target coupling state field, acquisition state field, reliability score field, and channel activation identifier field. Using this data structure, subsequent steps can clearly determine whether a specific physiological feedback signal can be used to generate the same monitoring quantity, and the reliability of that physiological feedback signal within the corresponding functional area.

[0120] For example, if the contact state corresponding to the left foot monitoring target A1 is a reliable contact state, and the cross-target area coupling state between A1 and adjacent monitoring targets within the same foot is a normal isolation state, then the control unit determines that A1 is in a reliable acquisition state and sets the signal reliability parameter of A1 to the enabled state with a reliability score of 0.90. If the contact state corresponding to the left foot monitoring target A2 is a reliable contact state, but the cross-target area coupling state between A2 and adjacent monitoring targets is a coupling risk state, then the control unit determines that A2 is in a restricted acquisition state and sets the reliability score of A2 to 0.50. If the contact state corresponding to the right foot monitoring target B2 is an abnormal contact state, then even if B2 itself acquires a physiological feedback signal of a certain amplitude, the control unit can still mark B2 as a restricted acquisition state or an abnormal acquisition state.

[0121] It should be noted that the determination of signal reliability parameters does not require the use of an artificial intelligence model. For implementations with limited data and relatively fixed device configurations, rule tables, mapping tables, or state machines can be used. If a machine learning model is used in other implementations, the model input should be limited to data relevant to the scenario of this application, such as contact scores, coupling risk scores, physiological feedback signal quality characteristics, and acquisition window identifiers. The model output should be the reliability score or acquisition status of the monitoring target. The model can employ a lightweight decision tree, logistic regression model, or small multilayer perceptron. Taking a decision tree as an example, training samples can be derived from reliable acquisition windows, restricted acquisition windows, and abnormal acquisition windows labeled in the wearing calibration experiment. Input features include contact scores, coupling risk scores, and physiological feedback signal stability characteristics, and the output category is reliable acquisition status, restricted acquisition status, or abnormal acquisition status. This machine learning method is only an optional embodiment and is not a necessary condition for implementing the technical solution of this application.

[0122] Regarding the above S105:

[0123] Among them, the corresponding monitoring quantities refer to the monitoring quantities located in the same functional area in the left and right feet and used to characterize the same physiological meaning. For example, if the first monitoring target point A1 in the left foot and the first monitoring target point B1 in the right foot both correspond to the first functional area, then the local perfusion characterization quantities obtained from the physiological feedback signals of A1 and B1 can constitute a set of corresponding monitoring quantities; if the second monitoring target point A2 in the left foot and the second monitoring target point B2 in the right foot both correspond to the second functional area, then the local electrical response characterization quantities obtained from the physiological feedback signals of A2 and B2 can constitute another set of corresponding monitoring quantities.

[0124] The term "same name" here does not require that the signal values ​​of the left and right feet be the same, but rather that their source regions, signal types, or physiological meanings have a corresponding relationship, which can be used for subsequent bilateral comparisons.

[0125] In one implementation, the control unit can group monitoring target points located in the same functional area of ​​the left and right feet into corresponding target point groups according to functional area identifiers. Each corresponding target point group includes at least one monitoring target point in the left foot and one corresponding monitoring target point in the right foot. For each corresponding target point group, the control unit can read the physiological feedback signal and corresponding signal reliability parameter of each monitoring target point in the corresponding target point group within the current acquisition window, and determine the bilateral effective state of the corresponding target point group based on the signal reliability parameter. The bilateral effective state is used to characterize whether the left and right foot signals of the corresponding target point group are comparable.

[0126] For example, if the signal confidence score of the left foot target monitoring point A1 is 0.86, and the signal confidence score of the right foot corresponding monitoring point B1 is 0.82, and the channel activation flags of A1 and B1 are both enabled, then the control unit can determine that the corresponding target point group of A1 and B1 meets the bilateral validity requirement. If the signal confidence score of A1 is 0.88, while the signal confidence score of B1 is 0.18 or the channel activation flag of B1 is not enabled, then the control unit can determine that the corresponding target point group does not meet the bilateral validity requirement, and therefore will not directly use the corresponding target point group to generate bilateral differential results, or will mark the corresponding target point group as a low confidence difference group. As an example, the bilateral validity requirement can be configured such that the confidence scores of both left and right monitoring target points are not lower than 0.50, and the difference between their confidence scores is not greater than 0.40; this value can be adjusted according to the equipment calibration results and monitoring scenario.

[0127] When the bilateral effective state meets the requirements, the control unit can generate corresponding monitoring quantities for the left and right feet in the corresponding functional areas based on the signal reliability parameters and physiological feedback signals of the two monitoring target points in the same target point group. This generation process may include feature extraction, window statistics, or reliability constraints on the physiological feedback signals. Taking the optical pulse wave signal as an example, the control unit can extract at least one of the following within a 1- to 5-second acquisition window: waveform amplitude, pulse wave area, rising slope, waveform stability, or perfusion-related features, and use it as a local perfusion characterization quantity. Taking the electrical response signal as an example, the control unit can extract at least one of the following: response amplitude, response phase, response stability, or impedance change features, and use it as a local electrical response characterization quantity.

[0128] The features mentioned above are merely examples. In actual implementation, other features that can characterize the physiological meaning of the target can be selected based on the structure of the monitored target and the type of signal.

[0129] In one possible implementation, when only one monitoring target point is set for a certain functional area on the left or right foot, the control unit can associate and store the physiological feedback signal characteristics of the monitoring target point with its signal reliability parameter, and determine the characteristic as the corresponding monitoring quantity of the foot in that functional area. For example, if the optical pulse wave amplitude of A1 in the left foot is stable in the k-th acquisition window and the signal reliability score is 0.86, the control unit can determine the local perfusion characterization quantity corresponding to A1 as the corresponding monitoring quantity of the first functional area of ​​the left foot, and record its reliability score as 0.86. The corresponding monitoring quantity of the first functional area of ​​the right foot B1 is generated in the same way.

[0130] In another implementation, when multiple monitoring target points are set in the same functional area of ​​the same foot, the control unit can perform weighted synthesis of the physiological feedback signals based on the signal confidence parameters of the multiple monitoring target points to generate the corresponding monitoring quantity of the foot in the functional area.

[0131] For example, a functional area of ​​the left foot includes two candidate monitoring targets, where the confidence score of the first candidate monitoring target is 0.80 and the confidence score of the second candidate monitoring target is 0.40. The control unit can then give higher weight to the physiological feedback signal of the first candidate monitoring target in the generation of the corresponding monitoring quantity for that functional area. When the channel activation flag of a candidate monitoring target is disabled, the control unit can generate the corresponding monitoring quantity without using the physiological feedback signal of that candidate monitoring target. Therefore, the corresponding monitoring quantity is not simply the original signal, but a monitoring quantity constrained by the signal confidence parameter.

[0132] To facilitate subsequent processing, identically named monitoring data can be recorded in a structured data format. For example, a single identically named monitoring data entry may include: acquisition window identifier, foot identifier, functional area identifier, monitoring data type, monitoring data value, corresponding signal reliability score, and bilateral valid status identifier. The monitoring data type can represent a local perfusion characterization, a local electrical response characterization, a body surface temperature characterization, or other physiological state characterization; the monitoring data value can be a single value or a vector composed of multiple features. This data structure clarifies the source, meaning, and reliability of identically named monitoring data.

[0133] After generating corresponding monitoring values ​​for the left and right feet, the control unit performs bilateral differential processing on these values. Bilateral differential processing is used to obtain the differences between the left and right feet in the same functional area and with the same physiological meaning. As one possible implementation, the control unit can calculate the difference between the corresponding monitoring values ​​for the left and right feet and use this difference as the bilateral differential result for the corresponding functional area.

[0134] As another possible implementation, the control unit can also normalize the difference based on the mean, historical benchmark, or individual baseline of the same monitoring values ​​of the left and right feet to reduce the impact of individual differences or dimensional differences on the results.

[0135] In a specific example, the first functional region corresponds to the local perfusion characterization. Within the k-th acquisition window, the local perfusion characterization of the first functional region of the left foot is 0.72, and the local perfusion characterization of the first functional region of the right foot is 0.60. Both meet the bilateral validity requirement, so the control unit can obtain the bilateral difference result of this functional region as the left foot being relatively higher than the right foot. If the second functional region corresponds to the local electrical response characterization, and the response characterization of the second functional region of the left foot is 0.45, and the response characterization of the second functional region of the right foot is 0.47, then the control unit can obtain the bilateral difference result of this functional region as nearly symmetrical.

[0136] In some implementations, the control unit can synthesize bilateral difference results corresponding to multiple functional regions to generate physiological state assessment indicators for the target object. These physiological state assessment indicators are intermediate monitoring data used to characterize differences in the physiological state of the left and right feet or changes in local conditions, and are not directly equivalent to disease diagnostic conclusions. For example, physiological state assessment indicators may include indicators of local perfusion differences between the two feet, indicators of functional region asymmetry, low-reliability monitoring alerts, or comprehensive status scores. These indicators can be used to indicate whether there is an increase in the difference in state between the left and right feet within the current monitoring window, unstable monitoring results in a certain functional region, or insufficient reliability of the monitoring data.

[0137] As one possible implementation, the control unit can jointly process the two-sided difference results of different functional areas with the confidence scores of the corresponding functional areas to obtain a comprehensive status score.

[0138] For example, if both the first and second functional regions meet the bilateral validity requirements, the control unit can incorporate the bilateral difference results of both functional regions into the overall state score. If the bilateral validity of the second functional region does not meet the requirements, the control unit can reduce the influence of the second functional region or only output a low-confidence warning for that functional region, without using it as the primary evaluation criterion. This can prevent a single functional region from having an excessive impact on the overall evaluation result due to poor contact or abnormal cross-target coupling.

[0139] For example, the control unit can generate a set of physiological state assessment indicators within each 2-second acquisition window. This set of indicators may include: bilateral difference values ​​for the first functional region, bilateral difference values ​​for the second functional region, a comprehensive bilateral difference score, the number of functional regions involved in the calculation, a low-confidence functional region identifier, and a timestamp. If, within multiple consecutive acquisition windows, the bilateral difference result for a certain functional region continuously increases, and the bilateral effective state of the corresponding target group continuously meets the requirements, the control unit can output an enhanced state difference indication for the corresponding functional region. If the bilateral difference result increases but the signal confidence parameter of the corresponding functional region is low, the control unit can output a low-confidence indication, suggesting that the result may be affected by the contact state or cross-target coupling state.

[0140] It should be noted that the generation of physiological state assessment indicators can be achieved using rule tables, weighted scoring, state machines, or lightweight classification models. If a rule table or state machine is used, the input can be limited to two-sided difference results, signal confidence parameters, and two-sided valid states, with the output being physiological state assessment indicators or corresponding prompts. If a lightweight classification model is used, the model input can include two-sided difference results for different functional regions, confidence scores for each functional region, the number of low-confidence functional regions, and continuous window change characteristics; the model output can be a state level or a comprehensive score. Training samples can come from labeled historical monitoring windows, with labels such as "stable difference between left and right feet," "increased difference between left and right feet," and "low monitoring confidence," among other intermediate state categories. This model approach is an optional embodiment, and this application does not require the use of a machine learning model.

[0141] In this way, the control unit converts the raw physiological feedback signal into corresponding monitoring quantities with left-right foot and functional area correspondences, and performs bilateral differential processing under reliability constraints. As a result, the physiological state assessment index can simultaneously reflect the physiological state differences between the left and right feet in the same functional areas and the reliability of the corresponding monitoring data, thereby reducing the impact of poor contact, abnormal cross-target area coupling, and inconsistent acquisition conditions between the left and right feet on the assessment results.

[0142] Optional, see Figure 2 The flowchart illustrates a method for determining the contact state corresponding to each monitoring target point, as provided in this application embodiment, including:

[0143] S201: Determine the pressure sensing distributions corresponding to the left and right feet based on the pressure sensing information;

[0144] S202: Determine the pressure-associated region corresponding to the target monitoring point based on the positional correspondence between the target monitoring point and the pressure sensing distribution;

[0145] S203: Based on the pressure sensing information in the pressure-associated area, determine the contact state of the target monitoring point.

[0146] In some implementations, to address the problem of difficulty in directly determining the contact state of each monitoring target point when the foot bearing information and the monitoring target point are not in the same physical location, the contact state of each monitoring target point can be determined based on the positional correspondence between the foot bearing distribution and the monitoring target point.

[0147] Specifically, the control unit can determine the plantar weight distribution for the left and right feet based on the plantar weight information. The plantar weight distribution can be composed of the output data from multiple plantar weight sampling units within the same acquisition window.

[0148] For example, eight load-bearing sampling units can be set in the sole area of ​​each foot, corresponding to the medial heel, lateral heel, medial arch, lateral arch, medial forefoot, lateral forefoot, big toe area, and little toe area, respectively. The control unit acquires the output value of each load-bearing sampling unit within each acquisition window and forms the corresponding sole load distribution of the foot. This output value can be a pressure value, load value, analog-to-digital conversion value, or normalized load value.

[0149] After determining the plantar weight-bearing distribution, the control unit can determine the weight-bearing associated region corresponding to the target monitoring point based on the positional correspondence between the target monitoring point and the plantar weight-bearing distribution. This positional correspondence can be pre-configured at the factory or determined by the control unit during the target user's initial wearing process based on the model of the left and right foot wearable components, the target point location, and foot size information. The weight-bearing associated region can be understood as one or more weight-bearing sampling areas in the plantar weight-bearing distribution that have a high degree of correlation with the contact state of the target monitoring point.

[0150] For example, if target monitoring point A1 is located near the lateral malleolus of the left foot or on the lateral side of the lower leg, the control unit can determine the weight-bearing sampling areas corresponding to the lateral heel, lateral arch, and lateral forefoot of the left foot as the weight-bearing associated areas of A1. If target monitoring point A2 is located near the medial malleolus of the left foot or on the medial side of the lower leg, the control unit can determine the weight-bearing sampling areas corresponding to the medial heel, medial arch, and medial forefoot of the left foot as the weight-bearing associated areas of A2. The corresponding monitoring point in the right foot can have its weight-bearing associated areas determined in the same way.

[0151] Furthermore, the control unit can configure a load-bearing association table for each monitoring target point. The load-bearing association table records the monitoring target point identifier, the associated load-bearing sampling unit identifier, and the association weight of each load-bearing sampling unit. For example, the load-bearing association area corresponding to monitoring target point A1 includes the heel lateral sampling unit P2, the arch lateral sampling unit P4, and the forefoot lateral sampling unit P6, whose association weights can be set to 0.3, 0.3, and 0.4, respectively. The control unit can obtain the load-bearing association information corresponding to A1 based on the plantar load-bearing information of the above sampling units within the current acquisition window. The above weights are only examples and can be adjusted according to the target point location, sock-like load-bearing structure, and calibration data.

[0152] After obtaining the plantar load information within the load-bearing associated area, the control unit can determine the contact state of the target monitoring point. As one possible implementation, the control unit can calculate the weighted load value and load fluctuation value within the load-bearing associated area in each acquisition window. The weighted load value characterizes the overall pressure level of the area corresponding to the target monitoring point, while the load fluctuation value characterizes the pressure stability of that area within the current acquisition window.

[0153] For example, within a 1-second acquisition window, if the load-bearing sampling unit samples at 50Hz, and the load-bearing associated area of ​​A1 includes 3 load-bearing sampling units, the control unit can determine the weighted load-bearing value and load-bearing fluctuation value corresponding to A1 based on 150 sampled values. When the weighted load-bearing value is within the normal pressure range obtained from the target object's wearing calibration, and the load-bearing fluctuation value does not show obvious short-term abrupt changes, it can be determined that A1 is in a reliable contact state; when the weighted load-bearing value is below the normal pressure range or the load-bearing fluctuation value shows short-term abrupt changes, it can be determined that A1 is in a weak contact state or an abnormal contact state.

[0154] As an example, if the normalized weighted load-bearing value is between 0.20 and 0.85, and the normalized load-bearing fluctuation value is no greater than 0.30, the target monitoring point can be determined to be in a reliable contact state; if the normalized weighted load-bearing value is below 0.20, or the normalized load-bearing fluctuation value is greater than 0.30, the target monitoring point can be determined to be in a weak contact state; if the normalized weighted load-bearing value is close to 0, or remains below 0.05 for multiple consecutive acquisition windows, the target monitoring point can be determined to be in an abnormal contact state. The above values ​​are only one embodiment; in actual use, they can be configured according to the sensor calibration results, the target object's weight range, the tightness of the wearable components, and the location of the monitoring point.

[0155] In this way, instead of simply using plantar pressure to determine the standing phase or gait, the plantar weight distribution is mapped to the weight-bearing associated area corresponding to each monitoring target point, and the contact state of the target monitoring target point is determined based on the plantar weight-bearing information in the weight-bearing associated area, thereby providing more target-specific input for the subsequent determination of signal reliability parameters.

[0156] Optional, see Figure 3 The flowchart illustrates a method for determining the cross-target region coupling state of a corresponding foot, as provided in this application embodiment, including:

[0157] S301: The cross-target coupling state is used to characterize the degree of abnormal conduction or signal crosstalk between different monitoring target points within the same foot.

[0158] S302: Determine the inter-regional response relationship between the target monitoring point within the same foot and adjacent target monitoring points;

[0159] S303: Determine the cross-target coupling state based on the degree of deviation of the inter-regional response relationship from the baseline response relationship of the corresponding target region.

[0160] In some implementations, in order to address the problem that abnormal conductivity or signal crosstalk may occur between multiple monitoring target points within the same foot due to sweat accumulation, conductive gel diffusion, local wetting, or changes in adhesion, the cross-target area coupling state of the corresponding foot can be determined based on the coupling detection information between different monitoring target points.

[0161] In this application, the cross-target coupling state is used to characterize the degree of abnormal conductivity or signal crosstalk between different monitoring target points within the same foot. The cross-target coupling state differs from the contact state of a single monitoring target point. The contact state primarily reflects the adhesion between a monitoring target point and the surface of the target object, while the cross-target coupling state primarily reflects whether there are undesirable inter-regional influences between different monitoring target points. For example, a monitoring target point may have good contact itself, but if abnormal conductivity occurs between it and adjacent monitoring target points, the physiological feedback signal collected by that monitoring target point may still be mixed with signal components from adjacent regions.

[0162] In one implementation, the control unit can determine the inter-regional response relationship between a target monitoring point within the same foot and adjacent monitoring points. The inter-regional response relationship can be used to represent the electrical response, signal interaction, or detection stimulus response between two monitoring points. Specifically, the control unit can apply a low-amplitude detection stimulus to the target monitoring point and adjacent monitoring points within a detection window and acquire the corresponding response signal.

[0163] To determine whether the current inter-regional response relationship is abnormal, the control unit can acquire the baseline response relationship for the corresponding target area. The baseline response relationship can be obtained during the initialization phase, the stable contact phase, or within a historical normal monitoring window. For example, when the target monitoring point and adjacent monitoring points are in a reliable contact state, and the monitoring components are in a stable wearing state, the control unit can collect the inter-regional response relationships within multiple detection windows and use their average value, median value, or stable interval as the baseline response relationship. The baseline response relationship can also be provided by device calibration data and corrected based on the individual data of the target object after initialization.

[0164] The control unit can determine the cross-target coupling state based on the degree of deviation of the current inter-region response relationship from the reference response relationship of the corresponding target region. The degree of deviation can be represented by changes in response amplitude, response phase, response vector distance, or differences in response curve shape.

[0165] As one possible implementation, the normalized difference between the current inter-region response vector and the reference response vector can be used as the coupling deviation, and the cross-target coupling state can be determined based on the coupling deviation.

[0166] For example, if the current inter-regional response relationship is basically consistent with the baseline response relationship, the cross-target coupling state can be determined as a normal isolation state; if the current inter-regional response relationship deviates continuously from the baseline response relationship, but the deviation is still within the correctable range, it can be determined as a coupling risk state; if the current inter-regional response relationship deviates significantly from the baseline response relationship, and the deviation is synchronous with the physiological feedback signal changes of adjacent monitoring target points, it can be determined as an abnormal coupling state.

[0167] In another implementation, the inter-regional response relationship is not necessarily obtained through active detection stimulation, but can also be obtained through the synchronous change relationship between the physiological feedback signals of the target monitoring point and adjacent monitoring points. For example, within the same acquisition window, if the physiological feedback signals of two adjacent monitoring points exhibit synchronous drift that exceeds the historical baseline relationship, and this synchronous drift is consistent with the pressure disturbance characteristics of the corresponding foot, this synchronous change relationship can be used as a manifestation of the inter-regional response relationship. This method is suitable for continuous monitoring scenarios where frequent detection stimulation is not desired or where power consumption needs to be reduced.

[0168] In some implementations, the control unit can associate and store the cross-target coupling state with the monitoring target identifier, adjacent target identifier, functional area identifier, and acquisition window identifier. For example, a cross-target coupling state record may include: foot identifier, target monitoring target identifier, adjacent monitoring target identifier, inter-regional response relationship, baseline response relationship, coupling deviation, and cross-target coupling state.

[0169] In this way, the inter-regional response relationship between different monitoring target points within the same foot can be converted into a cross-target coupling state. This cross-target coupling state can be used as input for subsequent determination of signal reliability parameters, enabling the consideration not only of the fit of a single target point but also the identification of abnormal conduction or signal crosstalk between multiple target points, thereby reducing the impact of cross-target coupling on physiological state assessment indicators.

[0170] Optionally, in some implementations, in order to address the problem that although a single monitoring target can collect physiological feedback signals, these signals may not be suitable for direct participation in the generation of subsequent monitoring quantities due to insufficient contact or cross-target coupling, the acquisition status of the target monitoring target can be determined based on the contact status and cross-target coupling status of the target monitoring target, and the signal reliability parameters of the target monitoring target can be further determined.

[0171] Specifically, the control unit can use the contact state obtained in step S102 and the cross-target coupling state obtained in step S103 as inputs. In practice, the control unit can pre-configure a state mapping table in the acquisition software, which is used to map the contact state and the cross-target coupling state to a reliable acquisition state or a restricted acquisition state.

[0172] For example, the control unit can operate within a detachable main unit of the sock-like carrier, an edge acquisition box, or a bedside monitoring terminal. This control unit may include a microcontroller, an analog-to-digital converter, a communication module, and a storage module. The storage module can store acquisition window data, contact status fields, cross-target coupling status fields, and signal reliability parameter fields. Each monitoring target point can correspond to one status record within an acquisition window. The status record may include: foot identification, monitoring target point identification, functional area identification, acquisition window identification, contact status, cross-target coupling status, acquisition status, reliability score, and channel activation indicator.

[0173] For example, when the contact state of the target monitoring point is a trusted contact state, and the cross-target area coupling state corresponding to the target monitoring point is an isolated normal state, the control unit determines that the target monitoring point is in a trusted acquisition state; when the contact state of the target monitoring point is a weak contact state, or its corresponding cross-target area coupling state is a coupling risk state, the control unit determines that the target monitoring point is in a restricted acquisition state; when the contact state of the target monitoring point is an abnormal contact state, or its corresponding cross-target area coupling state is an abnormal coupling state, the control unit can also determine that the target monitoring point is in a restricted acquisition state, and reduce or prohibit the signal use of the monitoring point in subsequent processing. The above-mentioned "trusted acquisition state" and "restricted acquisition state" are state indicators used for subsequent signal processing and do not require the acquisition hardware to stop working.

[0174] In another implementation, if both the contact state and the cross-target coupling state are represented in the form of scores, the control unit can determine the acquisition state based on the contact score and the coupling risk score.

[0175] As an example, the contact score can be output by S102, with a value ranging from 0 to 1. A higher value indicates a more reliable fit between the target monitoring point and the body surface. The coupling risk score can be output by S103, with a value ranging from 0 to 1. A higher value indicates a higher probability that the target monitoring point is affected by abnormal conduction or signal crosstalk from adjacent target points. If the contact score is not less than 0.60 and the coupling risk score is not greater than 0.30, the target monitoring point is determined to be in a reliable acquisition state. If the contact score is less than 0.60 or the coupling risk score is greater than 0.30, the target monitoring point is determined to be in a restricted acquisition state. The values ​​of 0.60 and 0.30 mentioned above are example values ​​for ease of explanation. In actual implementation, they can be adjusted according to equipment calibration, the location of the monitoring point, and the wearing status of the target object.

[0176] After determining whether the acquisition status is reliable or restricted, the control unit can determine the signal reliability parameters of the target monitoring point based on the acquisition status. The signal reliability parameters may include a channel enable flag and a reliability score. The channel enable flag indicates whether the physiological feedback signal of the corresponding monitoring point is allowed to enter the generation process of the same monitoring quantity; the reliability score indicates the degree of reliability of the physiological feedback signal of the corresponding monitoring point in subsequent processing.

[0177] As an example, a trusted acquisition status can be identified by the channel activation flag "Enabled," with a confidence score of 0.80–1.00; a restricted acquisition status can be identified by the channel activation flag "Enabled but with reduced weight" or "Not Enabled," with a confidence score of 0–0.80. The specific choice between "Enabled but with reduced weight" and "Not Enabled" can be determined by the severity of the acquisition status, the number of available monitoring targets, and the current monitoring task.

[0178] For example, within the k-th acquisition window, the contact score of the first monitoring target point A1 on the left foot is 0.82, and the coupling risk score is 0.12. Therefore, the control unit determines that A1 is in a reliable acquisition state and sets the channel enable flag of A1 to enabled, with a reliability score of 0.90. The contact score of the second monitoring target point A2 on the left foot is 0.74, but its coupling risk score is 0.46. Therefore, the control unit determines that A2 is in a restricted acquisition state and sets the channel enable flag of A2 to enabled but with reduced weight, with a reliability score of 0.45. The contact score of the second monitoring target point B2 on the right foot is 0.21. Even if B2 acquires a significant waveform, the control unit can still determine that B2 is in a restricted acquisition state and set its reliability score to 0.15 or set the channel enable flag to disabled.

[0179] In some implementations, the signal reliability parameter may also include a status reason field. The status reason field records the reason why the target monitoring point is determined to be in a restricted acquisition state, such as insufficient contact, contact fluctuation, cross-target area coupling risk, abnormal coupling, or a combination of multiple reasons. This field helps in subsequent output of low reliability alerts and also helps distinguish whether the signal unavailability is due to a wearing problem or a target area coupling problem during continuous monitoring. The status reason field can be represented by enumerated values, such as "contact_low", "contact_unstable", "coupling_risk", "coupling_abnormal", etc.; it can also be represented by Chinese status codes.

[0180] In this way, the contact state and cross-target coupling state are converted into signal reliability parameters that can be directly used in subsequent processing. This signal reliability parameter not only reflects whether the monitoring target can collect a signal, but also whether the signal is suitable for participating in the generation of the same monitoring data for the left and right feet, thereby avoiding the misinterpretation of signals caused by poor contact or cross-target crosstalk as valid physiological feedback signals.

[0181] Optionally, in some implementations, in order to address the problem that directly generating the same monitoring quantity may lead to bilateral differential misjudgment when the signal acquisition conditions of the monitoring target points corresponding to the left and right feet are inconsistent, the comparability status can be determined first based on the signal reliability parameters of the monitoring target points corresponding to the left and right feet, and then the same monitoring quantity can be generated when the comparability status meets the comparability requirements.

[0182] Specifically, the left foot target monitoring point and the corresponding right foot monitoring point refer to two monitoring points located on the left and right feet respectively, and situated within the same functional area. The functional area can be predefined during the acquisition component configuration phase. For example, the first monitoring point A1 on the left foot and the first monitoring point B1 on the right foot both correspond to the first functional area; the second monitoring point A2 on the left foot and the second monitoring point B2 on the right foot both correspond to the second functional area. The control unit can determine the corresponding monitoring points for the left and right feet based on the functional area identifiers, rather than solely relying on spatial distance or channel number.

[0183] In one specific embodiment, the acquisition software can configure the following fields for each monitoring target: foot identifier, monitoring target identifier, functional area identifier, signal type identifier, acquisition window identifier, and signal reliability parameter. Before generating the same-name monitoring quantity, the control unit can first filter out left and right foot monitoring targets within the same acquisition window that have the same functional area identifier, the same signal type identifier, or the same monitoring meaning, forming a candidate group of the same-name target. For example, if the functional area identifier of A1 and B1 is "F1" and the signal type identifier is "PPG perfusion signal", then A1 and B1 can form a candidate group of the same-name target; if the functional area identifier of A2 and B2 is "F2" and the signal type identifier is "electrical response signal", then A2 and B2 can form another candidate group of the same-name target.

[0184] For candidate target groups with the same name, the control unit can read the signal confidence parameters of the monitoring targets on both sides and determine the comparability status between the corresponding monitoring targets of the left and right feet. The comparability status is used to indicate whether the signals of the corresponding monitoring targets of the left and right feet are suitable for generating the same monitoring quantity and subsequent two-sided difference. The comparability status can be expressed as "meets comparability requirements" or "does not meet comparability requirements", or it can be expressed as a comparability score between 0 and 1.

[0185] As one possible implementation method, the comparability requirement may include at least one of the following conditions: the channel activation flags of the monitoring target points corresponding to the left and right feet are both enabled; the confidence scores of the monitoring target points corresponding to the left and right feet are not lower than the first score requirement; the difference in confidence scores of the monitoring target points corresponding to the left and right feet is not greater than the second score requirement; and the monitoring target points corresponding to the left and right feet are in the same acquisition window or the time offset does not exceed the allowable range.

[0186] For example, the first scoring requirement can be set to 0.50, the second scoring requirement can be set to 0.40, and the allowed time offset can be set to 100ms to 500ms. The above values ​​are just examples and can be adjusted according to the sampling frequency, signal type, and monitoring scenario.

[0187] For example, within the k-th acquisition window, the confidence score of the first monitoring target A1 on the left foot is 0.86, and the confidence score of the first monitoring target B1 on the right foot is 0.81. Both A1 and B1 are enabled, and their acquisition windows are the same. Therefore, the control unit determines that the comparability between A1 and B1 meets the comparability requirements. However, if the confidence score of A1 is 0.88, while the confidence score of B1 is 0.25, even if the physiological feedback signal quality of A1 is good, the control unit can still determine that the comparability between A1 and B1 does not meet the comparability requirements, thus avoiding the generation of identical monitoring values ​​based on data with significantly inconsistent acquisition conditions on both sides.

[0188] When the comparability requirements are met, the control unit can generate corresponding monitoring quantities for the left and right feet based on the physiological feedback signals of the monitoring target points corresponding to the left and right feet. As one implementation, if there is only one monitoring target point for each functional area, the same type of physiological feature can be extracted from the physiological feedback signals of the corresponding monitoring target points for the left and right feet, respectively, and corresponding monitoring quantities for the left and right feet can be generated.

[0189] For example, for PPG perfusion signals, one or more of the following can be extracted: pulse amplitude, waveform area, perfusion index, or waveform stability within the acquisition window; for electrical response signals, one or more of the following can be extracted: response amplitude, response phase, response stability, or normalized impedance change.

[0190] For example, if both A1 and B1 correspond to the first functional area and both acquire PPG perfusion signals, the control unit can extract the average pulse wave amplitude of A1 and B1 respectively within the same 2-second acquisition window, and generate corresponding monitoring quantities for the left and right first functional areas by combining the corresponding confidence scores. If the pulse wave amplitude characteristic of A1 is 0.72 and the confidence score is 0.86, and the pulse wave amplitude characteristic of B1 is 0.60 and the confidence score is 0.81, then 0.72 can be used as the corresponding monitoring quantity for the left first functional area, and 0.60 can be used as the corresponding monitoring quantity for the right first functional area, and the respective confidence scores can be associated and stored with the corresponding monitoring quantities.

[0191] In another implementation, if the same functional area includes multiple candidate monitoring targets in the same foot, the physiological feedback signal characteristics can be weighted and synthesized based on the signal confidence parameters of the candidate monitoring targets to obtain the same monitoring quantity of the foot in the corresponding functional area.

[0192] For example, the first functional region of the left foot includes two candidate monitoring targets, A1a and A1b. A1a has a confidence score of 0.90, and A1b has a confidence score of 0.45. The control unit can then give greater weight to the feature corresponding to A1a in the generation of the same monitoring quantity. If A1b is not enabled, its physiological feedback signal can be ignored. The weights can be obtained by normalizing the confidence scores or by a pre-configured functional region weight table.

[0193] When the comparability status does not meet the comparability requirements, the control unit may choose not to generate the same monitoring quantity for the same target group within the current acquisition window, or it may generate the same monitoring quantity with a low confidence flag for subsequent display or recording. For example, when the confidence of B1 is too low, the control unit may mark the same target group corresponding to A1 / B1 as "incomparable" and output a low confidence prompt for that functional region in the physiological state assessment index, instead of using the two-sided difference results of that functional region as the primary assessment basis. This can avoid spurious two-sided differences caused by abnormal contact on one side or abnormal cross-target coupling.

[0194] To facilitate implementation, the control unit can record comparability status and identical monitoring quantities as structured data. For example, a record of identical monitoring quantities may include: acquisition window identifier, functional area identifier, left foot monitoring target identifier, right foot monitoring target identifier, left foot confidence score, right foot confidence score, comparability status, identical monitoring quantity for the left foot, identical monitoring quantity for the right foot, and low confidence reason identifier. Among them, the low confidence reason identifier can record reasons such as "not meeting the left confidence requirement", "not meeting the right confidence requirement", "excessive difference in confidence between the left and right sides", and "inconsistent time window".

[0195] In this way, corresponding monitoring values ​​for the left and right feet are generated within the same functional area, the same acquisition window, and under the premise that the reliability conditions are met. This allows subsequent bilateral differential processing to be based on a more comparable and reliable data foundation, reducing evaluation errors caused by inconsistencies in the acquisition conditions for the left and right feet.

[0196] Optionally, in some implementations, in order to address the problem that it is difficult to directly compare the left and right feet when multiple monitoring target points have different body surface locations and different physiological signal meanings on the same foot, multiple monitoring target points can be divided into different functional regions, and the same monitoring quantity can be generated based on the functional regions.

[0197] Functional regions are used to characterize the body surface location category, signal acquisition purpose, or physiological signal meaning of the monitoring target. Different functional regions can have different functional region labels. For example, the first functional region can be denoted as F1, and the second functional region as F2. A monitoring target in the left foot located in the first functional region can be denoted as A1, and a monitoring target in the left foot located in the second functional region can be denoted as A2; a monitoring target in the right foot located in the first functional region can be denoted as B1, and a monitoring target in the right foot located in the second functional region can be denoted as B2. In this case, A1 and B1 belong to the same functional region, and A2 and B2 belong to the same functional region.

[0198] In one specific embodiment, the control unit may store a functional area configuration table. The functional area configuration table may include functional area identifiers, left foot monitoring target point identifiers, right foot monitoring target point identifiers, signal type identifiers, and monitoring quantity type identifiers. For example, the functional area configuration table may include the following records: functional area F1 corresponds to left foot monitoring target point A1 and right foot monitoring target point B1, the signal type is an optical pulse wave signal or an electrical response signal, and the monitoring quantity type is a first local state characterization quantity; functional area F2 corresponds to left foot monitoring target point A2 and right foot monitoring target point B2, the signal type is an electrical response signal or an impedance response signal, and the monitoring quantity type is a second local state characterization quantity.

[0199] When generating corresponding monitoring values, the control unit can pair left and right sides not directly according to channel numbers, but according to the functional area configuration table. That is, only left and right foot monitoring targets located in the same functional area and having the same or corresponding monitoring value type are used to generate corresponding monitoring values. For example, A1 and B1 can generate corresponding monitoring values ​​in the first functional area, and A2 and B2 can generate corresponding monitoring values ​​in the second functional area; although A1 and B2 may both output physiological feedback signals within the same acquisition window, they are not considered left and right corresponding monitoring values ​​because their functional areas are different.

[0200] In some implementations, the corresponding monitoring values ​​for different functional regions can be retained separately instead of being merged into a single total. For example, the control unit can generate the following data in the k-th acquisition window: the corresponding monitoring values ​​for the left foot M_L_F1 and the right foot M_R_F1 for the first functional region, and the corresponding monitoring values ​​for the left foot M_L_F2 and the right foot M_R_F2 for the second functional region. Subsequent two-sided differential processing can be performed separately for F1 and F2 to obtain the two-sided differential results for different functional regions.

[0201] In this way, multiple monitoring targets are not processed as undifferentiated multi-channel signals, but rather a correspondence between the left and right feet is established according to functional regions. This avoids mispairing signals from different body surface locations, with different signal meanings, or with different monitoring purposes, and improves the comparability of corresponding monitoring data from the left and right feet.

[0202] Optionally, in some embodiments, the first monitoring target point may correspond to the area adjacent to the nerves of the lower limb, and the second monitoring target point may correspond to the area adjacent to the acupoints of the lower limb. Here, "area adjacent to the nerves of the lower limb" and "area adjacent to the acupoints of the lower limb" are used to define the functional location of the monitoring target points on the body surface, and do not imply that this application necessarily performs therapeutic stimulation, nor does it imply that a disease diagnosis conclusion is directly given.

[0203] In one specific example, the area adjacent to the lower limb nerves can be the surface area adjacent to the common peroneal nerve, the surface area adjacent to the tibial nerve, or other surface areas suitable for collecting physiological feedback signals from the area adjacent to the lower limb nerves. Taking the area adjacent to the common peroneal nerve as an example, the first monitoring target point can be set on the lateral side of the lower leg, near the head of the fibula, or on the inner side of the sock-like support near this area. The first monitoring target point can include an electrode contact area, an impedance detection area, or an optical acquisition area, used to acquire physiological feedback signals from this area.

[0204] In one specific example, the area adjacent to acupoints on the lower limbs can be the area adjacent to Zusanli (ST36), Sanyinjiao (SP6), or other acupoints on the surface of the lower limbs. Taking the area adjacent to Zusanli (ST36) as an example, the second monitoring target point can be set on the inner side of a sock-like support or ankle support near the anterolateral aspect of the lower leg and the lateral aspect of the anterior crest of the tibia. The second monitoring target point can include an electrode contact area, an impedance detection area, an optical acquisition area, or a combination thereof, used to acquire physiological feedback signals from that area. The above-mentioned area positioning can be achieved through positioning marks on the sock-like support, left and right foot wearing direction marks, elastic positioning straps, or adjustable patches.

[0205] In some implementations, the first and second monitoring targets can correspond to different signal types or different monitoring quantity types. For example, the first monitoring target can collect electrical response signals, impedance response signals, or local perfusion-related signals, while the second monitoring target can collect electrical response signals, optical pulse wave signals, or local tissue response signals. In practice, the first and second monitoring targets can also collect the same type of physiological feedback signals, but because they are located in different functional areas, the corresponding monitoring quantities they generate still belong to different functional areas.

[0206] For the first monitoring target, the control unit can generate a first signal confidence parameter; for the second monitoring target, the control unit can generate a second signal confidence parameter. The first signal confidence parameter is used to constrain the generation of the same-name monitoring quantity in the first functional area, and the second signal confidence parameter is used to constrain the generation of the same-name monitoring quantity in the second functional area. Here, "constraints" may include determining the channel activation status of the corresponding monitoring target, confidence score, low confidence alert, or whether the monitoring target participates in the generation of the same-name monitoring quantity in the corresponding functional area.

[0207] In a specific example, within the k-th acquisition window, both the first monitoring target point A1 on the left foot and the first monitoring target point B1 on the right foot correspond to the nerve-adjacent region of the lower limb. The control unit can generate corresponding monitoring quantities for the left and right feet in the first functional region based on the signal confidence parameters corresponding to A1 and B1, respectively. Both the second monitoring target point A2 on the left foot and the second monitoring target point B2 on the right foot correspond to acupoints in the lower limb. The control unit can generate corresponding monitoring quantities for the left and right feet in the second functional region based on the signal confidence parameters corresponding to A2 and B2, respectively. If the signal confidence parameter of A2 indicates that it is in a restricted acquisition state, the control unit can mark the corresponding monitoring quantity in the second functional region as low confidence without affecting the normal generation of the corresponding monitoring quantity in the first functional region.

[0208] In some implementations, the signal confidence parameters of the first and second monitoring targets can use the same data structure, but different configuration rules are allowed. For example, the first monitoring target, being close to a joint or bony prominence, may be more sensitive to changes in weight-bearing on the lateral side of the foot due to its contact state; the second monitoring target, being close to the soft tissue area of ​​the anterolateral lower leg, may be more sensitive to changes in local wetting or electrode spacing due to its cross-target coupling state. The control unit can configure contact state weights, coupling state weights, or low confidence reason fields for the first and second functional areas respectively in the functional area configuration table, but ultimately both will output signal confidence parameters to ensure uniform entry into the same-name monitoring quantity generation process.

[0209] For example, functional region F1 corresponds to the area adjacent to the nerves of the lower limb, and its signal reliability parameter can emphasize the contact state; functional region F2 corresponds to the area adjacent to acupoints of the lower limb, and its signal reliability parameter can consider both the contact state and the cross-target coupling state. As an example, the reliability score of F1 can be determined by the influence ratio of the contact score and the coupling risk score at 0.7:0.3, and the reliability score of F2 can be determined by the influence ratio of the contact score and the coupling risk score at 0.5:0.5. These ratios are merely examples and can be adjusted according to the location of the monitored target, the type of signal acquired, and the calibration data.

[0210] In this way, the first and second monitoring target points are not merely physically different acquisition points, but correspond to the adjacent areas of lower limb nerves and lower limb acupoints with different functional meanings, respectively. Their signal reliability parameters are used to generate corresponding monitoring quantities for the corresponding functional areas, enabling this application to maintain independent assessment of different functional areas and left-right foot correspondence comparison in a bipedal multi-target structure, reducing the impact of signal confusion between different functional areas on physiological state assessment indicators.

[0211] In some implementations, to address the issue of inconsistencies in comparison between the left and right feet when physiological feedback signals from multiple functional areas are mixed and processed, the control unit can group the monitoring target points according to functional area identifiers. Specifically, the control unit can read a functional area configuration table, which records the functional area identifier, the left foot monitoring target point identifier, the right foot monitoring target point identifier, and the corresponding monitoring quantity type. Based on this functional area configuration table, the control unit groups the monitoring target points located in the same functional area of ​​the left and right feet into target point groups with the same name.

[0212] For example, functional area F1 corresponds to the first monitoring target point A1 of the left foot and the first monitoring target point B1 of the right foot, and functional area F2 corresponds to the second monitoring target point A2 of the left foot and the second monitoring target point B2 of the right foot. The control unit can generate two groups of target points with the same name within the k-th acquisition window: a first group of target points G1={A1, B1} and a second group of target points G2={A2, B2}. If a third functional area is subsequently added, a third group of target points G3 can be formed. This application does not limit the number of functional areas.

[0213] For each group of target points with the same name, the control unit can determine the bilateral effective state based on the signal confidence parameters of the left and right monitoring target points in that group. The bilateral effective state indicates whether the group of target points with the same name has the conditions to generate left and right foot identical monitoring quantities and participate in bilateral differential processing. As one possible implementation, the bilateral effective state can include an effective state, a low confidence state, and an invalid state. An effective state indicates that both left and right monitoring target points meet the usage requirements of the current functional area; a low confidence state indicates that at least one of the left and right monitoring target points has a risk of being deweighted; an invalid state indicates that at least one monitoring target point is not suitable for participating in bilateral differential processing within the acquisition window.

[0214] For example, the control unit can adopt the following processing rules: if the channel enable flags of both monitoring targets in a target group with the same name are both enabled, and both confidence scores are not lower than 0.50, then the target group with the same name is determined to be in a valid state; if neither monitoring target is disabled but at least one confidence score is between 0.30 and 0.50, then the target group with the same name is determined to be in a low confidence state; if the channel enable flag of any monitoring target is disabled, or the confidence score is lower than 0.30, then the target group with the same name is determined to be in an invalid state. The above 0.30 and 0.50 are only examples and can be adjusted according to the device calibration results and functional area type.

[0215] When the bilateral effective state meets the requirements, the control unit generates corresponding monitoring quantities for the left and right feet in their respective functional areas based on the signal reliability parameters and physiological feedback signals of the two monitoring targets in the same target group. Here, "generating separately" means that the left and right feet retain independent monitoring quantities, rather than first mixing the left and right foot signals into a single result. For example, for G1={A1, B1}, the control unit generates M_L_F1 and M_R_F1; for G2={A2, B2}, the control unit generates M_L_F2 and M_R_F2. Here, M_L represents the monitoring quantity of the left foot, M_R represents the monitoring quantity of the right foot, and F1 and F2 represent different functional areas.

[0216] In some implementations, different functional regions can correspond to different data generation rules. For example, for functional region F1, if A1 and B1 acquire local perfusion-related signals, the control unit can extract perfusion characterization quantities within a 2-second acquisition window and use them as M_L_F1 and M_R_F1; for functional region F2, if A2 and B2 acquire electrical response signals, the control unit can extract response amplitude stability or impedance change characterization quantities and use them as M_L_F2 and M_R_F2. Using different feature extraction methods for different functional regions does not affect their participation as monitored quantities in the two-sided differential analysis of their respective functional regions.

[0217] When using the same monitoring data corresponding to different functional regions for two-sided differential processing, the control unit can calculate the two-sided differential results for each functional region separately, instead of first merging the monitoring data from different functional regions. For example, the control unit can obtain the two-sided differential result D_F1 for the first functional region based on M_L_F1 and M_R_F1, and obtain the two-sided differential result D_F2 for the second functional region based on M_L_F2 and M_R_F2. Subsequently, the control unit can output D_F1 and D_F2 as independent components, or it can be combined when generating physiological state assessment indicators later.

[0218] For example, within the k-th acquisition window, G1's bilateral valid state is valid, with an A1 confidence score of 0.86 and a B1 confidence score of 0.81. The control unit generates M_L_F1=0.72 and M_R_F1=0.60, and uses them for bilateral differential processing in the first functional area. G2's bilateral valid state is low-confidence, with an A2 confidence score of 0.48 and a B2 confidence score of 0.75. The control unit can generate M_L_F2 and M_R_F2 with low-confidence indicators, or temporarily not use the corresponding results of G2 as the main differential basis. This can prevent low-confidence monitoring results in one functional area from affecting the normal evaluation of other functional areas.

[0219] Through the above implementation method, the monitoring quantities with the same name corresponding to different functional areas can be generated separately, their effectiveness evaluated separately, and entered into two-sided differential processing separately, thereby avoiding the mixing of monitoring signals from different functional areas, with different signal meanings, or with different confidence levels.

[0220] In some implementations, to address the issue that directly detecting the inter-regional response relationship between two monitoring targets is easily affected by static contact impedance, individual differences, or the natural correlation between the two physiological signals, the naturally occurring changes in plantar weight-bearing during continuous monitoring can be used as a perturbation reference to determine whether the inter-regional response relationship between the first and second monitoring targets changes dynamically with this weight-bearing change. This method does not simply measure the static response between the two targets, but rather utilizes the unique weight-bearing perturbations in the foot-wearing scenario to identify abnormal conduction states or signal crosstalk states.

[0221] Among them, the pressure disturbance feature is used to characterize the change in plantar weight-bearing of the corresponding foot during a continuous monitoring period. The control unit can read the plantar weight-bearing information of the corresponding foot in multiple consecutive acquisition windows, and determine the pressure disturbance feature based on the magnitude, direction, duration, or shift of the center of force of the plantar weight-bearing information.

[0222] For example, a continuous monitoring period may include 5 to 20 acquisition windows, each with a length of 1 second or 2 seconds. When the normalized total weight-bearing variation of a certain foot is greater than 0.15, or when the weight-bearing center of the foot continuously shifts between the forefoot and hindfoot regions, the control unit can determine that there are pressure disturbance characteristics during that period. The number and variation of the windows mentioned above are only examples and can be adjusted according to the sampling frequency and monitoring scenario.

[0223] After determining the characteristics of the pressure disturbance, the control unit determines the inter-regional response relationship between the first monitoring target point and the second monitoring target point within the time period corresponding to the pressure disturbance characteristics. The first monitoring target point can be a monitoring target point corresponding to the region adjacent to the nerves of the lower limb, and the second monitoring target point can be a monitoring target point corresponding to the region adjacent to the acupoints of the lower limb. The inter-regional response relationship can be the detection response relationship between the first monitoring target point and the second monitoring target point, or it can be the synchronous change relationship between their physiological feedback signals. For example, if the control unit detects a shift in weight-bearing from the outside to the inside of the left foot within the k to k+5 acquisition windows, the control unit can extract the inter-regional response relationship change sequence between A1 and A2 of the left foot within the same time period.

[0224] In one possible implementation, the inter-regional response relationship can be represented as a time-varying sequence of response characteristics.

[0225] In the implementation of passively acquiring inter-regional response relationships, the control unit can extract window-level inter-regional response values ​​from the physiological feedback signals of the first and second monitoring targets, using acquisition windows as units. Specifically, within the i-th acquisition window, the control unit acquires physiological feedback signal segments X1_i from the first and second monitoring targets, respectively, performs mean-removal or linear detrending processing on X1_i and X2_i, and determines the correlation coefficient, normalized covariance value, or proportional change in the same direction between the two processed signal segments as the inter-regional response value R_i within the acquisition window. The R_i corresponding to multiple consecutive acquisition windows constitute the inter-regional response relationship change sequence R_A12.

[0226] Correspondingly, the control unit can generate a load-bearing change sequence W_L based on the foot load-bearing information within the same continuous monitoring period. For example, the normalized change in total foot load, the lateral displacement of the load-bearing center, or the anterior-posterior displacement of the load-bearing center within each acquisition window can be used as the load-bearing disturbance value W_i for that acquisition window. The W_i corresponding to multiple consecutive acquisition windows constitute the load-bearing change sequence W_L. Subsequently, the control unit calculates the follow-up relationship between R_A12 and W_L, and uses the correlation between the two, the number of changes in the same direction, or the proportion of consistent changes within the allowable lag window as follow-up characteristics.

[0227] For example, within six consecutive acquisition windows, the left foot weight-bearing change sequence W_L is [0.10, 0.12, 0.31, 0.44, 0.58, 0.61], and the inter-regional response relationship change sequence R_A12 obtained from the detrended physiological feedback signals of the first monitoring target A1 and the second monitoring target A2 is [0.18, 0.20, 0.45, 0.57, 0.70, 0.72]. The control unit can calculate the correlation between the two. If the correlation is not less than 0.80, or if there are no fewer than a predetermined proportion of windows showing unidirectional response changes in the windows with significant weight-bearing disturbances, then the inter-regional response relationship between A1 and A2 is determined to have strong follow-up characteristics. If the correlation between R_B12 and W_R corresponding to the right foot is low, for example, less than 0.30, then the target area coupling characteristics corresponding to the left foot are determined to be higher than those corresponding to the right foot. The above values ​​and correlation requirements are only examples and can be adjusted according to the acquisition window length, signal type, and equipment calibration results.

[0228] For example, the control unit generates a response value between regions A1 and A2 within each acquisition window, and multiple consecutive acquisition windows form a response feature sequence R_A12. This response feature sequence can originate from the detected stimulus response or from the synchronous changes between the physiological feedback signals of A1 and A2. The pressure disturbance characteristics can also be represented as a weight-bearing change sequence W_L. The control unit can compare the temporal relationship between R_A12 and W_L to determine whether the inter-regional response relationship is modulated by changes in plantar weight-bearing.

[0229] Follow-up features are used to represent the degree of synchronous change, lag, or directional consistency of inter-regional response relationships relative to the pressure disturbance feature. As one possible implementation, the control unit can calculate the correlation, number of unidirectional changes, or proportion of consistent changes within a lag window between the inter-regional response relationship change sequence and the pressure disturbance feature sequence.

[0230] For example, if, within four out of six consecutive acquisition windows where the pressure disturbance characteristics change significantly, the inter-regional response relationship shows a change in the same direction or a corresponding change within a preset lag range, then it can be determined that the inter-regional response relationship has strong follow-up characteristics. Here, "change in the same direction" and "lag range" can be set according to the acquisition frequency and signal response speed; for example, a lag of one to two acquisition windows may be allowed.

[0231] The control unit determines the target area coupling characteristics of the corresponding foot based on the follow-up characteristics. The target area coupling characteristics are used to characterize whether the inter-regional response relationship between the first and second monitoring target points changes with the weight-bearing capacity of the foot. If the inter-regional response relationship is not sensitive to weight-bearing disturbances, it indicates that the first and second monitoring target points are relatively independent; if the inter-regional response relationship shows significant co-movement with weight-bearing disturbances, it indicates that there may be abnormal coupling pathways between the two functional areas caused by contact interfaces, sweat pathways, conductive medium diffusion, or local pressure changes.

[0232] For example, during an 8-second continuous monitoring period of the left foot, the control unit detects that the center of weight-bearing on the sole of the foot has shifted from the outer side of the heel to the outer side of the forefoot, forming a pressure disturbance feature W_L on the left foot. Simultaneously, the inter-regional response relationship R_A12 between the first monitoring target point A1 and the second monitoring target point A2 on the left foot synchronously increases within adjacent acquisition windows. If this synchronous increase continues to occur in multiple windows, the control unit can determine that the coupling feature of the left foot target area is a high coupling feature. Conversely, if the change in the pressure disturbance feature W_R of the right foot is small, or if the inter-regional response relationship R_B12 between B1 and B2 of the right foot does not change accordingly with the change in weight-bearing, the control unit can determine that the coupling feature of the right foot target area is low.

[0233] After determining the target area coupling characteristics of the left and right feet, the control unit can determine the cross-target area coupling state of the left and right feet based on the differences between them. Specifically, if the target area coupling characteristics of the left foot are significantly higher than those of the right foot, the control unit can determine that there is more likely an abnormal conduction state or signal crosstalk state between the first and second monitoring target points in the left foot; if the target area coupling characteristics of both feet are low, it can be determined that the cross-target area coupling state of both feet is in a normal isolation state; if the target area coupling characteristics of both feet are high, it can be further determined whether it is a bilateral common wearing problem or a systemic interference by combining the contact state and pressure disturbance characteristics of the corresponding feet.

[0234] For example, the control unit can represent the target area coupling characteristics as a coupling follow-up score between 0 and 1. If the left foot coupling follow-up score is 0.78 and the right foot coupling follow-up score is 0.22, then the left foot's cross-target area coupling state can be determined as an abnormal coupling state or a coupling risk state, while the right foot's cross-target area coupling state is an isolation normal state. If the left foot coupling follow-up score is 0.35 and the right foot coupling follow-up score is 0.31, then it can be determined that neither of them shows obvious unilateral abnormal coupling. The above scoring thresholds can be adjusted in combination with equipment calibration, monitoring environment, and functional area type. For example, 0-0.30 can be used as the isolation normal range, 0.30-0.60 as the coupling risk range, and above 0.60 as the abnormal coupling range.

[0235] In some implementations, the control unit can also record the pressure disturbance characteristics, inter-regional response relationships, follow-up characteristics, target area coupling characteristics, and cross-target area coupling states in a structured manner. For example, a record may include: foot identification, continuous monitoring period identification, first monitoring target identification, second monitoring target identification, pressure disturbance characteristics, inter-regional response relationship sequence, follow-up characteristics, target area coupling characteristics, and cross-target area coupling states. In this way, when determining signal reliability parameters, the cross-target area coupling states of the corresponding foot and the corresponding target point group can be directly read from the record.

[0236] It should be noted that in implementations capable of performing inter-regional detection excitation, the cross-target coupling state can be preferentially determined based on the inter-regional response relationship corresponding to the detection excitation. In this case, the inter-regional response relationship reflects the electrical pathway or signal coupling pathway between the first and second monitoring targets, rather than simply reflecting the amplitude changes of the physiological signals acquired by each of the two monitoring targets. Changes in foot weight-bearing can alter the local tension of the sock support, the contact pressure between the electrode and the skin, the distribution of the interfacial liquid film, or the diffusion morphology of the conductive medium. When these changes cause the inter-regional detection response vector to deviate from the baseline response relationship, it indicates a change in the abnormal conduction path or crosstalk path between the two monitoring targets.

[0237] In implementations where no detection stimulus is applied, the control unit can use the follow-up characteristics between physiological feedback signals as an auxiliary criterion for judging the risk of cross-target coupling. To reduce the risk of misjudging genuine physiological correlation as abnormal coupling, the control unit can combine at least one of the following for judgment: the degree of follow-up of the inter-regional response relationship relative to the pressure disturbance characteristics, the difference between the coupling characteristics of the target areas corresponding to the left and right feet, the contact state of the corresponding monitoring target points, and the baseline response relationship in the historical stable window. If there is only a natural correlation between the physiological signals of the two monitoring target points, and no follow-up deviation corresponding to the plantar weight-bearing disturbance, or if the target areas corresponding to the left and right feet show similar common changes, the control unit may not determine it as an abnormal coupling state.

[0238] In this way, by using changes in foot weight-bearing as a natural perturbation reference, it is possible to determine whether the inter-regional response relationship between the lower limb nerve adjacency region and the lower limb acupoint adjacency region changes with weight-bearing perturbation, and to determine the cross-target area coupling state by combining the differences in coupling characteristics between the left and right foot target areas. This allows for a better distinction between the correlation of real physiological signals and cross-target area coupling caused by contact interfaces or abnormal conduction pathways.

[0239] Based on the same inventive concept, this application also provides a multi-target adaptive monitoring system based on physiological signal feedback, which corresponds to the multi-target adaptive monitoring method based on physiological signal feedback. Since the principle of the system in this application is similar to the multi-target adaptive monitoring method based on physiological signal feedback described above, the implementation of the system can refer to the implementation of the method, and the repeated parts will not be described again.

[0240] Reference Figure 4 The diagram shown is a schematic of a multi-target adaptive monitoring system based on physiological signal feedback provided in an embodiment of this application. The system includes:

[0241] The acquisition module 10 is used to acquire pressure sensing information corresponding to the left and right feet of the target object, as well as physiological feedback signals collected by multiple monitoring target points placed on the left and right feet; and to determine the contact state corresponding to each monitoring target point based on the pressure sensing information.

[0242] The first processing module 20 is used to determine the cross-target area coupling state of the corresponding foot based on the coupling detection information between different monitoring target points within the same foot.

[0243] The second processing module 30 is used to determine the signal reliability parameters of each of the monitoring target points based on the contact state and the cross-target coupling state.

[0244] The generation module 40 is used to generate corresponding monitoring quantities for the left foot and the right foot respectively from the physiological feedback signal based on the signal confidence parameter; perform bilateral differential processing on the corresponding monitoring quantities for the left foot and the right foot, and generate physiological state assessment indicators for the target object based on the bilateral differential processing results.

[0245] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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 or all of the technical features therein. 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 this application.

Claims

1. A multi-target adaptive monitoring method based on physiological signal feedback, characterized in that, include: The pressure sensing information corresponding to the left and right feet of the target object is obtained, as well as the physiological feedback signals collected by multiple monitoring target points placed on the left and right feet respectively; Based on the pressure sensing information, the contact state corresponding to each of the monitoring target points is determined; Based on the coupling detection information between different monitoring target points within the same foot, the cross-target area coupling state of the corresponding foot is determined. Based on the contact state and the cross-target coupling state, the signal reliability parameters of each monitoring target point are determined; Based on the signal reliability parameter, corresponding monitoring quantities for the left foot and the right foot are generated from the physiological feedback signal; bilateral differential processing is performed on the corresponding monitoring quantities for the left foot and the right foot, and physiological state assessment indicators for the target object are generated based on the bilateral differential processing results.

2. The multi-target adaptive monitoring method based on physiological signal feedback according to claim 1, characterized in that, Determining the contact state corresponding to each of the monitoring target points includes: The pressure sensing distributions corresponding to the left and right feet are determined based on the pressure sensing information. Based on the positional correspondence between the target monitoring point and the pressure sensing distribution, the pressure-associated region corresponding to the target monitoring point is determined; Based on the pressure sensing information within the pressure-associated area, the contact state of the target monitoring point is determined.

3. The multi-target adaptive monitoring method based on physiological signal feedback according to claim 1, characterized in that, Determining the cross-target region coupling state of the corresponding foot includes: The cross-target coupling state is used to characterize the degree of abnormal conductivity or signal crosstalk between different monitoring target points within the same foot. Determine the inter-regional response relationship between target points within the same foot region and adjacent target points; The cross-target coupling state is determined based on the degree of deviation of the inter-regional response relationship from the baseline response relationship of the corresponding target region.

4. The multi-target adaptive monitoring method based on physiological signal feedback according to claim 1, characterized in that, The determination of the signal reliability parameters for each of the monitoring target points includes: When the contact state of the target monitoring point meets the contact requirements and the cross-target area coupling state corresponding to the target monitoring point meets the isolation requirements, the target monitoring point is determined to be in a reliable acquisition state. When the contact state of the target monitoring point does not meet the contact requirements, or the cross-target area coupling state corresponding to the target monitoring point does not meet the isolation requirements, the target monitoring point is determined to be in a restricted acquisition state. The signal reliability parameter of the target monitoring point is determined based on the reliable acquisition state or the restricted acquisition state.

5. The multi-target adaptive monitoring method based on physiological signal feedback according to claim 1, characterized in that, The step of generating corresponding monitoring quantities for the left and right feet based on the signal reliability parameter and the physiological feedback signal includes: Based on the signal confidence parameters of the left foot target monitoring point and the signal confidence parameters of the right foot monitoring point located in the same functional area as the left foot target monitoring point, the comparability status between the corresponding monitoring points of the left and right feet is determined. When the comparability requirements are met in the comparability state, based on the physiological feedback signals of the monitoring target points corresponding to the left and right feet, the corresponding monitoring quantities for the left and right feet are generated respectively.

6. The multi-target adaptive monitoring method based on physiological signal feedback according to claim 1, characterized in that, The multiple monitoring targets include: a first monitoring target and a second monitoring target located in different functional areas; The corresponding monitoring quantity is the monitoring quantity generated by the monitoring target points located in the same functional area in the left and right feet.

7. The multi-target adaptive monitoring method based on physiological signal feedback according to claim 6, characterized in that, The first monitoring target point corresponds to the area adjacent to the nerves of the lower limb, and the second monitoring target point corresponds to the area adjacent to the acupoints of the lower limb. The signal confidence parameters corresponding to the first monitoring target and the second monitoring target are used to generate the same monitoring quantity for the corresponding functional area.

8. The multi-target adaptive monitoring method based on physiological signal feedback according to claim 6, characterized in that, The generation of corresponding monitoring values ​​for the left foot and right foot includes: According to the different functional areas, the monitoring target points in the left foot and the right foot that are located in the same functional area are formed into a target point group with the same name. For each of the aforementioned target groups, the bilateral effective state of the target group is determined based on the signal confidence parameters of the two monitoring targets in the target group. When the bilateral effective state meets the requirements, based on the signal confidence parameters and physiological feedback signals of the two monitoring target points in the same target point group, the same monitoring quantities of the left foot and the right foot in the corresponding functional areas are generated respectively. The monitoring quantities with the same name corresponding to different functional areas are used for the two-sided differential processing.

9. The multi-target adaptive monitoring method based on physiological signal feedback according to claim 7, characterized in that, Determining the cross-target region coupling state of the corresponding foot includes performing the following processes on the left foot and the right foot respectively: Based on the changes in the pressure sensing information of the corresponding foot during a continuous monitoring period, the pressure disturbance characteristics are determined; Within the time period corresponding to the pressure disturbance characteristics, determine the inter-regional response relationship between the first monitoring target and the second monitoring target; Based on the follow-up characteristics of the inter-regional response relationship relative to the pressure disturbance characteristics, the target area coupling characteristics of the corresponding foot are determined; Based on the target area coupling characteristics of the left foot and the right foot and the differences between them, the cross-target area coupling state of the left foot and the right foot is determined respectively. The cross-target coupling state is used to characterize the abnormal conduction state or signal crosstalk state between the lower limb nerve adjacent area and the lower limb acupoint adjacent area as pressure changes.

10. A multi-target adaptive monitoring system based on physiological signal feedback, characterized in that, include: The acquisition module is used to acquire pressure sensing information corresponding to the left and right feet of the target object, as well as physiological feedback signals acquired by multiple monitoring target points placed on the left and right feet respectively. Based on the pressure sensing information, the contact state corresponding to each of the monitoring target points is determined; The first processing module is used to determine the cross-target area coupling state of the corresponding foot based on the coupling detection information between different monitoring target points within the same foot. The second processing module is used to determine the signal reliability parameters of each of the monitoring target points based on the contact state and the cross-target coupling state. The generation module is used to generate corresponding monitoring quantities for the left foot and the right foot respectively from the physiological feedback signal based on the signal confidence parameter; perform bilateral differential processing on the corresponding monitoring quantities for the left foot and the right foot, and generate physiological state assessment indicators for the target object based on the bilateral differential processing results.

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