Smart forceps system with haptic feedback and cerebral blood flow monitoring and control method thereof

Abstract

The application discloses a kind of intelligent forceps system with tactile feedback and brain blood flow monitoring and control method thereof;Intelligent forceps system includes forceps body, flexible fiber bragg grating sensor group, brain blood flow detection sensor, processing unit and host computer;Host computer is communicated with processing unit and is configured to be based on the clamping force signal and brain blood flow signal after processing, according to a preset blood flow-clamping force dynamic model, calculate and generate an adaptive safe clamping force threshold value that changes with the individual physiological state of fetus;And real-time display the clamping force, temperature, brain blood flow parameters and adaptive safe clamping force threshold value, and send alarm when clamping force exceeds the threshold value.The application not only can monitor clamping force and local temperature in real time, more integrated brain blood flow monitoring function, and through the dynamic model between the establishment clamping force and brain blood flow change, generate an adaptive safe force threshold value that adjusts in real time with the individual physiological state of fetus.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, and more specifically, to an intelligent forceps delivery system with tactile feedback and cerebral blood flow monitoring, and its control method. Background Technology

[0002] Forceps delivery is a commonly used clinical method that can effectively promote fetal delivery, shorten the duration of fetal distress and asphyxia, and has the advantages of high success rate and short operation time. When used appropriately in emergency situations, it usually does not cause adverse maternal and infant outcomes. However, this method still has certain limitations: due to the large size of the forceps, it occupies a certain part of the birth canal, and it is difficult to accurately control the force used, which may lead to facial abrasions and respiratory distress in newborns. In severe cases, it may even cause asphyxia, intracranial hemorrhage, and death.

[0003] In recent years, the medical device field has made various attempts to improve forceps. Some research teams have tried to add strain gauges or pressure sensors to forceps to assist in force control through electronic feedback. For example, the patent with publication number CN 213283276 U proposes an intelligent stress forceps that uses fiber optic stress sensors on the blades to monitor the force applied to the fetal head in real time, providing feedback on the pulling force and speed used, as well as the corresponding pressure on the fetal head, which helps clinicians adjust the force during operation.

[0004] However, existing technologies mostly use fixed safety thresholds (usually set at 50-60N), without taking into account individual fetal differences. Different fetuses have significant differences in skull hardness and vascular fragility, and fixed thresholds cannot adapt to clinical diversity. They only monitor physical pressure and lack real-time assessment of the fetal physiological state. The risks in forceps delivery are not only from mechanical pressure, but also directly related to the fetal cerebrovascular response.

[0005] The aforementioned technical problems urgently need to be solved. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the present invention aims to provide an intelligent forceps delivery system and its control method with tactile feedback and cerebral blood flow monitoring. This system can not only monitor clamping force and local temperature in real time, but also integrate cerebral blood flow monitoring function. Furthermore, by establishing a dynamic model between clamping force and changes in cerebral blood flow, an adaptive safety force threshold that is adjusted in real time according to the individual physiological state of the fetus is generated.

[0007] To achieve the above objectives, on the one hand, the present invention provides an intelligent forceps delivery system with tactile feedback and cerebral blood flow monitoring, including a forceps body, wherein the forceps body includes a forceps handle, a forceps arm and a forceps blade connected in sequence; characterized in that it further includes a flexible fiber optic grating sensor group, a cerebral blood flow detection sensor, a processing unit and a host computer;

[0008] The flexible fiber grating sensor group is attached to the inner surface of the clamp blade that contacts the fetal head, and is used to monitor the distribution of clamping force and local temperature applied to the fetal head in real time.

[0009] The brain blood flow detection sensor is a flexible encapsulated capacitive micromechanical ultrasonic transducer patch attached to the inner surface of the clamp blades. It is used to emit ultrasound to the fetal head and receive echo signals to monitor fetal brain blood flow parameters.

[0010] The processing unit is used to receive and process signals from the flexible fiber optic grating sensor group and the cerebral blood flow detection sensor.

[0011] The host computer is communicatively connected to the processing unit and is configured as follows:

[0012] Based on the processed gripping force signal and cerebral blood flow signal, and according to a preset blood flow-gripping force dynamic model, an adaptive safe gripping force threshold that varies with the individual physiological state of the fetus is calculated and generated; and

[0013] The clamping force, temperature, cerebral blood flow parameters, and adaptive safety clamping force threshold are displayed in real time, and an alarm is issued when the clamping force exceeds the threshold.

[0014] Preferably, the flexible fiber Bragg grating sensor group includes a front sensing unit arranged at the front end of the clamp blade and lateral sensing units arranged on both sides of the clamp blade.

[0015] The front-end sensing unit includes a first fiber grating laid in the lower layer and encapsulated in a stainless steel capillary tube, and a second fiber grating laid in the upper layer and in a horizontally curved shape.

[0016] The sensing signal of the first fiber Bragg grating is used for temperature measurement and for temperature compensation of the force sensing signal of the second fiber Bragg grating, so as to achieve force-temperature decoupling.

[0017] Preferably, the lateral sensing unit includes a third fiber grating and a fourth fiber grating horizontally disposed on the left and right sides of the clamp leaf, respectively.

[0018] The second, third, and fourth fiber gratings are arranged in a three-tiered configuration using wavelength division multiplexing, and the clamping force distribution at least three points on the clamp blade is monitored through wavelength demodulation.

[0019] Preferably, the first fiber grating is embedded at a depth of 0.85 mm in the clamp-shaped composite material, the second fiber grating is embedded at a depth of 0.25 mm, and the third and fourth fiber gratings are embedded at a depth of 0.3 mm.

[0020] Preferably, the patch structure of the cerebral blood flow detection sensor includes, from the skin-contact side to the back side, the following layers in sequence: a biocompatible skin contact layer, a CMUT array layer, an encapsulation dielectric layer, a flexible substrate layer, a circuit interconnection layer integrating excitation and signal preprocessing circuits, and a bottom mechanical support layer.

[0021] Preferably, the blood flow-grip force dynamic model is used to calculate the adaptive safety gripping force threshold. Its expression is:

[0022]

[0023] Among them, A c P is the equivalent force-transmitting cross-sectional area of ​​the force-sensing unit. crit The critical clamping pressure affecting fetal cerebral blood flow is Q0, which represents the current baseline fetal cerebral blood flow obtained through monitoring. min,0 β is the preset minimum safe cerebral blood flow threshold, and β is the sensitivity influence coefficient characterizing individual cerebrovascular reactivity. To monitor in real time the rate of change of cerebral blood flow with respect to clamping force.

[0024] Preferably, the baseline cerebral blood flow Q0 and the rate of change The signal from the cerebral blood flow detection sensor was obtained by performing power Doppler analysis and singular value decomposition to denoise the signal, extracting the pure blood flow velocity signal, and then calculating it.

[0025] Preferably, the processing unit includes a data acquisition module and an embedded algorithm module;

[0026] The embedded algorithm module is configured to run a Kalman filter, the state vector X(t) of which is defined as:

[0027] X(t)=[F(t),Q(t),T(t)] T

[0028] Where F(t) is the clamping force, Q(t) is the cerebral blood flow, and T(t) is the local temperature;

[0029] The Kalman filter performs optimal estimation of the state vector through the state-space equation to filter and reduce noise and fuse multi-sensor information, providing input parameters for the blood flow-clamping force dynamic model.

[0030] Preferably, the host computer is further configured to: when the real-time monitored cerebral blood flow Q(t) is lower than the minimum safe cerebral blood flow threshold Q min,0 At that time, the adaptive safety clamping force threshold is dynamically triggered. Recalculation and update.

[0031] On the other hand, the present invention also provides a control method for the intelligent forceps system, comprising the following steps:

[0032] S1: Multimodal signal acquisition: Real-time acquisition of clamping force distribution signal F(t) and temperature signal T(t) through a flexible fiber optic grating sensor group; acquisition of ultrasound echo signal through a cerebral blood flow detection sensor;

[0033] S2: Proprietary signal processing: Perform power Doppler imaging processing on the ultrasound echo signal to locate the fetal cerebral vascular region; perform singular value decomposition on the signal of the fetal cerebral vascular region to suppress tissue clutter and extract the pure cerebral blood flow velocity signal v(t), and then convert it into cerebral blood flow signal Q(t).

[0034] S3: Adaptive safety threshold calculation: Based on the cerebral blood flow signal Q(t) and the clamping force signal F(t), and according to the blood flow-clamping force dynamic model, calculate the adaptive safety clamping force threshold for the specific fetus at the current moment. ;

[0035] S4: Information Fusion and Real-time Interaction: The clamping force signal F(t), temperature signal T(t), cerebral blood flow signal Q(t), and the calculated safety threshold are integrated. The data is displayed and compared in real time on the host computer interface; when the clamping force F(t) at any position exceeds the threshold... At that time, visual and auditory alarms are triggered.

[0036] Compared with existing technologies, the present invention provides an intelligent forceps delivery system and its control method with tactile feedback and cerebral blood flow monitoring. It integrates high-precision distributed force / temperature sensing and flexible ultrasound cerebral blood flow monitoring on the forceps, enabling doctors to be aware of the applied force and its key physiological effects during operation. By establishing a dynamic model of clamping force and cerebral blood flow, the safety threshold is transformed from a fixed value into a variable that is adjusted in real time according to the individual's physiological state (basal blood flow, vascular reactivity), which is more in line with clinical practice and can provide customized safety boundaries for different fetuses, significantly improving safety.

[0037] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0038] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute a limitation thereof.

[0039] Figure 1 This is a schematic diagram of the overall structure of the intelligent delivery forceps of the present invention.

[0040] Figure 2 This is a schematic diagram of the structure of the flexible fiber Bragg grating sensor at the front end of the clamp blade.

[0041] Figure 3 This is a schematic diagram of the structure of the flexible fiber Bragg grating sensor on both sides of the clamp leaf.

[0042] Figure 4 This is an exploded view of the structure of a cerebral blood flow detection sensor.

[0043] Figure 5 The image shows the deformation contours of the FBG at different landfill depths, obtained through finite element simulation.

[0044] Figure 6 The image shows the longitudinal path stress curve of the FBG package obtained through finite element simulation.

[0045] Figure 7 This is a flowchart illustrating the operation of the intelligent forceps tactile sensing system of the present invention.

[0046] Figure 8 This is a flowchart illustrating the workflow of the intelligent forceps delivery cerebral blood flow monitoring system of the present invention.

[0047] Figure 9 This is the overall operation logic diagram of the intelligent delivery forceps host computer system of the present invention. Detailed Implementation

[0048] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] Example

[0050] like Figure 1 As shown, this embodiment provides an intelligent delivery forceps system, whose hardware entities mainly include an intelligent delivery forceps body, a signal processing and transmission unit (not shown separately in the figure, which can be integrated into the forceps handle or externally) and a host computer (workstation or mobile computing device).

[0051] The intelligent forceps body is the core platform of the system. Based on the traditional forceps mechanical structure, it integrates a multimodal sensing system. Its main body includes a forceps handle 101, a forceps arm 102 and a pair of forceps blades 103 that are hinged or connected in sequence. The forceps blades 103 are the parts that directly contact the fetal head. Their inner surface curves are optimized to fit the fetal temporal region, and relevant functional sensors are integrated here.

[0052] Multimodal sensing systems include flexible fiber optic grating tactile sensing systems and flexible ultrasound cerebral blood flow monitoring systems.

[0053] The flexible fiber grating tactile sensing system is used for real-time, distributed monitoring of the clamping mechanical state and local temperature between the forceps blades and the fetal head. Its core component is the flexible fiber grating sensor group 2; such as... Figure 2 and Figure 3 As shown, the sensor group 2 is arranged differently according to the functional area of ​​the clamp leaf 103; the front-end sensing unit is attached to the arc-shaped area at the front end of the clamp leaf 103, which corresponds to the anterior fontanelle or the anterior temporal lobe of the fetus and is the core position for monitoring key force application points. This unit adopts a unique double-layer FBG structure.

[0054] The fiber grating of the lower FBG1 is sealed within a thin stainless steel capillary tube 4, which is then embedded in a flexible encapsulation (such as medical silicone). The stainless steel capillary tube 4 has high rigidity, effectively isolating external pressure from being transmitted to the internal FBG1, making it almost unresponsive to mechanical strain, thus dedicated to temperature measurement. At the same time, the tube encapsulation also provides physical protection. The fiber grating of the upper FBG2 is laid in a horizontally curved (arc) shape on the upper layer of the encapsulation, directly attached to the upper surface of the encapsulation. This curved structure makes it extremely sensitive to curvature changes in the encapsulation caused by stress, transforming traditional axial strain measurement into more sensitive bending strain measurement, significantly improving the force sensing sensitivity and linearity under micro-biomechanical influences.

[0055] Lateral sensing units are attached to the left and right sides of the clamp leaf 103 (corresponding to the fetal temporal region), such as Figure 3 As shown; each side uses a single FBG laid horizontally in a straight line, namely FBG3 on the left and FBG4 on the right; FBG2, FBG3, and FBG4 are connected in series on a single optical fiber through wavelength division multiplexing technology to form a three-tiered layout. By demodulating their independent Bragg wavelength drift, synchronous monitoring of the clamping force at three different positions can be achieved.

[0056] The performance of the sensor is closely related to the embedding depth of the FBG in the flexible package (silicone); through finite element simulation analysis (material parameters are shown in Table 1), a 5N load is applied to the surface of the package to study the mechanical transmission efficiency at different embedding depths. Figure 5 Strain contour maps at different depths were displayed. Figure 6The stress curves quantitatively show that as the burial depth increases, the stress transmitted to the FBG attenuates, and the sensitivity decreases; however, too shallow a burial depth may affect durability. Based on the simulation results, an optimal burial depth scheme was determined to balance the requirements of sensitivity, reliability, and temperature compensation.

[0057] The lower layer temperature measurement FBG1 at the front end of the clamp blade (encased in a steel tube) has a burial depth of 0.85mm. The deeper burial further ensures its mechanical isolation, making its signal reflect temperature purely.

[0058] Upper force measuring unit FBG2 at the front end of the clamp blade: buried at a depth of 0.25mm. The shallow burial ensures that it can efficiently sense contact pressure and achieve high-sensitivity measurement.

[0059] Force measuring devices FBG3 / FBG4 on both sides of the clamp blades: burying depth is 0.30mm. Provides reliable lateral force monitoring.

[0060] Table 1

[0061]

[0062] Force-temperature decoupling and measurement principle:

[0063] FBG center wavelength drift Δλ B Simultaneously affected by strain ε and temperature change ΔT, it can be expressed as:

[0064]

[0065] in, ξ is the elasto-optic coefficient, and ξ is the thermo-optic coefficient. K is the coefficient of thermal expansion. ε and K T These are the strain and temperature sensitivity coefficients, respectively.

[0066] For the front-end sensing units, FBG1 (temperature measurement) and FBG2 (force measurement) are in almost the same temperature field. Let their wavelength drifts be Δλ1 and Δλ2, respectively. Since FBG1 is isolated by the steel pipe, we can assume that Δλ1≈K. T1 If ΔT is the wavelength drift of FBG2, the portion caused purely by force can be decoupled using the finite difference method:

[0067]

[0068] Decoupled Δλ 2F The clamping force is proportional to the applied force F, and the accurate clamping force can be calculated using a pre-calibrated coefficient; the lateral FBG3 / FBG4 can also be temperature compensated using FBG1 or its own historical baseline.

[0069] The flexible ultrasound cerebral blood flow monitoring system is used for non-invasive, real-time monitoring of fetal intracranial hemodynamics during forceps delivery. Its core component is the cerebral blood flow detection sensor 3, which is a flexible CMUT patch. For example... Figure 4 As shown, the flexible patch adopts a multi-layer stacked structure, which consists of a skin contact layer 301, a CMUT array layer 302, an encapsulation dielectric layer 303, a flexible substrate layer 304, a circuit interconnect layer 305, and a bottom support layer 306, arranged from the contact surface downwards.

[0070] The skin contact layer 301 can be made of medical-grade silicone to ensure biocompatibility, acoustic impedance matching, and protection of the underlying structure. The CMUT array layer 302 can be integrated in a two-dimensional array of tens to hundreds of micron-sized capacitive ultrasonic micro-elements on a flexible substrate. Each micro-element can vibrate and emit ultrasonic waves under voltage drive and receive echoes to generate electrical signals. The preferred operating frequency is 8-10MHz to balance penetration into the neonatal skull and blood flow detection resolution. The encapsulation dielectric layer 303 protects the CMUT array from moisture and mechanical damage and provides electrical insulation. The flexible substrate layer 304 can be made of polyimide, giving the entire patch good flexibility so that it can conform to the curved clamp blade surface. The circuit interconnection layer 305 integrates high-voltage pulse transmitting circuits, low-voltage receiving amplification circuits, multiplexing switches, and analog-to-digital converters to realize signal excitation, primary processing, and digitization. The bottom support layer 306 provides necessary mechanical support and strength and includes external interfaces.

[0071] Blood flow detection can be performed using the following principle: CMUT emits ultrasound waves towards the fetal head. When the ultrasound waves encounter flowing red blood cells, the echo signal will experience a Doppler frequency shift (f). D According to the Doppler equation, the blood flow velocity v is:

[0072]

[0073] Where c is the speed of sound in the tissue, f0 is the emission frequency, and θ is the angle between the sound beam and the direction of blood flow.

[0074] As a capacitive transducer, the CMUT receives a signal, and the diaphragm vibration causes a change in capacitance, generating an induced current I(t) under a DC bias voltage VDC. Existing theoretical derivations show that this current signal is related to the diaphragm vibration velocity. Proportional, and Ultimately, it is related to the Doppler frequency shift f D The correlation is such that the blood flow velocity can be calculated by detecting the current signal.

[0075] Due to the complex intracranial monitoring environment of the fetus, including strong tissue clutter, bone echoes, and interference from the forceps itself, it is extremely difficult to directly extract weak blood flow signals. This method combines power Doppler imaging and singular value decomposition for noise reduction.

[0076] Power Doppler vessel localization involves beamforming the multi-channel echo signals received by the CMUT array to obtain a series of time-series data s(r,n) for each scan line; then, the power spectral density P(r) at each position r is calculated.

[0077]

[0078] The power of scattering particles (red blood cells) in blood flow regions (within blood vessels) is significantly higher than that in static tissues. By setting a threshold P, the image is binarized, and regions where P(r) > P threshold are initially identified as high-power areas. Further analysis, including morphological processing (screening for slender structures) and pulsation frequency analysis (compared to the known fetal heart rate f), is then performed. fetal heart Perform a correlation test, corr(f) blood ,f fetal heart (>τ), precisely locate the target blood vessel area.

[0079] SVD denoising to extract pure blood flow signals involves constructing a space-time data matrix D from multiple frames of echo data. Singular value decomposition is then performed on this matrix.

[0080]

[0081] Here, Σ is a diagonal singular value matrix, with values ​​arranged in descending order. The first few large singular values ​​typically correspond to strongly correlated tissue clutter and background noise; several middle singular values ​​correspond to blood flow signals; and the numerous small singular values ​​at the end correspond to random noise. By adaptively selecting a threshold, the large singular values ​​representing tissue clutter are set to zero, and then the remaining singular values ​​are used to reconstruct matrix D. filt :

[0082]

[0083] From D filt This allows for the extraction of a pure blood flow signal with a significantly improved signal-to-noise ratio, free from major static and low-velocity tissue interference, and thus enables accurate calculation of the Doppler frequency shift f. D and mean blood flow velocity v; combined with preset or estimated vessel cross-sectional area A v The cerebral blood flow is obtained as Q(t) = v(t) * A. v .

[0084] The adaptive safety threshold model and its calculation are the core intelligent decision-making algorithm of this method, aiming to break the limitations of fixed thresholds. This model is based on the dynamic relationship between blood flow and clamping force. It is assumed that cerebral blood vessels have linear compliance characteristics under external pressure, and their blood flow Q(t) is related to the applied clamping pressure P(t) (P(t) = F(t) / A). c A c The relationship (for the equivalent force transmission area) can be simplified to:

[0085]

[0086] Where Q0 is the baseline cerebral blood flow without pressure, P crit It is the critical pressure that causes a significant decrease in cerebral blood flow.

[0087] To ensure fetal safety, Q(t) > Q must be satisfied. min Q min The minimum safe blood flow rate; substituting into the above formula, we can obtain the static safety force F. safe =A c P crit (1−Q min / Q0).

[0088] To reflect individual differences, this method will use Q min Dynamic, defining the sensitivity of blood flow to force The higher the sensitivity (S), the more sensitive the fetal cerebral blood vessels are to compression and the worse their tolerance; therefore, the actual required safety margin should be larger. Accordingly, the minimum safe blood flow rate is revised as follows:

[0089]

[0090] Among them, Q min,0 It is the general basic safety threshold, and β is the sensitivity impact coefficient (which needs to be clinically calibrated).

[0091] Dynamic Q min Substituting (t) into the static safety force formula yields the adaptive safety clamping force threshold. :

[0092]

[0093] The model shows that the safety threshold It is not a fixed value, but rather varies with the fetal baseline blood flow Q0 and the sensitivity of blood flow to force. Dynamic adjustment; a decrease in Q0 or an increase in sensitivity will cause the safety threshold to be automatically lowered, providing more conservative protection.

[0094] Q0 is obtained by averaging the cerebral blood flow signal Q(t) before the gripping operation begins or during the stable phase of the force application interval. Within a short time window (e.g., a few seconds), a linear regression is performed on the synchronously acquired clamping force F(t) and cerebral blood flow Q(t) data, and the slope of the regression is the real-time estimate of the sensitivity.

[0095] To improve system robustness, force, blood flow, and temperature signals are fused and optimally estimated, defining the system state vector X(t)=[F(t),Q(t),T(t)]. T Establish a state-space model:

[0096] Equations of state:

[0097] Where A is the state transition matrix, which reflects the coupling relationship between force, blood flow, and temperature; The input is the doctor's input; W(t) is the process noise.

[0098] Observation equation: Y(t) = HX(t) + V(t)

[0099] in, t represents sensor observations (wavelength drift, Doppler shift, direct temperature measurement); H represents the observation matrix; V(t) represents the observation noise.

[0100] The Kalman filter recursively performs prediction and update, using the observed value Y(t) to obtain the optimal estimate of the state vector X(t). Filtered output and It is smoother and more resistant to interference than the original signal, and is used for subsequent display and threshold judgment.

[0101] Combination Figure 7 , Figure 8 and Figure 9 The workflow of the entire intelligent birthing forceps system can be summarized as follows:

[0102] Step 1: Initialization and Clamping

[0103] The system powers on, the host computer software starts, and connections are established with the control center, FBG demodulator, and embedded processing unit. The obstetrician inserts the intelligent forceps into the birth canal as usual, ensuring the forceps blades 103 align with the temporal regions of the fetal head, and the flexible CMUT patch is aligned with the fetal temporal windows and other ultrasound-transmitting windows. In the initial stage, before traction begins but after secure placement, the system automatically collects and records baseline signals: clamping force baseline (close to zero), local temperature baseline T0, and most critically, baseline cerebral blood flow Q0, completing individualized initial calibration.

[0104] Step 2: Synchronous Acquisition and Preprocessing of Multimodal Signals

[0105] The FBG demodulator demodulates the wavelengths of FBG1-FBG4 in real time at a high frequency (e.g., 1kHz). After the data is transmitted to the host computer, the temperature compensation module uses the data from FBG1 to decouple the signals of FBG2, FBG3, and FBG4, calculates the real-time pressure at each monitoring point, and synthesizes it into a total clamping force F(t), while simultaneously outputting the temperature T(t). The embedded processing unit drives the CMUT array to emit ultrasonic pulses and receive the echoes. After preliminary amplification, filtering, and digitization, the RF data stream is sent to the host computer.

[0106] Step 3: Advanced Signal Processing and Feature Extraction

[0107] The host computer's data processing unit executes a proprietary blood flow extraction algorithm on the ultrasound radio frequency data: first, beamforming and power Doppler imaging are performed to locate the fetal cerebral blood vessels; then, SVD decomposition and filtering are performed on the vascular region signal to suppress tissue clutter, and finally, the pure cerebral blood flow velocity v(t) is extracted, and the blood flow rate Q(t) is calculated.

[0108] Short-time analysis of F(t) and Q(t) is performed to estimate the baseline blood flow Q0 and sensitivity at the current moment in real time. .

[0109] Step 4: Adaptive Security Decision Making and Information Fusion

[0110] Real-time estimated Q0 and Together with the preset model parameters (A) c ,P crit Q min,0 Substituting β into the adaptive safety threshold model, the personalized safety clamping force threshold at the current moment is calculated. .

[0111] Run the Kalman filter to fuse the observations of F(t), Q(t), and T(t) and output the optimal estimated state. .

[0112] Step 5: Real-time display, alerts, and interaction

[0113] The host computer display interface can dynamically update the following information:

[0114] Main curve area: Displays the filtered clamping force side-by-side as trend curves. and dynamically changing adaptive safety threshold line The difference between the two is obvious.

[0115] Blood flow monitoring area: Displays cerebral blood flow spectrum waveform, average velocity v(t), and blood flow. curve.

[0116] Parameter panel: Digital display of real-time temperature The estimated Q0 and wait.

[0117] Force distribution diagram: The pressure distribution at each FBG monitoring point on the blade is displayed intuitively in the form of a heat map.

[0118] Security monitoring and alarms: continuous comparison from the control center and .once Immediately trigger visual alarms (such as the threshold line turning red or the curve area flashing) and auditory alarms; simultaneously monitor cerebral blood flow. If it is lower than the absolute safety lower limit Q min,0 Regardless of the clamping force, an emergency alarm will be triggered, and a lower value may be forced to be recalculated. .

[0119] Step Six: Data Recording and Review

[0120] All raw data, processed parameters, and alarm events during the entire operation are recorded synchronously by the system; after the operation, a complete report can be generated for medical record archiving, postoperative review, teaching, and clinical research.

[0121] The key parameters involved in the adaptive threshold model in this method can be determined using the following method: P crit Critical pressure (β) and sensitivity coefficient (β) are core physiological parameters of the model and need to be calibrated through preliminary animal experiments (such as fetal sheep models) or statistical analysis of a large amount of retrospective clinical data. For example, under controlled conditions, the pressure on the experimental subject's head is gradually increased while simultaneously monitoring changes in cerebral blood flow to determine the pressure range that causes a significant decrease in blood flow, and a P value including a safety margin is set based on expert experience. crit Values ​​(e.g., 20-30 mmHg); the β value reflects the degree of influence of sensitivity on the safety threshold, and can be obtained by fitting and optimizing experimental data using machine learning methods to obtain an initial value. Q min,0 (Minimum safe blood flow) is a conservative, universally accepted threshold determined based on literature research on neonatal physiology and cerebral ischemia. A c The equivalent force transmission cross-sectional area is determined by the FBG sensor's package size, material properties, and specific layout on the blades through mechanical simulation or physical calibration experiments. Clinically adjustable parameters include the threshold P-threshold and the correlation threshold τ, which can be jointly adjusted by engineers and physicians during clinical trials to achieve optimal vascular detection and signal extraction results.

[0122] In summary, this embodiment details an intelligent forceps delivery system integrating flexible FBG tactile sensing and flexible CMUT cerebral blood flow monitoring, along with its operating method. Through innovative sensor structure and layout, targeted blood flow signal extraction algorithms, and, most importantly, an adaptive safety threshold model based on physiological feedback, this intelligent forceps delivery system achieves a leap in forceps delivery operations from experience-dependent to data-driven, and from static alarms to dynamic individualized protection. This system provides obstetricians with unprecedented quantitative information and intelligent decision support, and is expected to significantly reduce the risk of forceps delivery-related complications.

[0123] Finally, it should be noted that this article uses specific examples to illustrate the principles and implementation methods of the present invention. The above description of the embodiments is only for the purpose of helping to understand the core ideas of the present invention. Without departing from the principles of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the protection scope of the present invention.

Claims

1. A smart forceps delivery system with tactile feedback and cerebral blood flow monitoring, comprising a forceps body, said forceps body including a forceps handle, forceps arms, and forceps blades connected in sequence; characterized in that, It also includes a flexible fiber Bragg grating sensor array, a cerebral blood flow detection sensor, a processing unit, and a host computer; The flexible fiber grating sensor group is attached to the inner surface of the clamp blade that contacts the fetal head, and is used to monitor the distribution of clamping force and local temperature applied to the fetal head in real time. The brain blood flow detection sensor is a flexible encapsulated capacitive micromechanical ultrasonic transducer patch attached to the inner surface of the clamp blades. It is used to emit ultrasound to the fetal head and receive echo signals to monitor fetal brain blood flow parameters. The processing unit is used to receive and process signals from the flexible fiber optic grating sensor group and the cerebral blood flow detection sensor. The host computer is communicatively connected to the processing unit and is configured as follows: Based on the processed gripping force signal and cerebral blood flow signal, an adaptive safe gripping force threshold that varies with the individual physiological state of the fetus is calculated and generated according to a preset blood flow-gripping force dynamic model. and The clamping force, temperature, cerebral blood flow parameters, and adaptive safety clamping force threshold are displayed in real time, and an alarm is issued when the clamping force exceeds the threshold.

2. The intelligent forceps delivery system with tactile feedback and cerebral blood flow monitoring according to claim 1, characterized in that: The flexible fiber optic grating sensor group includes a front sensing unit arranged at the front end of the clamp blade and lateral sensing units arranged on both sides of the clamp blade. The front-end sensing unit includes a first fiber grating laid in the lower layer and encapsulated in a stainless steel capillary tube, and a second fiber grating laid in the upper layer and in a horizontally curved shape. The sensing signal of the first fiber Bragg grating is used for temperature measurement and for temperature compensation of the force sensing signal of the second fiber Bragg grating, so as to achieve force-temperature decoupling.

3. The intelligent forceps delivery system with tactile feedback and cerebral blood flow monitoring according to claim 2, characterized in that: The lateral sensing unit includes a third fiber grating and a fourth fiber grating, which are horizontally laid on the left and right sides of the clamp leaf, respectively. The second, third, and fourth fiber gratings are arranged in a three-tiered configuration using wavelength division multiplexing, and the clamping force distribution at least three points on the clamp blade is monitored through wavelength demodulation.

4. The intelligent molybdenum production system with tactile feedback and cerebral blood flow monitoring according to claim 3, characterized in that: The first fiber grating is embedded at a depth of 0.85 mm in the clamp-shaped composite material, the second fiber grating is embedded at a depth of 0.25 mm, and the third and fourth fiber gratings are embedded at a depth of 0.3 mm.

5. The intelligent forceps delivery system with tactile feedback and cerebral blood flow monitoring according to claim 1, characterized in that: The patch structure of the cerebral blood flow detection sensor, from the skin-contact side to the back side, includes: a biocompatible skin contact layer, a CMUT array layer, an encapsulation dielectric layer, a flexible substrate layer, a circuit interconnect layer integrating excitation and signal preprocessing circuits, and a bottom mechanical support layer.

6. The intelligent forceps delivery system with tactile feedback and cerebral blood flow monitoring according to claim 1, characterized in that: The blood flow-grip force dynamic model is used to calculate the adaptive safe gripping force threshold. Its expression is: Among them, A c P is the equivalent force-transmitting cross-sectional area of ​​the force-sensing unit. crit The critical clamping pressure affecting fetal cerebral blood flow is Q0, which represents the current baseline fetal cerebral blood flow obtained through monitoring. min,0 β is the preset minimum safe cerebral blood flow threshold, and β is the sensitivity influence coefficient characterizing individual cerebrovascular reactivity. To monitor in real time the rate of change of cerebral blood flow with respect to clamping force.

7. The intelligent forceps delivery system with tactile feedback and cerebral blood flow monitoring according to claim 6, characterized in that: The baseline cerebral blood flow Q0 and rate of change The signal from the cerebral blood flow detection sensor was obtained by performing power Doppler analysis and singular value decomposition to denoise the signal, extracting the pure blood flow velocity signal, and then calculating it.

8. The intelligent forceps delivery system with tactile feedback and cerebral blood flow monitoring according to claim 1, characterized in that: The processing unit includes a data acquisition module and an embedded algorithm module; The embedded algorithm module is configured to run a Kalman filter, the state vector X(t) of which is defined as: X(t)=[F(t),Q(t),T(t)] T Where F(t) is the clamping force, Q(t) is the cerebral blood flow, and T(t) is the local temperature; The Kalman filter performs optimal estimation of the state vector through the state-space equation to filter and reduce noise and fuse multi-sensor information, providing input parameters for the blood flow-clamping force dynamic model.

9. The intelligent forceps delivery system with tactile feedback and cerebral blood flow monitoring according to claim 6, characterized in that: The host computer is also configured to: when the real-time monitored cerebral blood flow Q(t) is lower than the minimum safe cerebral blood flow threshold Q min,0 At that time, the adaptive safety clamping force threshold is dynamically triggered. Recalculation and update.

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