Method of reducing the rate of false triggering of an electrocautery surgical instrument and system therefor

Abstract

The application discloses a method and system for reducing the false trigger rate of an electrocoagulation surgical instrument, and relates to the technical field of surgical instrument control. The method comprises the following steps: synchronously collecting the operator's fingertip operation of the instrument holding area to obtain a three-dimensional multi-modal operation feature vector containing a contact position, a touch area and a pressing force; inputting the vector into a trained intention discrimination model to output a confidence value corresponding to an intentional operation; discriminating whether the current operation is intentional or unintentional according to the comparison result of the confidence value and a preset threshold; if the operation is determined to be intentional, further determining whether the preset trigger confirmation duration is continuously met; if yes, releasing the power-on instruction, otherwise, intercepting. The application discriminates through multi-modal features, does not rely on a single-dimensional threshold, effectively distinguishes the operation intention before power-on, filters out transient false judgments through the trigger confirmation duration, actively intercepts unintentional operations in advance, and significantly reduces the false trigger rate.

Description

Technical Field

[0001] This invention relates to the field of surgical instrument control technology, and more specifically to a method and system for reducing the false triggering rate of electrocoagulation surgical instruments. Background Technology

[0002] Electrocoagulation surgical instruments are widely used hemostatic instruments in delicate surgeries such as neurosurgery and thyroid surgery. The output of electrocoagulation energy is usually controlled by a foot switch or a hand button. After the surgeon positions the working end of the instrument to the target tissue, he steps on the foot switch or presses the button, and the instrument outputs a high-frequency current.

[0003] However, existing foot-operated / button-based triggering mechanisms have inherent flaws: foot switches or hand-operated buttons are merely binary on / off controllers, only reflecting whether a stepping / pressing action has occurred, and completely unable to sense the operator's hand position while holding the instrument. In clinical practice, operator fatigue leading to adjustments in grip posture, unconsciously tightening the instrument handle, or accidental stepping while highly focused can all accidentally trigger the foot switch. Once triggered, the instrument discharges unconditionally, easily causing accidental electrocoagulation damage to non-target tissues.

[0004] To address the issue of accidental triggering, existing solutions attempt to prevent it through physical limiting structures. For example, Chinese invention patent CN116458992A discloses a bipolar electrocoagulation forceps with nerve detection function. This forceps uses a stopcock that extends between the two handles in nerve detection mode, mechanically limiting the forceps tip from accidentally closing. However, this type of physical protection relies on manual triggering by the surgeon, making it a passive mechanical protection that cannot actively detect accidental triggering based on the surgeon's real-time operating status. Summary of the Invention

[0005] The technical problem this application aims to solve is: how to address the high false trigger rate of existing electrocoagulation surgical instruments, which rely solely on foot pedal or button trigger signals as the energizing criterion and cannot distinguish between intentional operation and unintentional gripping by the surgeon before energizing.

[0006] To address the aforementioned deficiencies in the existing technology, the present invention aims to provide a method for reducing the accidental triggering rate of electrocoagulation surgical instruments, comprising the following steps:

[0007] Simultaneous data collection was performed on the operator's fingertip movements in the instrument grip area at each sampling time. Obtain the three-dimensional multimodal operation feature vectors at the same time. ,in Characterizing time The fingertip contact point, Characterizing time The area of ​​fingertip touch, Characterizing time The pressure applied by the fingertips;

[0008] Based on the aforementioned three-dimensional multimodal operational feature vectors, model input features are constructed. These model input features are then input into a trained intent discrimination model, which outputs time-varying features. This corresponds to the confidence level value of the operator's intentional action.

[0009] Based on the comparison between the confidence level and the preset confidence threshold, it is determined whether the current operation is intentional or unintentional.

[0010] If the operation is determined to be intentional, it is further determined whether the determination result continues to meet the preset trigger confirmation time.

[0011] If the trigger confirmation duration is consistently met, the power-on command is allowed; otherwise, the power-on command is blocked.

[0012] The present invention also provides a system for reducing the accidental triggering rate of electrocoagulation surgical instruments, comprising: a processor; and a memory for storing a computer program; wherein the processor executes the computer program to implement the above-described method.

[0013] Compared to existing technologies, the beneficial effects of this application are as follows: Addressing the high false trigger rate of existing electrocoagulation surgical instruments due to their reliance solely on foot pedal or button trigger signals as the energizing criterion, which fails to distinguish between intentional operation and unintentional gripping before energization, this application constructs a multimodal feature vector by acquiring the three-dimensional features of the surgeon's fingertip contact position, touch area, and pressure intensity. An intent discrimination model outputs the confidence level of intentional operation, which is then binarized with a threshold to determine the operational intent. Furthermore, the determination result is required to continuously meet the trigger confirmation duration before energization is allowed, thus forming a complete decision-making chain of "multimodal feature acquisition—intent discrimination—trigger confirmation—gating release / interception" before actual discharge. Compared to existing technologies that rely solely on the binary on / off signal of the foot pedal / button as the only energizing criterion and are completely unable to perceive the surgeon's operational intent, this application can effectively distinguish between intentional operation and unintentional gripping before energization and filters out momentary misjudgments through the trigger confirmation duration, achieving proactive interception of unintentional operations in advance, thereby significantly reducing the false trigger rate from a perception perspective. Attached Figure Description

[0014] Figure 1 This is a system schematic diagram of the present invention.

[0015] Figure 2 This is a flowchart of the method of the present invention.

[0016] Figure 3 This is a schematic diagram illustrating the intentional / unintentional separability of the present invention in the (L,A,P) three-dimensional feature space.

[0017] Figure 4This is a circuit diagram of the present invention.

[0018] Figure 5 This is a flowchart illustrating the intent determination and power-on gating process of this invention. 3 Detailed Implementation

[0019] As described in the background section, bipolar electrocoagulation forceps typically consist of a double-lobed forceps body (the working end of the forceps tip), a forceps handle extending along its length, an electrode base / high-frequency input interface, and an outer insulating structure. Its operation involves positioning the forceps tip to the target tissue, then a high-frequency generator delivers a high-frequency current to the tissue through the two-lobed forceps body, causing local tissue dehydration and coagulation, thereby achieving hemostasis or precise cutting. In clinical use, the surgeon's grip on the electrocoagulation forceps is mainly concentrated in the transition area between the proximal end of the forceps handle and the electrode base, with the thumb and index finger (sometimes supplemented by the middle finger) forming an enveloping grip resembling a "ring-tiger's mouth." This area not only bears the responsibility for positioning and applying pressure during the entire delicate surgery but also involves frequent postural adjustments and force redistribution.

[0020] Current mainstream electrocoagulation forceps still heavily rely on external triggering sources for energy output authorization: either the surgeon (or their assistant) presses a foot switch to enable the high-frequency path, or a hand-controlled button / finger pressure switch is added to the forceps handle / grip, using a simple Boolean signal of "press = 1 / release = 0" to determine "whether discharge is allowed". Even if some solutions introduce passive mechanical anti-accidental touch measures such as canceling / limiting structures between the forceps handles to reduce the risk in specific working conditions (e.g., mechanically preventing accidental clamping of the two forceps tips in mode switching or detection state), they are essentially still based on the idea of ​​"structural limit + manual switching". They cannot determine in real time whether the finger movements in the grip area are intentional or unintentional gripping / posture adjustment before each possible discharge.

[0021] In view of this, this application, based on the inherent relationship between the existing structural configuration of bipolar electrocoagulation tweezers and the aforementioned trigger control paradigm, proposes an active intent recognition scheme starting from "end-point sensing." This scheme abandons the traditional approach of relying solely on external triggering devices or passive mechanical limiting, instead integrating the sensing unit into the tweezers handle gripping area. By real-time extraction of the operator's fingertip contact position, effective contact area, pressure intensity, and their temporal evolution characteristics, a "sensing-discrimination-authorization" decision-making closed loop is constructed at the front end of the energy output link. Specifically, the system deploys a multi-dimensional sensor array in the gripping area to synchronously collect the fingertip operation state at a preset sampling frequency, acquiring multi-modal feature vectors containing spatial distribution and mechanical information. Then, through intent discrimination model reasoning, a trigger authorization signal is generated to achieve gating management of high-frequency energy output. The specific implementation of this application is described in detail below with reference to the accompanying drawings.

[0022] The system first synchronously senses and samples the operator's fingertip state in the grip area, and then enters the complete chain of multimodal feature construction, intent discrimination model inference, trigger confirmation and gating release. The specific steps are as follows:

[0023] The system synchronously collects data on the operator's fingertip movements within the instrument's grip area. The system uses a preset sampling rate. Discrete acquisition is performed at each sampling time. Obtain the three-dimensional multimodal operation feature vectors at the same time. .in:

[0024] Characterizing time The fingertip contact position (e.g., characterized by the centroid coordinates of the sensing array);

[0025] A(tk) represents the area of ​​the fingertip touching at time tk;

[0026] P(tk) represents the pressure applied by the fingertip at time tk.

[0027] To capture the transient dynamic process at the start of an intentional operation (such as press-to-establishment or area change), the lower limit of the sampling rate is set to... ≥100Hz (i.e., adjacent sampling interval Δt≤10ms).

[0028] Furthermore, before inputting the three-dimensional multimodal operation feature vector X(tk) into the model, a feature preprocessing step is performed: min-max normalization is independently applied to the contact position L(tk), touch area A(tk), and pressure P(tk). This normalization process eliminates individual differences caused by different operators' hand sizes and pressing habits. The specific normalization formula is:

[0029]

[0030] Where i∈{L,A,P}

[0031] Within the design range of this embodiment, the typical physical boundary parameters for each dimension are set as follows: contact position L∈[0,1] (mapped and normalized according to array cell number), touch area A∈[0,200]. The pressure intensity P∈[0,5]N.

[0032] Intent discrimination model reasoning.

[0033] The normalized three-dimensional multimodal operational feature vector Input the trained intent discrimination model, output the time step. Confidence value corresponding to the operator's intentional operation .

[0034] In this embodiment, the intended discrimination model is a 3- to 5-layer fully connected multilayer perceptron (MLP). Let the input be... , No. The forward propagation formula for the hidden layer is: .

[0035] The output layer of the multilayer perceptron is activated by the Sigmoid function and then outputs the confidence level. And satisfy:

[0036]

[0037] Where W(L) is the weight matrix of the output layer, b(L) is the bias vector of the output layer, H(L−1) is the output vector of the last hidden layer, and Y∈(0,1).

[0038] This intent discrimination model possesses multimodal discrimination characteristics: for any two sampling times ti and tj, when the pressing force is equal, i.e., P( )=P( When the contact position L(ti) ≠ L(tj) or the touch area A(ti) ≠ A(tj), the system outputs different confidence values ​​through the intent discrimination model. This allows the algorithm features to be deeply bound to the physical operation features, overcoming the deficiency of traditional single pressure thresholds in distinguishing between "intentional hard press" and "unintentional grip".

[0039] Trigger confirmation and debouncing logic.

[0040] Based on the confidence level Compared with the preset confidence threshold The comparison results determine whether the current action was intentional or unintentional. Typical engineering values ​​are taken as follows: (Can be adjusted between 0.70 and 0.95 according to clinical needs).

[0041] like If the operation is determined to be intentional, then it is further determined whether the determination result continuously meets the preset trigger confirmation duration. .

[0042] Trigger confirmation duration The typical range of values ​​is The system settings confirmation window contains consecutive... Each sampling period, of which .

[0043] The system requires confirmation of continuous [internal flow] within the window. The confidence scores of all sampling points exceed the threshold, meaning the product of the point-by-point discrimination results is 1:

[0044]

[0045] Here, 1(·) is an indicator function, taking the value 1 if the condition is met, and 0 otherwise. If any sampling point within the window falls back to Y≤γ, the product is 0, and the confirmation count is reset to zero and re-accumulated.

[0046] Power-on gate control execution.

[0047] If the trigger confirmation duration is consistently met, the power-on command is granted; otherwise, the power-on command is blocked. This step is completed before the actual discharge. Even if the tweezers tip has contacted the tissue or the tweezers handle has closed, as long as the algorithm determines that there is no intent, no discharge will be made, thus achieving proactive interception in advance.

[0048] Based on Example 1, in order to further ensure surgical safety and ensure that the clamping is fully in place, this example introduces physical clamping force determination and system-level hardware interlocking.

[0049] The step of issuing the power-on command also includes physical clamping force determination. The system calculates the final gating signal Gtk at time tk according to the following formula:

[0050]

[0051] In the formula, FC(tk) represents the real-time tissue clamping force at the end of the instrument at time tk; FS represents the minimum clamping force required for reliable coagulation, and the energizing command is released if and only if the final gating signal Gtk is 1.

[0052] The real-time tissue clamping force FC(tk) does not require an additional force sensor at the tweezers tip. Instead, it is equivalently calculated from the three-dimensional multimodal operation feature vector X(tk). The specific calculation is based on the following mechanical equivalent mapping formula:

[0053]

[0054] In the formula:

[0055] P(tk) is the extracted fingertip pressure intensity, which serves as the dynamic scalar input to the system;

[0056] ϕ(L(tk)) is a preset position-lever mapping function, based on the lever principle, used to convert the extracted contact position L(tk) into an effective input power arm of the instrument operation unit;

[0057] μ is the preset mechanical transmission coefficient of the internal structure of the instrument;

[0058] Ff is the system friction loss compensation constant of the instrument transmission mechanism. Both μ and Ff are determined by the factory calibration of the physical structure of the hinge and connecting rod of the specific instrument. The dimension of μ is [N / N·m], which is [1 / m].

[0059] Minimum clamping force required for reliable sealing The minimum force required to ensure proper clamping and formation of an effective closed loop.

[0060] In addition, this embodiment also includes a system-level hardware interlocking step: receiving a physical enable signal Sfoot sent by an external foot switch, which contains only a Boolean state of 0 or 1; performing a logical AND operation between the power-on command output by the algorithm and the physical enable signal Sfoot; and sending an execution signal to close the high-frequency current output circuit to the underlying relay only when the result of the logical AND operation is true (i.e., the operator steps down and the hand is determined to be an intentional operation).

[0061] To further improve the distinction between instantaneous accidental touches and gradual intentional pressure application, this embodiment adds a temporal feature matrix construction step before inputting features into the model:

[0062] Set a sliding time window of length W (e.g., W=10 frames); extract the multimodal operation feature vectors of the current time tk and the past W−1 historical time points; concatenate the vectors according to the time series to generate a two-dimensional temporal feature matrix M(tk):

[0063]

[0064] The temporal feature matrix M(tk) is flattened into a one-dimensional feature vector (3×W) and then input into a multilayer perceptron or directly into a one-dimensional convolutional neural network (1D-CNN). This enables the model to extract the dynamic changes in the operator's fingertip movements over time (such as the rate of pressure increase ΔP / Δt and the rate of area expansion ΔA / Δt), thereby more accurately determining the intent.

[0065] This embodiment details the construction and training process of the above-mentioned intent discrimination model.

[0066] Training data collection: In simulator or ex vivo tissue training scenarios, collect standardized operation data from experts (such as senior physicians) as positive samples of intentional operation (label set to 1), and collect deviation operation data and unconscious grip data from novices (such as resident physicians) as negative samples of unintentional grip (label set to 0).

[0067] Model Training: A 3- to 5-layer fully connected MLP network is constructed. The cross-entropy loss function is used to calculate the error between the predicted confidence and the true label. Gradient descent optimization algorithms (such as the Adam optimizer) are used for backpropagation to update the weight matrix W and bias vector b in the network. After iterative training with a large number of samples, the model converges and learns the "intentional" and "unintentional" separable boundaries in the multimodal feature space.

[0068] In some embodiments, to demonstrate the feasibility of the multimodal discrimination feature of the present invention, reference is made to... Figure 3 The following two sets of comparative scenarios are given (settings) ):

[0069] Scenario 1 (Intentional Operation): The practitioner presses the touch area with the center of their fingertip, revealing the original features. After normalization The confidence sequence is obtained by training the MLP inference. The values ​​at 10 consecutive sampling points are 0.88, 0.90, 0.89, 0.91, 0.90, 0.92, 0.90, 0.91, 0.93, and 0.92, all greater than 0.85. The product of the indicator functions is 1, which satisfies the release condition, and a power-on release command is sent to the back end.

[0070] Scenario 2 (Unintentional grip): The practitioner unintentionally grips the area with the sides of their fingers, applying the same pressure as in Scenario 1 (both are...). However, the contact area is significantly increased and the position is shifted. Original features After normalization Confidence level obtained from MLP inference It remains stable within the 0.30–0.45 range, never exceeding the 0.85 threshold. When the product of the indicator functions is 0, gating is activated, and power is not allowed.

[0071] The above comparison confirms the core advantage of the present invention: under the same pressing force, the opposite energizing result can be output simply because of the different contact area / position, which completely solves the industry pain point that single-channel pressure triggering is prone to misjudgment.

[0072] In some embodiments, the present invention also provides a system for reducing the accidental triggering rate of electrocoagulation surgical instruments, comprising: at least one processor; and a memory communicatively connected to the processor. The memory stores computer program instructions executable by the processor, which, when executed, implement the method steps as described in any of the above embodiments.

[0073] This system is suitable for controlling the timing of energization of one or more energy surgical instruments, such as bipolar electrocoagulation forceps, ultrasonic scalpels, or electric hooks, and has broad clinical application prospects.

[0074] To facilitate understanding of the methods and systems provided in the embodiments of this application, the background of the embodiments of this application will be introduced before introducing the embodiments of this application.

[0075] The embodiments and functional operations of the subject matter described in this specification can be implemented in the following ways: digital electronic circuits, tangibly implemented computer software or firmware, computer hardware, including the structures disclosed in this specification and their equivalents, or combinations thereof. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, that is, one or more modules of computer program instructions encoded on one or more tangible non-transitory program carriers, for execution by a data processing device or to control the operation of the data processing device.

[0076] Alternatively or additionally, program instructions may be encoded on artificially generated propagation signals, such as machine-generated electrical, optical, or electromagnetic signals, which are then generated as coded information to be transmitted to an appropriate receiver device executed by data processing equipment. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or one or more combinations of the above.

[0077] The processing and logic flows described in this specification can be executed by one or more programmable computers, which execute one or more computer programs by processing input data and generating output to run functions. The processing and logic flows can also be executed by special-purpose logic circuitry, such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits), and the device can also be implemented as special-purpose logic circuitry.

[0078] To transmit interactions with a user, embodiments of the subject matter described in this specification can be implemented on a computer having: a display device, such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user; and a keyboard and a positioning device, such as a mouse or trackball, which the user can use to send input to the computer. Other types of devices can also be used to transmit interactions with the user; for example, feedback provided to the user can be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including sound input, voice input, or tactile input. Additionally, the computer can interact with the user by sending documents to and receiving documents from a device used by the user; for example, by sending a webpage to a web browser on the user's client device in response to a received request from a web browser.

[0079] While this specification contains numerous specific implementation details, these should not be construed as limiting the scope of any invention or the scope of the claims, but rather as descriptions of features that can embody specific embodiments of a particular invention. Specific features described in this specification within the context of an independent embodiment may also be implemented in combination with a single embodiment. Conversely, various features described within the context of a single embodiment may also be implemented independently in multiple embodiments, or in any suitable sub-combination. Furthermore, while features may be described for combination and even initially claimed in this way, one or more features from a claimed combination may be removed from that combination in some cases, and the claimed combination may be redirected to a sub-combination or a variation thereof.

[0080] Similarly, although operations are described in the accompanying drawings in a specific order, it should not be construed as requiring that such operations be performed in the specific order shown or in sequential order, or that all illustrated operations be performed, in order to achieve the desired result. In certain cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that program components and systems can generally be integrated into a single software product or packaged into multiple software products.

[0081] Specific implementations of the subject matter have been described. Other implementations are within the scope of the following claims. For example, the activities described in the claims can be performed in a different order and still achieve the desired result. As an example, the processes described in the drawings do not necessarily require a specific order or sequence to be shown in order to achieve the desired result. In certain implementations, multitasking and parallel processing may be advantageous.

Claims

1. A method for reducing the accidental triggering rate of electrocoagulation surgical instruments, characterized in that, Includes the following steps: Simultaneous sampling of the operator's fingertip movements in the instrument grip area, at each sampling time Obtain the three-dimensional multimodal operation feature vectors at the same time. ,in Characterizing time The fingertip contact point, Characterizing time The area of ​​fingertip touch, Characterizing time The pressure applied by the fingertips; Based on the aforementioned three-dimensional multimodal operational feature vectors, model input features are constructed. These model input features are then input into a trained intent discrimination model, which outputs time-varying features. This corresponds to the confidence level value of the operator's intentional action. Based on the comparison between the confidence level and the preset confidence threshold, it is determined whether the current operation is intentional or unintentional. If the operation is determined to be intentional, it is further determined whether the determination result continues to meet the preset trigger confirmation time. If the trigger confirmation duration is consistently met, the power-on command is allowed; otherwise, the power-on command is blocked.

2. The method for reducing the accidental triggering rate of electrocoagulation surgical instruments according to claim 1, characterized in that, The step of issuing the power-on command also includes determining the physical clamping force, and the system calculates the timing based on the following formula. The final gating signal : In the formula: the trigger confirmation duration includes N consecutive sampling periods; Indicates in The confidence value output at each time step; This refers to the preset confidence threshold; Indicates the instrument tip at Real-time organizational clamping force at any given moment; This indicates the minimum clamping force required for reliable condensation. ( () is an indicator function that takes the value 1 when the condition is true and 0 otherwise; If and only if the final gating signal When the value is 1, the power-on command is granted.

3. The method for reducing the accidental triggering rate of electrocoagulation surgical instruments according to claim 2, characterized in that, The real-time tissue clamping force The three-dimensional multimodal operation feature vector The equivalent calculation is derived based on the following mechanical equivalent mapping formula: In the formula: For the extraction time The pressure applied by the fingertip is used as a scalar quantity of power input to the system; A preset position-lever mapping function is used to extract the contact position. Converted into an effective input power arm for the instrument operating unit; μ is the preset mechanical transmission coefficient of the internal structure of the instrument; This is a constant used to compensate for the system friction loss of the instrument transmission mechanism.

4. The method for reducing the accidental triggering rate of electrocoagulation surgical instruments according to claim 1, characterized in that, The trained intent discrimination model is trained on a sample set containing different contact positions and touch areas to possess multimodal discrimination characteristics: for any two sampling times and When the pressing force is equal, that is... and the contact position or touch area At that time, the system outputs different confidence values ​​through the intent discrimination model; In the discrimination step, based on the difference in confidence value, the opposite result of allowing or blocking the power-on command is output under the same fingertip pressure.

5. The method for reducing the accidental triggering rate of electrocoagulation surgical instruments according to claim 1, characterized in that, The intent discrimination model is a multilayer perceptron. The output layer of the multilayer perceptron is activated by a sigmoid function and outputs the confidence score Y, where Y ∈ (0,1) and satisfies: in, The weight matrix of the output layer is... The bias vector of the output layer. This is the output vector of the last hidden layer.

6. The method for reducing the accidental triggering rate of electrocoagulation surgical instruments according to claim 1, characterized in that, Before inputting the model into the feature input model, a feature preprocessing step is also included: for contact locations Touch area and fingertip pressure A normalization process is performed to eliminate individual differences caused by different operators' hand sizes and pressing habits.

7. The method for reducing the accidental triggering rate of electrocoagulation surgical instruments according to claim 1, characterized in that, It also includes system-level hardware interlocking steps: receiving a physical enable signal from an external foot switch. The physical enable signal contains only Boolean states of 0 or 1; the power-on command that is granted will be combined with the physical enable signal. Perform a logical AND operation; send an execution signal to the underlying relay to close the high-frequency current output loop if and only if the result of the logical AND operation is true.

8. The method for reducing the accidental triggering rate of electrocoagulation surgical instruments according to claim 1, characterized in that, The model input features are time-series feature matrices. Before inputting the model with features, the process includes a time-series feature matrix construction step: setting a sliding time window of length W; extracting the current time step... and the past Multimodal operation feature vectors at each historical moment; concatenated according to the time series to generate a temporal feature matrix. The time-series feature matrix Input the intent discrimination model, which is used to extract the dynamic change features of the operator's fingertip movements in the time dimension, in order to distinguish between instantaneous accidental touches and gradual intentional pressure.

9. The method for reducing the accidental triggering rate of electrocoagulation surgical instruments according to claim 1, characterized in that, The method is applicable to the timing control of energizing one or more energy surgical instruments, such as bipolar electrocoagulation forceps, ultrasonic scalpels, or electric hooks.

10. A system for reducing the accidental triggering rate of electrocoagulation surgical instruments, characterized in that, include: processor; And a memory for storing a computer program; when the processor executes the computer program, it implements the method as described in any one of claims 1 to 9.

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