Pathogen microscopic examination skin sampling system

Abstract

The present application discloses a system and method for sampling skin samples for pathogen microscopic examination. The present invention determines the type of sampling tool in the first stage, and determines the target skin position in the second stage; the processor receives the recognition result of the image recognition module and the feedback signal of the sensor, adjusts the control strategy of the robot arm in the first stage, and generates a control signal according to the recognition result and the feedback signal in the second stage; the robot arm receives the control signal and uses the sampling tool to sample the skin lesions of the human body. The scheme of the present invention can greatly improve the accuracy and efficiency of sampling skin samples for pathogen microscopic examination.

Description

Technical Field

[0001] The present invention generally relates to the field of automatic control, and more specifically, to a system and method for obtaining skin samples for microscopic examination of pathogens. Background Art

[0002] At present, the primary or secondary pathogens on the surface of skin diseases are mainly detected manually, such as by doctors or technicians. Pathogens such as fungi, scabies, and trichomonas need to be sampled manually, such as by doctors or technicians, and processed before they can be observed using an optical microscope to find the pathogens.

[0003] Microscopic examination of pathogens, especially fungi, is one of the commonly used and necessary detection methods in dermatology. Most pathogens, such as scabies, lice, and fungi, are highly contagious and can easily lead to cross-infection. If the sampling time is too long or the operator is unskilled or the force is inappropriate, the patient may experience secondary damage or discomfort at the sampling site, and the risk of related infection will also increase. On the other hand, improper sampling may also lead to missed diagnosis or misdiagnosis. Due to the degree of familiarity of the technicians with the professional knowledge of pathogen-related skin diseases, the skin lesions involve multiple parts and lack typical skin lesions, the sampling doctor or technician may choose a small number of non-representative lesions, or there may be losses during the operation due to too few skin lesions in typical parts, which may cause false negative results.

[0004] In the prior art, a robotic arm can be used to collect samples of skin for microscopic examination of pathogens. However, since the robotic arm is not an ideal rigid body, unexpected shaking may occur during movement, such as slight tremors up, down, left, and right. When high-precision control is required, for example, when using a scalpel to collect samples, these mechanical vibrations are unacceptable; in other control situations, such as when using a cotton swab to collect samples, the tolerance for the shaking of the robotic arm is higher. Therefore, how to use the characteristics of different sampling tools and select different solutions to remove shaking has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] In order to at least solve the technical problems described in the above background technology section, the present invention proposes a pathogen microscopic skin sampling system and method. Using the solution of the present invention, different sampling systems adopt different control strategies to improve the control accuracy and efficiency of the robot arm. In view of this, the present invention provides solutions in the following aspects.

[0006] The first aspect of the present invention provides a pathogen microscopic skin sampling system for sampling skin lesions, which includes an image recognition module, a processor, a sensor, a robotic arm, and a sampling tool; the image recognition module is used to determine the type of sampling tool in the first stage, and to determine the target skin position in the second stage; the processor receives the recognition result of the image recognition module and the feedback signal of the sensor, and is used to adjust the control strategy of the robotic arm in the first stage, and to generate a control signal according to the recognition result and the feedback signal in the second stage; the robotic arm receives the control signal and uses the sampling tool to sample the skin lesions of the human body; the sensor monitors the mechanical signal of the robotic arm, the mechanical signal includes the mechanical feedback signal generated by the movement of the sampling tool and the tremor signal generated by the movement of the robotic arm, and converts the mechanical signal into an electronic signal and feeds it back to the processor; the control strategy includes selecting different filtering methods according to different sampling tools in the first stage to reduce the interference of the tremor signal on the skin sampling system.

[0007] In one embodiment, the types of sampling tools include invasive tools and non-invasive tools.

[0008] In one embodiment, the control strategy includes: when the invasive tool is used to collect samples, the processor uses an infinite impulse response filter (IIR) type of filtering; when the non-invasive tool is used to collect samples, the processor uses a finite impulse response filter (FIR) type of filtering.

[0009] In one embodiment, the transfer function of the infinite impulse response filter includes:

[0010] ;

[0011] The transfer function of the finite impulse response filter includes:

[0012] ;

[0013] Among them, a 0 、a 1 、b 0 、b 1 、c 0 are the coefficients of the filter.

[0014] In one embodiment, the invasive tool comprises a scalpel; and the non-invasive tool comprises a cotton swab.

[0015] The second aspect of the present invention provides a method for sampling skin samples for pathogen microscopic examination, which is used for sampling skin lesions, including: determining the type of sampling tool in the first stage, and determining the target skin position in the second stage; receiving the recognition result of the image recognition module and the feedback signal of the sensor, adjusting the control strategy of the robotic arm in the first stage, and generating a control signal according to the recognition result and the feedback signal in the second stage; receiving the control signal, and sampling the skin lesions of the human body using the sampling tool; monitoring the mechanical signal of the robotic arm, the mechanical signal including the mechanical feedback signal generated by the movement of the sampling tool and the tremor signal generated by the movement of the robotic arm, and converting the mechanical signal into an electronic signal and feeding it back to the processor; the control strategy includes, in the first stage, selecting different filtering methods according to different sampling tools to reduce the interference of the tremor signal on the skin sampling system.

[0016] In one embodiment, the types of sampling tools include invasive tools and non-invasive tools.

[0017] In one embodiment, the control strategy includes: when the invasive tool is used to collect samples, the processor uses an infinite impulse response filter (IIR) type of filtering; when the non-invasive tool is used to collect samples, the processor uses a finite impulse response filter (FIR) type of filtering.

[0018] In one embodiment, the transfer function of the infinite impulse response filter includes:

[0019] ;

[0020] The transfer function of the finite impulse response filter includes:

[0021] ;

[0022] Among them, a 0 、a 1 、b 0 、b 1 、c 0 are the coefficients of the filter.

[0023] In one embodiment, the invasive tool comprises a scalpel; and the non-invasive tool comprises a cotton swab.

[0024] By using the solution provided by the present invention, different filtering methods are selected for different sampling tools, which, on the one hand, improves the accuracy of the robot arm operation when using invasive tools; on the other hand, when using non-invasive tools, it also avoids unnecessary system performance consumption, thereby improving the control efficiency. The present invention also provides different specific filtering implementation methods for different operating tools. The filter provided by the present invention can maximize the balance between control accuracy and system efficiency, thereby improving the operating efficiency of the overall sampling system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0026] Figure 1 1. It shows a pathogen microscopic skin sampling system according to an embodiment of the present invention;

[0027] Figure 2 The present invention shows a method for obtaining skin samples for microscopic examination of pathogens according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0029] It should be understood that the terms "first", "second", "third" and "fourth" etc. in the claims, specifications and drawings of the present invention are used to distinguish different objects rather than to describe a specific order. The terms "include" and "comprise" used in the specification and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their collections.

[0030] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the claims, the singular forms of "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in the specification of the present invention and the claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0031] As used in this specification and claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0032] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0033] A first aspect of the present invention provides a skin sampling system for pathogen microscopic examination. Figure 1 is a schematic diagram showing a pathogen microscopic skin sampling system according to an embodiment of the present invention, the pathogen microscopic skin sampling system of the present invention comprises an image recognition module, a processor, a sensor, a robotic arm, and a sampling tool;

[0034] The image recognition module is used to determine the type of the sampling tool in the first stage and to determine the target skin position in the second stage;

[0035] The processor receives the recognition result of the image recognition module and the feedback signal of the sensor, and is used to adjust the control strategy of the robot arm in the first stage, and generate a control signal according to the recognition result and the feedback signal in the second stage;

[0036] The robotic arm receives the control signal and uses the sampling tool to sample the skin lesions of the human body;

[0037] The sensor monitors the mechanical signal of the robotic arm, wherein the mechanical signal includes a mechanical feedback signal generated by the movement of the robotic arm using the material collection tool and a tremor signal generated by the movement of the robotic arm, and converts the mechanical signal into an electronic signal to feed back to the processor;

[0038] The control strategy includes, in the first stage, selecting different filtering modes according to different sampling tools to reduce the interference of the trembling signal on the skin sampling system.

[0039] In the present invention, a robotic arm is used to collect samples of pathogen skin, thereby effectively avoiding the risk of cross infection of medical staff. Different collection tools can be installed at the end of the robotic arm, and driven by the guide system, the collection tools can be moved to the desired position, and can be driven to complete basic collection actions (such as up and down, front and back, left and right swing), and the precision of movement or action meets the requirements.

[0040] In the embodiment of the present invention, the movement of the robot arm is driven by devices including motors, sensors, controllers, etc., under the control of the processor to complete the required actions. At the same time, the state of the robot arm can be fed back to the processor, such as the position, speed and direction of the action, the external force received, etc., so that the robot arm state can be adjusted at any time to meet the requirements.

[0041] Optionally, the system of the present invention has a safety limit or automatic stop function in an unsafe state.

[0042] In the embodiment of the present invention, the image recognition module is used to determine the type of sampling tool in the first stage and the target skin position in the second stage. The image recognition module can be installed at any location where the sampling tool and skin condition can be observed, such as the end of the robotic arm. The system automatically identifies the sampling tool used and the area of ​​the patient's affected part to be sampled based on the image captured by the camera, and feeds back to the main control system.

[0043] Optionally, the following steps can be used to identify the patient's location: 1. A high-definition camera collects images of the affected area and its surrounding skin; 2. Image segmentation and processing; 3. Extracting feature values ​​(such as red patches or macules, which only change the skin color and are flush with the surrounding skin; papules or plaques, which are lesions that are higher than the skin surface; such as blisters or bullae, which are cavity lesions that contain fluid and are higher than the skin surface; such as scales, which are epidermal stratum corneum that is about to fall off or has fallen off); 4. Deep machine learning to extract the image location. It can be understood that image recognition can be performed using any existing image recognition technology.

[0044] In an embodiment of the present invention, since the robotic arm is not an ideal rigid body, unexpected shaking may occur during the movement, such as slight tremors up, down, left, and right. When high-precision control is required, for example, when using a scalpel to obtain materials, these mechanical vibrations will interfere with the processor's judgment of the next specific action, which is unacceptable; in other control situations, such as when using a cotton swab to obtain materials, the tolerance for the shaking of the robotic arm is higher. Therefore, the present invention has built-in different control strategies in the processor for different sampling tools obtained by image recognition to improve the accuracy and efficiency of control.

[0045] The present invention makes full use of the different existing filtering methods to achieve different effects, and uses different control strategies for different sampling scenarios, thereby improving control accuracy when precise operation with a scalpel is required; and reducing system consumption and improving system efficiency when using a cotton swab sampling method with lower precision requirements. Specifically, when the invasive tool is used to sample, the processor uses an infinite impulse response filter (IIR) type for filtering; when the non-invasive tool is used to sample, the processor uses a finite impulse response filter (FIR) type for filtering.

[0046] In a preferred embodiment of the present invention, the transfer function of the infinite impulse response filter includes:

[0047] ;

[0048] The transfer function of the finite impulse response filter includes:

[0049] ;

[0050] Among them, a 0 、a 1 , b 0 , b 1 、c 0 are the coefficients of the filter.

[0051] Figure 2 The method for obtaining samples from skin for microscopic examination of pathogens according to an embodiment of the present invention is shown in FIG. Figure 2 As shown, the method for obtaining samples from skin for microscopic examination of pathogens of the present invention comprises steps: S100-S400:

[0052] S100: determining the type of the sampling tool in the first stage, and determining the target skin position in the second stage;

[0053] S200: receiving a recognition result of the image recognition module and a feedback signal of the sensor, adjusting a control strategy of the robot arm in the first stage, and generating a control signal according to the recognition result and the feedback signal in the second stage;

[0054] S300: receiving the control signal, and using the sampling tool to sample the skin lesion of the human body;

[0055] S400: Monitor the mechanical signal of the robotic arm, the mechanical signal including the mechanical feedback signal generated by the movement of the robotic arm using the material collecting tool and the tremor signal generated by the movement of the robotic arm, and convert the mechanical signal into an electronic signal to feed back to the processor.

[0056] The control strategy includes, in the first stage, selecting different filtering modes according to different sampling tools to reduce the interference of the trembling signal on the skin sampling system.

[0057] In one embodiment, the types of sampling tools include invasive tools and non-invasive tools.

[0058] In one embodiment, the control strategy includes: when the invasive tool is used to collect samples, the processor uses an infinite impulse response filter (IIR) type of filtering; when the non-invasive tool is used to collect samples, the processor uses a finite impulse response filter (FIR) type of filtering.

[0059] In one embodiment, the transfer function of the infinite impulse response filter includes:

[0060] ;

[0061] The transfer function of the finite impulse response filter includes:

[0062] ;

[0063] Among them, a 0 、a 1 , b 0 , b 1 、c 0 are the coefficients of the filter.

[0064] In one embodiment, the invasive tool comprises a scalpel; and the non-invasive tool comprises a cotton swab.

[0065] Although this specification has shown and described a plurality of embodiments of the present invention, it is obvious to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will appreciate many changes, modifications and alternatives without departing from the thought and spirit of the present invention. It should be understood that in the process of practicing the present invention, various alternatives to the embodiments of the present invention described herein may be adopted. The attached claims are intended to define the scope of protection of the present invention, and therefore cover the modular compositions, equivalents or alternatives within the scope of these claims.

Claims

1. A pathogen microscopic skin sampling system, used for sampling skin lesions, characterized in that: Includes image recognition module, processor, sensor, robotic arm, and material collection tool; The image recognition module is used to determine the type of the sampling tool in the first stage and to determine the target skin position in the second stage; The processor receives the recognition result of the image recognition module and the feedback signal of the sensor, and is used to adjust the control strategy of the robot arm in the first stage, and generate a control signal according to the recognition result and the feedback signal in the second stage; The robotic arm receives the control signal and uses the sampling tool to sample the skin lesions of the human body; The sensor monitors the mechanical signal of the robotic arm, wherein the mechanical signal includes a mechanical feedback signal generated by the movement of the robotic arm using the material collection tool and a tremor signal generated by the movement of the robotic arm, and converts the mechanical signal into an electronic signal to feed back to the processor; The control strategy includes, in the first stage, selecting different filtering methods according to different sampling tools to reduce the interference of the tremor signal on the skin sampling system; The types of sampling tools include invasive tools and non-invasive tools; The control strategy includes: When the invasive tool is used to collect the sample, the processor uses an infinite impulse response filter (IIR) type to perform filtering; When sampling using the non-invasive tool, the processor uses a finite impulse response filter (FIR) type filter; The transfer function of the infinite impulse response filter includes: ； The transfer function of the finite impulse response filter includes: ； Among them, a0, a1, b0, b1, and c0 are all coefficients of the filter.

2. The pathogen microscopic skin sampling system according to claim 1, characterized in that: The invasive tool includes a scalpel; the non-invasive tool includes a cotton swab.

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