Portable red light therapeutic instrument

Abstract

The application discloses a portable red light therapeutic instrument, and relates to the field of red light physiotherapy, which comprises an infrared irradiation module, an adjusting module, and a capturing unit.The infrared irradiation module is used for supplying red light according to a preset adjusting scheme, and the adjusting module is used for presetting the adjusting scheme containing the indexes of irradiation intensity and irradiation range, and providing position and posture adjustment for the infrared irradiation module.The capturing unit is used for identifying the state of the current skin area to be treated, and capturing the state data of the skin area to be treated according to preset capturing indexes.Through constructing a treatment planning model and training the model by taking historical treatment data as samples, the model can generate a personalized treatment plan according to the skin state of an individual, and through the image acquisition, data cleaning and state identification links, the state of the skin area to be treated can be monitored in real time, the changes of the skin area in the treatment process can be captured in time, actual feedback can be generated immediately after the treatment, and the treatment scheme can be quickly responded and adjusted.

Description

Technical Field

[0001] This invention relates to the field of red light therapy technology, specifically a portable red light therapy device. Background Technology

[0002] Red light therapy devices are devices that use specific wavelengths of red light for treatment and conditioning. Red light can stimulate cell metabolism and promote cell repair, and is often used for wound healing and postoperative recovery. Red light therapy can relieve pain in muscles, joints and soft tissues, and has a good effect on patients with chronic pain. In sports medicine, it is used for muscle repair and adjunctive treatment of sports injuries in athletes. With the development of technology, portable red light therapy devices have gradually entered the home use market for daily health care and pain relief. They can also be used as part of home beauty care devices, suitable for daily skin care and anti-aging.

[0003] Traditional portable red light therapy typically uses uniform light intensity and treatment cycle parameters to provide the same treatment plan for all patients. However, this cannot meet the needs of individual differences and often lacks the ability to monitor the patient's skin condition and feedback in real time. The treatment effect can only be evaluated in subsequent diagnosis and treatment, lacking the ability to make immediate adjustments and making it difficult to detect problems in treatment in a timely manner. Data collection and analysis are often relatively simple and cannot comprehensively consider multiple influencing factors or effectively utilize historical data. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a portable red light therapy device that can effectively solve the problems of the existing technology.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] This invention discloses a portable red light therapy device, comprising:

[0009] The infrared irradiation module is used to supply red light with varying intensity and range according to a preset adjustment scheme.

[0010] The adjustment module is used to preset the adjustment scheme including the irradiation intensity and irradiation range indicators, and to provide position and attitude adjustment to the infrared irradiation module.

[0011] The capture unit is used to identify the current state of the skin area to be treated and capture the state data of the skin area to be treated according to the preset capture indicators.

[0012] The analysis unit is used to construct a treatment planning model. The treatment planning model takes the current skin area status data as input, outputs the treatment rounds within the preset cycle and the light index of each round of treatment, and outputs the expected treatment feedback after each round. The current treatment effect is compared with the expected treatment feedback.

[0013] The alarm module is used to receive the comparison results from the analysis unit, provide alarm prompts for abnormal states, and stop treatment.

[0014] Furthermore, the capture unit is equipped with sub-modules, including a data collection module, a preprocessing module, a status recognition module, and an indicator capture module. The data collection module and the preprocessing module are interconnected via a wireless network. The preprocessing module and the status recognition module are interconnected via a wireless network. The status recognition module and the indicator capture module are interconnected via a wireless network.

[0015] The data collection module is used to collect images of the user's current skin, actively input skin information, and historical treatment information data;

[0016] The preprocessing module is used to organize and clean the collected data, remove duplicate data, fill in missing values, and calibrate the data to transform it into model training data. At the same time, it normalizes skin images and information data.

[0017] The status recognition module is used to identify the current status of the skin area to be treated in real time based on the skin status image and information data after normalization processing by the preprocessing module.

[0018] The indicator capture module is used to set standard capture indicators and automatically monitor and capture data of the skin area to be treated based on the preset capture indicators.

[0019] Furthermore, the standard capture index attributes of the index capture module include: skin moisture content, sebum secretion, degree of bacterial infection, pigmentation, redness and swelling area, and skin temperature.

[0020] Furthermore, it includes: a model building module, a plan generation module, an expectation generation module, and a feedback comparison module. The model building module is interconnected with the plan generation module and the expectation generation module via a wireless network. The feedback comparison module is also interconnected with the plan generation module and the expectation generation module via a wireless network.

[0021] The model building module uses machine learning algorithms to build a treatment planning model, trains the model using historical treatment data as samples, and performs cross-validation.

[0022] The treatment plan generation module is used to input the current skin region status data into the trained treatment planning model to generate the planned treatment rounds and the illumination indicators for each round, including intensity and range.

[0023] The expected generation module is used to connect with the capture unit to generate the actual feedback for this round based on changes in the skin condition after treatment;

[0024] The feedback comparison module is used to analyze the actual feedback and expected feedback of each round of treatment, calculate the difference, and determine whether the result is within the preset threshold range.

[0025] Furthermore, the feedback comparison module quantitatively compares the actual feedback of each round of treatment with the expected feedback using several indicators, analyzing whether the feedback comparison threshold is met. The quantitative comparison expression is as follows:

[0026] D i =A i -E i ;

[0027]

[0028] In the formula, D i A represents the feedback difference value actually observed after the i-th round of treatment, reflecting the effectiveness of the treatment. i E represents the actual feedback value of the i-th round of treatment, a score for a certain skin sign. i R represents the expected outcome feedback value set before the i-th round of treatment. It is the ideal outcome generated based on historical data and the treatment planning model. i The result of the feedback comparison for the i-th round of treatment is either yes or no, used to determine whether the treatment effect is within the set range. If R i =1, indicating that the effect meets expectations; if R = 1, it means that the effect meets expectations; i =0 indicates that the effect does not meet expectations. T represents the set threshold range, which represents the maximum acceptable difference between the actual effect and the expected effect.

[0029] Furthermore, the calculation logic of the quantitative comparison expression is as follows:

[0030] a. By calculating the feedback difference D in the i-th round of treatment. i By reducing expected feedback E i To obtain actual feedback A i ;

[0031] b. Use judgment conditions to judge feedback differences, and perform absolute value calculations |D i | Determine whether the difference is within the set threshold T range;

[0032] c. Based on the judgments in steps a and b, generate the feedback comparison result R.i ;

[0033] d. If the feedback difference is within the threshold range (|D i If |≤T), then maintain the treatment plan;

[0034] e. If the feedback difference exceeds the threshold range (|D i If |>T), an alarm will be issued and a prompt to adjust the treatment plan will be sent.

[0035] Furthermore, the alarm module and the update module are interconnected via a wireless network, and the update module is interconnected with the analysis unit via a wireless network. After the alarm module is triggered, the treatment rounds and treatment indicators in the current cycle are retrieved, and abnormal factors that do not meet the preset threshold in the comparison results are identified in real time. Available reference features are extracted and fed back to the analysis unit for retraining.

[0036] Furthermore, the infrared irradiation module and the adjustment module are connected via an electrical medium, the infrared irradiation module and the capture unit are interconnected via a wireless network, the capture unit and the analysis unit are interconnected via a wireless network, and the analysis unit and the alarm module are interconnected via a wireless network.

[0037] (III) Beneficial Effects

[0038] Compared with known prior art, the technical solution provided by this invention has the following beneficial effects:

[0039] 1. By constructing a treatment planning model and training it with historical treatment data, the model can generate personalized treatment plans based on individual skin conditions. The data-driven approach based on the user's current skin condition ensures that each user receives appropriate irradiation intensity and range. Through image acquisition, data cleaning, and condition recognition, the condition of the skin area to be treated is monitored in real time, and changes in the skin area during treatment can be captured in a timely manner. Immediate feedback is generated after treatment, helping users and medical personnel to understand the treatment effect in a timely manner, and enabling them to respond quickly and adjust the treatment plan.

[0040] 2. By calculating the difference between actual and expected feedback, the treatment effect can be directly judged by setting a threshold, providing a quantitative basis for clinical decision-making. The treatment effect can be monitored and evaluated through the set threshold range to ensure that phototherapy is both safe and effective, avoiding unnecessary repeated treatments. Through quantitative comparison, the relationship between feedback effect and set threshold can be analyzed to achieve refined management and optimize treatment effect. Abnormal states can be identified in a timely manner, and inappropriate treatment can be stopped through an alarm mechanism, which can greatly improve the safety and reliability of use and avoid potential skin irritation or damage.

[0041] 3. Through a feedback loop mechanism, the system is supported to have self-learning capabilities, enabling continuous iteration and improvement, thereby enhancing the scientific validity and effectiveness of treatment plans. It can extract usable reference features when feedback does not meet preset thresholds and feed them back to the model for retraining, providing more accurate guidance for future treatments and continuously optimizing the treatment planning model. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0043] Figure 1 This is a schematic diagram of the architecture of the present invention.

[0044] The labels in the diagram represent: 1. Infrared irradiation module; 2. Adjustment module; 3. Capture unit; 31. Data collection module; 32. Preprocessing module; 33. Status recognition module; 34. Index capture module; 4. Analysis unit; 41. Model building module; 42. Plan generation module; 43. Expected generation module; 44. Feedback comparison module; 5. Alarm module; 6. Update module. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0046] The present invention will be further described below with reference to embodiments.

[0047] Example 1

[0048] This embodiment provides a portable red light therapy device, such as... Figure 1 As shown, it includes:

[0049] Infrared irradiation module 1 is used to supply red light with irradiation intensity and range according to a preset adjustment scheme, providing precise light conditions for skin treatment and helping to optimize the treatment effect;

[0050] The adjustment module 2 is used to preset the adjustment scheme including irradiation intensity and irradiation range indicators, and provides position and posture adjustment to the infrared irradiation module 1, allowing users to customize the adjustment scheme of irradiation intensity and range. At the same time, it can adjust the position and posture according to the user's skin condition to improve the targeting and effectiveness of treatment.

[0051] The capture unit 3 is used to identify the current state of the skin area to be treated and capture the state data of the skin area to be treated according to the preset capture index.

[0052] The capture unit 3 has sub-modules, including a data collection module 31, a preprocessing module 32, a status recognition module 33, and an indicator capture module 34. The data collection module 31 and the preprocessing module 32 are connected via a wireless network, the preprocessing module 32 and the status recognition module 33 are connected via a wireless network, and the status recognition module 33 and the indicator capture module 34 are connected via a wireless network.

[0053] Data collection module 31 is used to collect images of the user's current skin, actively input skin information, and historical treatment information data to provide a comprehensive skin quality assessment;

[0054] The preprocessing module 32 is used to organize and clean the collected data, delete duplicate data, fill in missing values, and calibrate the data to transform it into model training data. At the same time, it normalizes the skin image and information data to ensure the accuracy of the data. The normalized skin information is beneficial for subsequent analysis.

[0055] The status recognition module 33 is used to identify the status of the current skin area to be treated in real time based on the skin status image and information data normalized by the preprocessing module 32. It uses the processed data to perform real-time status recognition, accurately locate the area to be treated, and improve the targeting of treatment.

[0056] The indicator capture module 34 is used to set standard capture indicators. Based on the preset capture indicators, it automatically monitors and captures data of the skin area to be treated. The standard capture indicator attributes include: skin moisture content, sebum secretion, degree of bacterial infection, pigmentation, redness and swelling area and skin temperature, to ensure that the treatment is based on the actual situation. The standard capture indicators include a variety of skin characteristics to provide a comprehensive skin quality assessment.

[0057] Analysis unit 4 is used to construct a treatment planning model. The treatment planning model takes the current skin area status data as input, outputs the treatment rounds within the preset cycle and the light index of each round of treatment, and outputs the expected treatment feedback after each round. The current treatment effect is compared with the expected treatment feedback.

[0058] Analysis unit 4 has sub-modules deployed below it, including: model building module 41, plan generation module 42, expectation generation module 43, and feedback comparison module 44. Model building module 41 is interconnected with plan generation module 42 and expectation generation module 43 via a wireless network. Feedback comparison module 44 is interconnected with plan generation module 42 and expectation generation module 43 via a wireless network.

[0059] The model building module 41 uses machine learning algorithms to build a treatment planning model, trains the model using historical treatment data as samples, and performs cross-validation to ensure the model's accuracy and reliability.

[0060] The plan generation module 42 is used to input the current skin region state data into the trained treatment planning model to generate the planned treatment rounds and the light indexes for each round, including intensity and range.

[0061] The expected generation module 43 is used to connect with the capture unit 3 to generate actual feedback for this round based on changes in the skin condition after treatment. The actual feedback is generated in combination with changes in the skin condition after treatment, providing a basis for subsequent treatment adjustments.

[0062] The feedback comparison module 44 is used to analyze the actual feedback and expected feedback of each round of treatment, calculate the difference, and determine whether the result is within the preset threshold range to ensure that the expected effect is met.

[0063] The alarm module 5 is used to receive the comparison results from the analysis unit 4, provide alarm prompts for abnormal states and stop treatment, which can ensure patient safety, avoid the risks caused by incorrect treatment, and ensure real-time monitoring and feedback mechanisms during the treatment process.

[0064] The alarm module 5 and the update module 6 are connected via a wireless network. The update module 6 and the analysis unit 4 are also connected via a wireless network. After the alarm module 5 is triggered, the treatment rounds and treatment indicators in the current cycle are retrieved, and abnormal factors that do not meet the preset thresholds in the comparison results are identified in real time. Available reference features are extracted and fed back to the analysis unit 4 for retraining. When abnormal situations occur, the device can extract available reference features and feed them back to the model for retraining, providing more accurate guidance for future treatments.

[0065] As one implementation method in this embodiment, such as Figure 1 As shown, the infrared irradiation module 1 and the adjustment module 2 are connected via an electrical medium. The infrared irradiation module 1 and the capture unit 3 are connected via a wireless network. The capture unit 3 and the analysis unit 4 are connected via a wireless network. The analysis unit 4 and the alarm module 5 are connected via a wireless network.

[0066] Compared with existing technologies, this system significantly improves the safety and effectiveness of treatment through features such as personalization, real-time monitoring, intelligent optimization, and anomaly alarms. Its design philosophy revolves around enhancing user experience and treatment outcomes. From red light supply to real-time status monitoring, and then to treatment planning and feedback, it forms a complete, closed-loop treatment system that greatly improves the effectiveness and safety of treatment. At the same time, by utilizing machine learning and automated data processing capabilities, it continuously optimizes treatment plans to ensure that the user's treatment experience and outcomes are continuously improved.

[0067] Example 2

[0068] At other levels, this embodiment also provides a calculation method for quantitative comparison analysis. The feedback comparison module 44 performs a quantitative comparison of several indicators between the actual feedback and the expected feedback of each round of treatment, and analyzes whether the feedback comparison threshold is met. The quantitative comparison expression is as follows:

[0069] D i =A i -E i ;

[0070]

[0071] In the formula, D i A represents the feedback difference value actually observed after the i-th round of treatment, reflecting the effectiveness of the treatment. i E represents the actual feedback value of the i-th round of treatment, a score for a certain skin sign. i R represents the expected outcome feedback value set before the i-th round of treatment. It is the ideal outcome generated based on historical data and the treatment planning model. i The result of the feedback comparison for the i-th round of treatment is either yes or no, used to determine whether the treatment effect is within the set range. If R i =1, indicating that the effect meets expectations; if R = 1, it means that the effect meets expectations; i =0 indicates that the effect does not meet expectations. T represents the set threshold range, which represents the maximum acceptable difference between the actual effect and the expected effect.

[0072] The calculation logic for the quantitative comparison expression is as follows:

[0073] a. By calculating the feedback difference D in the i-th round of treatment. i By reducing expected feedback E i To obtain actual feedback A i ;

[0074] b. Use judgment conditions to judge feedback differences, and perform absolute value calculations |D i | Determine whether the difference is within the set threshold T range;

[0075] c. Based on the judgments in steps a and b, generate the feedback comparison result R. i ;

[0076] d. If the feedback difference is within the threshold range |D i If |≤T, then maintain the treatment plan;

[0077] e. If the feedback difference exceeds the threshold range |D i If |>T, an alarm will be triggered and a prompt for treatment plan adjustment will be sent;

[0078] Compared with existing technologies, it provides an effective and standardized method to analyze the difference between actual treatment feedback and expected feedback. In clinical practice, it can help users or medical personnel judge the treatment effect and promptly identify and correct situations that do not meet expectations.

[0079] Working principle: The present invention constructs a treatment planning model through the model building module 41 and trains the treatment planning model with historical treatment data as samples. The data collection module 31 identifies the current state of the skin treatment area and filters the collected data through the preprocessing module 32. The state recognition module 33 captures the skin state data to be treated according to the preset capture index. The plan generation module 42 takes the current skin state data as input to the treatment planning model and outputs the treatment rounds within the preset cycle and the light index (range and intensity) of each round of treatment. The expectation generation module 43 outputs the expected feedback of treatment after each round. The adjustment module 2 receives the planned light index and submits it to the infrared irradiation module 1 to execute the treatment.

[0080] After each round of treatment, the feedback comparison module 44 compares the feedback with the preset expected feedback for that round of treatment. If the comparison result meets the threshold, the preset round and light index will continue to be executed. If the comparison result does not meet the threshold, the alarm module 5 will sound an alarm and stop the treatment. Based on the current skin condition, the treatment planning model will redefine the future treatment rounds and treatment indexes within the current cycle and identify abnormal factors that cause the comparison result to not meet the threshold. The update module 6 will extract usable reference features to the treatment planning model for training.

[0081] This invention, through a multi-layered modular design, achieves functions such as intelligent recognition of skin condition, formulation of personalized treatment plans, real-time effect feedback, and early warning of abnormal conditions, providing users with an efficient and safe red light therapy experience. It not only focuses on improving treatment effects but also emphasizes data feedback and continuous optimization, making it highly practical and intelligent, and able to meet the needs of different users.

[0082] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A portable red light therapy device, characterized in that, include: Infrared irradiation module (1) is used to supply red light with irradiation intensity and irradiation range according to a preset adjustment scheme; The adjustment module (2) is used to preset the adjustment scheme including the irradiation intensity and irradiation range indicators, and to provide position and attitude adjustment to the infrared irradiation module (1); The capture unit (3) is used to identify the current state of the skin area to be treated and capture the state data of the skin area to be treated according to the preset capture index. Analysis unit (4) is used to construct a treatment planning model. The treatment planning model takes the current skin area status data as input, outputs the treatment rounds within the preset cycle and the light index of each round of treatment, and outputs the expected treatment feedback after each round. The current treatment effect is compared with the expected treatment feedback. The alarm module (5) is used to receive the comparison results from the analysis unit (4), provide alarm prompts for abnormal states, and stop treatment. The analysis unit (4) has sub-modules deployed below it, including: a model building module (41), a plan generation module (42), an expected generation module (43), and a feedback comparison module (44). The model building module (41) is interconnected with the plan generation module (42) and the expected generation module (43) via a wireless network. The feedback comparison module (44) is interconnected with the plan generation module (42) and the expected generation module (43) via a wireless network. The model building module (41) uses machine learning algorithms to build a treatment planning model, trains the model using historical treatment data as samples, and performs cross-validation. The plan generation module (42) is used to input the current skin region state data into the trained treatment planning model to generate the planned treatment rounds and the light indexes including intensity and range for each round. The expected generation module (43) is used to connect the capture unit (3) to generate the actual feedback for this round based on the changes in the skin condition after treatment; The feedback comparison module (44) is used to analyze the actual feedback and expected feedback of each round of treatment, calculate the difference, and determine whether the result is within the preset threshold range. The feedback comparison module (44) quantitatively compares the actual feedback of each round of treatment with the expected feedback using several indicators, and analyzes whether the feedback comparison threshold is met. Its quantitative comparison expression is as follows: ； ； In the formula, Representing the The observed feedback differences after each round of treatment reflect the effectiveness of the treatment. Representing the The actual feedback value of a round of treatment is a score for a certain skin condition. Representing the The expected outcome values ​​set before each round of treatment are ideal outcomes generated based on historical data and treatment planning models. Representing the The feedback comparison results of each round of treatment are used to determine whether the treatment effect is within the set range. =1 indicates that the effect meets expectations; if A value of 0 indicates that the effect does not meet expectations. This represents the set threshold range, which represents the maximum acceptable difference between the actual effect and the expected effect.

2. The portable red light therapy device according to claim 1, characterized in that, The capture unit (3) has sub-modules deployed below it, including a data collection module (31), a preprocessing module (32), a status recognition module (33), and an indicator capture module (34). The data collection module (31) and the preprocessing module (32) are interconnected via a wireless network. The preprocessing module (32) and the status recognition module (33) are interconnected via a wireless network. The status recognition module (33) and the indicator capture module (34) are interconnected via a wireless network. The data collection module (31) is used to collect images of the user's current skin, actively input skin information, and historical treatment information data; The preprocessing module (32) is used to organize and clean the collected data, delete duplicate data, fill in missing values, and calibrate the data to convert it into model training data. At the same time, it normalizes the skin image and information data. The status recognition module (33) is used to identify the current status of the skin area to be treated in real time based on the skin status image and information data normalized by the preprocessing module (32); The indicator capture module (34) is used to set standard capture indicators and automatically monitor and capture data of the skin area to be treated according to the preset capture indicators.

3. A portable red light therapy device according to claim 2, characterized in that, The standard capture index attributes of the index capture module (34) include: skin moisture content, sebum secretion, degree of bacterial infection, pigmentation, redness and swelling area and skin temperature.

4. A portable red light therapy device according to claim 1, characterized in that, The calculation logic for the quantitative comparison expression is as follows: a. By calculating the first Differences in feedback from round therapy By reducing expected feedback To obtain actual feedback ; b. Use judgment conditions to judge feedback differences, through absolute value calculation. Determine whether the difference is within the set threshold. Within the scope; c. Based on the judgments in steps a and b, generate feedback comparison results. ; d. If the feedback difference is within the threshold range ( If so, then maintain the treatment plan; e. If the feedback difference exceeds the threshold range ( If the condition is not met, an alarm will be triggered and a notification to adjust the treatment plan will be sent.

5. A portable red light therapy device according to claim 1, characterized in that, The alarm module (5) and the update module (6) are connected via a wireless network. The update module (6) and the analysis unit (4) are connected via a wireless network. After the alarm module (5) is triggered, the treatment rounds and treatment indicators in the current cycle are retrieved, and abnormal factors that do not meet the preset threshold in the comparison results are identified in real time. Available reference features are extracted and fed back to the analysis unit (4) to re-participate in the training.

6. A portable red light therapy device according to claim 1, characterized in that, The infrared irradiation module (1) and the adjustment module (2) are connected through an electrical medium. The infrared irradiation module (1) and the capture unit (3) are connected through a wireless network. The capture unit (3) and the analysis unit (4) are connected through a wireless network. The analysis unit (4) and the alarm module (5) are connected through a wireless network.

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