Temporomandibular joint real-time monitoring and behavior intervention device and method based on AI
Through the AI-based temporomandibular joint real-time monitoring device, which uses multimodal sensors and deep learning models, the problems of expensive and single-function existing equipment have been solved, the accuracy of bite force monitoring and real-time intervention have been achieved, and TMD research and clinical application have been promoted.
Patent Information
- Application Number
- CN202511057974.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-30
- Publication Date
- 2025-09-12
AI Technical Summary
Existing bite force detection equipment is expensive, uncomfortable to wear, and has a single monitoring function, making it difficult to be widely promoted in clinical practice.
An AI-based real-time monitoring and behavioral intervention device for the temporomandibular joint was designed, consisting of a headwear component, a monitoring component, and a wearable component. It uses pressure sensors, electromyography sensors, and inertial sensors for multimodal data acquisition, combined with a deep learning model for abnormal behavior identification. It also provides instant warnings through a vibration motor and indicator light, and synchronizes data to a mobile phone app to generate a visual report.
It has achieved a 15% improvement in the accuracy of bite force monitoring, can capture early signs of TMD, provide standardized data to assist clinical treatment, and support real-time monitoring and intervention. The device's lightweight design is suitable for daily wear and has a long battery life.
Smart Images
Figure CN120616539A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of medical equipment, and specifically to an AI-based real-time monitoring and behavior intervention device and method for the temporomandibular joint. Background Art
[0002] Oral health is an important part of the overall health of the human body, and bite force, as a key indicator to measure chewing function, directly affects the individual's nutritional intake and digestive system health. Studies have shown that the bite force of healthy adults varies greatly with gender, age, body shape, and craniofacial morphology, generally ranging from 300 to 500 N. A very small number of adults can have a bite force exceeding 500 N, but the maximum does not exceed 600 N [8]. Personal health factors such as gingivitis, tooth defects, and maxillofacial trauma can also lead to a decrease in individual bite force and degeneration of chewing function, increase the gastrointestinal burden, reduce the body's food intake and nutrient absorption capacity, and lead to malnutrition, anorexia, and a sharp decrease in weight. Therefore, bite force detection and screening are important means of monitoring oral and digestive system health. Therefore, it is necessary to use AI-based temporomandibular joint real-time monitoring and behavioral intervention devices and methods.
[0003] With the continuous progress of society and the rapid development of modern science and technology, people have entered the digital age. At the same time, the concept of digital medicine has gradually become well-known, and related digital medical products and digital technologies are increasingly widely used in healthcare and medical practice. Many cutting-edge medical technologies are associated with the most advanced software and equipment based on the digital industry. The development of artificial intelligence is in the ascendant. In the future, the field of oral medicine is expected to have an artificial intelligence system that integrates prediction, diagnosis, treatment, and prognosis, further benefiting doctors and patients. At present, the application of artificial intelligence in the diagnosis and treatment of TMD is still immature. Research is only based on TMD cases in a single medical unit, and the number of cases based on the research is relatively small. In addition, the data quality control, information feature extraction and reliability of artificial intelligence algorithms in most studies have yet to be verified. At present, there are no standardized bite force detection methods and equipment in clinical practice. There are only a small number of imported products on the market, and most of them are in the laboratory research stage. Existing detection technologies such as strain detection, piezoelectric detection, force-sensitive resistor detection and other electrical measurement technologies, as well as various mechanical detection technologies based on force and transmission, have made certain progress, but most of these devices have problems such as high price, uncomfortable wearing, and single monitoring function, making it difficult to widely promote them in clinical practice. Summary of the Invention
[0004] The purpose of the present invention is to provide an AI-based real-time monitoring and behavioral intervention device and method for the temporomandibular joint, so as to address the problems of the existing devices mentioned in the above background art, such as being expensive, uncomfortable to wear, and having limited monitoring functions, which make them difficult to be widely promoted in clinical practice. To achieve the above purpose, the present invention provides the following technical solutions: an AI-based real-time monitoring and behavioral intervention device and method for the temporomandibular joint, comprising a head wearable device; A monitoring component includes a device frame, the device frame is arranged on one side of the headwear, a monitoring terminal compartment is fixedly connected to the interior of the device frame, a braces connection line is fixedly connected to the side surface of the headwear, the other end of the braces connection line is fixedly connected to the monitoring braces, a pressure sensor is installed inside the monitoring braces, a patch connection line is fixedly connected to the side surface of the headwear, and the other end of the patch connection line is fixedly connected to the mandibular patch; The wearing component includes a fitting groove, the fitting groove is opened on one side of the head wearing piece, and an adjustment belt is provided on one side of the head wearing piece.
[0005] Further preferably, the monitoring component is arranged on one side of the head wearable piece, the wearing component is arranged on one side of the head wearable piece, a prompt light is fixedly connected to one side of the device frame, a vibration motor is installed inside the device frame, a mobile battery is installed inside the device frame, a wiring hole is provided on the top of the device frame, a wireless charging seat is fixedly connected to the bottom of the device frame, and a charging hole is provided on the top of the device frame. By installing the monitoring component, the pressure sensor in the monitoring braces can be used to capture the bite force distribution and intensity in real time, and the myoelectric sensor and micro inertial sensor integrated in the mandibular patch synchronously collect the chewing electromyographic activity and the mandibular movement trajectory. After these data are transmitted to the monitoring terminal warehouse of the device frame, they are time synchronized and feature extracted by a low-power processor, and then deep The deep learning model identifies abnormal behaviors such as clenching teeth and unilateral chewing, and combines user portraits with clinical databases to generate TMD risk assessment results. When an abnormality is detected, the vibration motor and indicator light will immediately issue an alert. At the same time, the data is synchronized to the mobile phone APP to generate a visual health report and push a personalized intervention plan. This design not only improves the accuracy of abnormal behavior recognition by 15% through multimodal fusion and can capture early signs of TMD, but also realizes closed-loop management from passive diagnosis to real-time monitoring intervention. The monitoring braces and mandibular patches are made of flexible materials, with a thickness of less than 3mm and a weight of less than 8g, suitable for daily wear. The device frame integrates a battery and a wireless charging station with a battery life of 72 hours, thereby providing standardized data for oral medicine to assist clinical treatment and scientific research, and promote the iteration of TMD research and AI-assisted diagnosis systems.
[0006] Further preferably, the wearing component also includes a limiting ring, which is fixedly connected to one side of the head wearing piece, and one side of the adjusting strap is fixedly connected with a Velcro, and the adjusting strap is installed on the side surface of the head wearing piece through a fixing block, and the maximum diameter of the adjusting strap is smaller than the inner diameter of the limiting ring, and one side of the device frame is fixedly connected with a fitting block, and the fitting block is adapted to the fitting groove. By installing the wearing component, the adaptation design of the fitting groove and the fitting block can be realized so that the device frame can be quickly installed on one side of the head wearing piece, and the adjusting strap can be adapted to users with different head circumferences through the Velcro, and the limiting ring further fixes the adjusting strap to prevent it from loosening. This structure not only ensures the stability of the monitoring component in dynamic scenarios such as talking and chewing, and avoids signal distortion caused by displacement of the sensor, but also supports quick disassembly and cleaning (the head wearing piece is washable). At the same time, the lightweight design reduces the wearing burden, thereby ensuring reliability for long-term use.
[0007] The method for using the AI-based temporomandibular joint real-time monitoring and behavior intervention device includes the following steps: S1: By installing monitoring components, the pressure sensors in the monitoring braces can be used to capture the distribution and intensity of bite force in real time. The myoelectric sensors and micro-inertial sensors integrated in the mandibular patch synchronously collect chewing electromyographic activity and mandibular movement trajectory. After these data are transmitted to the monitoring terminal compartment of the device frame, the low-power processor performs time synchronization and feature extraction, and then uses a deep learning model to identify abnormal behaviors such as clenching teeth and unilateral chewing. The TMD risk assessment results are generated by combining user portraits and clinical databases. When an abnormality is detected, the vibration motor and prompt light will immediately issue an early warning. At the same time, the data is synchronized to the mobile phone APP to generate a visual health report and push personalized intervention plans. This design not only improves the accuracy of abnormal behavior recognition by 15% through multimodal fusion and can capture early signs of TMD, but also realizes closed-loop management from passive diagnosis to real-time monitoring intervention. The monitoring braces and mandibular patch are made of flexible materials, with a thickness of less than 3mm and a weight of less than 8g, suitable for daily wear. The device frame has an integrated battery and wireless charging base with a battery life of hours.
[0008] S2: By installing the wearing component, the adaptive design of the fitting groove and the fitting block can be realized so that the device frame can be quickly installed on one side of the head wearable component. The adjustment strap can be adapted to users with different head circumferences through Velcro, and the limit ring further fixes the adjustment strap to prevent it from loosening. This structure not only ensures the stability of the monitoring component in dynamic scenarios such as talking and chewing, and avoids signal distortion caused by sensor displacement, but also supports quick disassembly and cleaning. The head wearable component is washable, and the lightweight design reduces the wearing burden.
[0009] Compared with the prior art, the present invention has the following beneficial effects: In this invention, by installing a monitoring component, the pressure sensors within the monitoring braces can be used to capture the distribution and intensity of bite force in real time. The myoelectric sensors and micro-inertial sensors integrated into the mandibular patch simultaneously collect chewing myoelectric activity and mandibular motion trajectory. After this data is transmitted to the monitoring terminal compartment of the device frame, a low-power processor performs time synchronization and feature extraction. A deep learning model then identifies abnormal behaviors such as clenching and unilateral chewing. A TMD risk assessment is generated by combining user profiles with a clinical database. When an abnormality is detected, a vibration motor and indicator light provide an immediate warning. Simultaneously, the data is synchronized to a mobile phone app to generate a visual health report and promote personalized intervention plans. This design not only improves the accuracy of abnormal behavior recognition by 15% through multimodal fusion, enabling the capture of early signs of TMD, but also achieves a closed-loop management system from passive diagnosis to real-time monitoring and intervention. The monitoring braces and mandibular patch are made of flexible materials, are both less than 3mm thick and weigh less than 8g, making them suitable for daily wear. The device frame integrates a battery and wireless charging station with a battery life of 72 hours. This provides standardized data to assist clinical treatment and scientific research in oral medicine, promoting the iteration of TMD research and AI-assisted diagnostic systems.
[0010] In the present invention, by installing the wearing component, the adaptation design of the interlocking groove and the interlocking block can be realized so that the device frame can be quickly installed on one side of the head wearable component. The adjustment belt can be adapted to users with different head circumferences through Velcro, and the limiting ring further fixes the adjustment belt to prevent loosening. This structure not only ensures the stability of the monitoring component in dynamic scenarios such as talking and chewing, avoids signal distortion caused by displacement of the sensor, but also supports quick disassembly and cleaning (the head wearable component is washable). At the same time, the lightweight design reduces the wearing burden, thereby ensuring reliability for long-term use. BRIEF DESCRIPTION OF THE DRAWINGS
[0011] Figure 1 Schematic diagram of the three-dimensional structure of the present invention Figure 1 ; Figure 2 Schematic diagram of the three-dimensional structure of the present invention Figure 2 ; Figure 3 For the present invention Figure 2 A in the middle is an enlarged structural diagram; Figure 4 It is a schematic diagram of a partial three-dimensional structure of the present invention; Figure 5 This is a schematic diagram of the three-dimensional structure of the present invention; Figure 6 For the present invention Figure 5 Schematic diagram of the structure of B.
[0012] In the figure: 1. Head wearable component; 2. Monitoring component; 201. Device frame; 202. Monitoring terminal compartment; 203. Braces connecting line; 204. Monitoring braces; 205. Pressure sensor; 206. Patch connecting line; 207. Mandibular patch; 3. Wearing component; 301. Fitting groove; 302. Adjustment belt; 303. Limiting ring; 304. Velcro; 4. Prompt light; 5. Vibration motor; 6. Mobile battery; 7. Wiring hole; 8. Wireless charging base; 9. Charging hole; 10. Fitting block. DETAILED DESCRIPTION
[0013] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.
[0014] See also Figures 1-6 , the present invention provides a technical solution: an AI-based temporomandibular joint real-time monitoring and behavior intervention device and method, including a head wearable component 1; Monitoring component 2, monitoring component 2 includes a device frame 201, which is set on one side of the head wearable component 1. The interior of the device frame 201 is fixedly connected to a monitoring terminal compartment 202. After the data is transmitted to the monitoring terminal compartment 202 of the device frame 201, a low-power processor performs time synchronization and feature extraction, and then uses a deep learning model to identify abnormal behaviors such as clenching teeth and unilateral chewing. Combined with user portraits and clinical databases, TMD risk assessment results are generated. The side surface of the head wearable component 1 is fixedly connected to a brace. A connecting line 203 is fixedly connected to the other end of the brace connecting line 203 with a monitoring brace 204. A pressure sensor 205 is installed inside the monitoring brace 204. The pressure sensor 205 inside the monitoring brace 204 captures the distribution and intensity of the bite force in real time. A patch connecting line 206 is fixedly connected to the side surface of the headwear 1. The other end of the patch connecting line 206 is fixedly connected to the mandibular patch 207. The mandibular patch 207 integrates an electromyographic sensor and a micro-inertial sensor to synchronously collect chewing electromyographic activity and mandibular movement trajectory; The wearing component 3 includes a fitting groove 301 , which is provided on one side of the headwear component 1 , and an adjustment belt 302 is provided on one side of the headwear component 1 .
[0015] In this embodiment, Figure 1 、 Figure 2 and Figure 3As shown, the monitoring component 2 is arranged on one side of the head wearable component 1, and the wearing component 3 is arranged on one side of the head wearable component 1. A warning light 4 is fixedly connected to one side of the device frame 201, and a vibration motor 5 is installed inside the device frame 201. When an abnormality is detected, the vibration motor 5 and the warning light 4 immediately issue an alarm. At the same time, the data is synchronized to the mobile phone APP to generate a visual health report and push a personalized intervention plan. A mobile battery 6 is installed inside the device frame 201, and a wiring hole 7 is provided on the top of the device frame 201. The bottom of the device frame 201 is fixedly connected to a wireless charging base 8, and a charging port 9 is provided on the top of the device frame 201. This design not only improves the accuracy of abnormal behavior recognition by 15% through multimodal fusion and can capture early signs of TMD, but also realizes closed-loop management from passive diagnosis to real-time monitoring intervention. The monitoring braces 204 and the mandibular patch 207 are made of flexible materials, with a thickness of less than 3 mm and a weight of less than 8 g, suitable for daily wear. The device frame 201 integrates a battery and a wireless charging base 8 with a battery life of 72 hours.
[0016] In this embodiment, Figure 2 、 Figure 3 and Figure 4 As shown, the wearing component 3 also includes a limiting ring 303, which is fixedly connected to one side of the head wearing piece 1, and a Velcro 304 is fixedly connected to one side of the adjustment strap 302. The adjustment strap 302 can adapt to users with different head circumferences through the Velcro 304, and the limiting ring 303 further fixes the adjustment strap 302 to prevent loosening. The adjustment strap 302 is installed on the side surface of the head wearing piece 1 through a fixing block. The maximum diameter of the adjustment strap 302 is smaller than the inner diameter of the limiting ring 303. A fitting block 10 is fixedly connected to one side of the device frame 201, and the fitting block 10 is adapted to the fitting groove 301. The adaptation design of the fitting groove 301 and the fitting block 10 enables the device frame 201 to be quickly installed on one side of the head wearing piece 1. This structure not only ensures the stability of the monitoring component 2 in dynamic scenarios such as talking and chewing, avoids signal distortion caused by displacement of the sensor, but also supports quick disassembly and cleaning (the head wearing piece 1 is washable), and the lightweight design reduces the wearing burden.
[0017] The method for using the AI-based temporomandibular joint real-time monitoring and behavior intervention device includes the following steps: S1: By installing the monitoring component 2, the pressure sensor 205 in the monitoring brace 204 can be used to capture the bite force distribution and strength in real time. The electromyographic sensor and micro-inertial sensor integrated in the mandibular patch 207 synchronously collect the chewing electromyographic activity and mandibular movement trajectory. After these data are transmitted to the monitoring terminal compartment 202 of the device frame 201, the low-power processor performs time synchronization and feature extraction, and then uses the deep learning model to identify abnormal behaviors such as clenching teeth and unilateral chewing. Combined with the user portrait and clinical database, the TMD risk assessment results are generated. When the detection When an abnormality occurs, the vibration motor 5 and the indicator light 4 will give an immediate warning. At the same time, the data will be synchronized to the mobile phone APP to generate a visual health report and push a personalized intervention plan. This design not only improves the accuracy of abnormal behavior recognition by 15% through multimodal fusion and can capture early signs of TMD, but also realizes closed-loop management from passive diagnosis to real-time monitoring intervention. The monitoring braces 204 and the mandibular patch 207 are made of flexible materials with a thickness of less than 3mm and a weight of less than 8g, suitable for daily wear. The device frame 201 integrates a battery and a wireless charging base 8 with a battery life of 72 hours.
[0018] S2: By installing the wearing component 3, the adaptation design of the fitting groove 301 and the fitting block 10 can be realized so that the device frame 201 can be quickly installed on one side of the head wearing component 1. The adjustment belt 302 can be adapted to users with different head circumferences through the Velcro 304, and the limiting ring 303 further fixes the adjustment belt 302 to prevent it from loosening. This structure not only ensures the stability of the monitoring component 2 in dynamic scenarios such as talking and chewing, and avoids signal distortion caused by displacement of the sensor, but also supports quick disassembly and cleaning. The head wearing component 1 is washable, and the lightweight design reduces the wearing burden.
[0019] Usage and advantages of the present invention: The AI-based temporomandibular joint real-time monitoring and behavior intervention device and method, when in use, works as follows: like Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 、 Figure 5 and Figure 6As shown, first, by installing the monitoring component 2, the pressure sensor 205 in the monitoring brace 204 can be used to capture the bite force distribution and intensity in real time. The electromyographic sensor and micro-inertial sensor integrated in the mandibular patch 207 synchronously collect the chewing electromyographic activity and mandibular movement trajectory. After these data are transmitted to the monitoring terminal compartment 202 of the device frame 201, the low-power processor performs time synchronization and feature extraction, and then uses the deep learning model to identify abnormal behaviors such as clenching teeth and unilateral chewing. Combined with the user portrait and clinical database, TMD risk assessment results are generated. When an abnormality is detected, the vibration motor 5 and the prompt light 4 are used to issue an immediate warning. At the same time, the data is synchronized to the mobile phone APP to generate a visual health report and push a personalized intervention plan. This design not only improves the accuracy of abnormal behavior recognition by 15% through multimodal fusion, but also can capture early TMD Signs, it also realizes closed-loop management from passive diagnosis to real-time monitoring intervention, and the monitoring braces 204 and the mandibular patch 207 are made of flexible materials, with a thickness of less than 3mm and a weight of less than 8g, suitable for daily wear. The device frame 201 integrates a battery and a wireless charging stand 8, with a battery life of 72 hours. Then, by installing the wearing component 3, the adaptation design of the fitting groove 301 and the fitting block 10 can be realized so that the device frame 201 can be quickly installed on one side of the head wearable component 1, and the adjustment belt 302 can be adapted to users with different head circumferences through the Velcro 304, and the limiting ring 303 further fixes the adjustment belt 302 to prevent loosening. This structure not only ensures the stability of the monitoring component 2 in dynamic scenarios such as talking and chewing, avoids signal distortion caused by displacement of the sensor, but also supports quick disassembly and cleaning (the head wearable component 1 is washable), and the lightweight design reduces the wearing burden.
[0020] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.
Claims
1. AI-based real-time monitoring and behavioral intervention device for temporomandibular joint, characterized by: It includes a head wearable piece (1); A monitoring component (2), the monitoring component (2) comprising a device frame (201), the device frame (201) being arranged on one side of a head wearable component (1), the interior of the device frame (201) being fixedly connected to a monitoring terminal compartment (202), the side surface of the head wearable component (1) being fixedly connected to a braces connection line (203), the other end of the braces connection line (203) being fixedly connected to a monitoring braces (204), the interior of the monitoring braces (204) being equipped with a pressure sensor (205), the side surface of the head wearable component (1) being fixedly connected to a patch connection line (206), the other end of the patch connection line (206) being fixedly connected to a mandibular patch (207); A wearing component (3), the wearing component (3) comprising an engaging groove (301), the engaging groove (301) being provided on one side of a head wearing piece (1), and an adjusting belt (302) being provided on one side of the head wearing piece (1).
2. The AI-based temporomandibular joint real-time monitoring and behavior intervention device according to claim 1, characterized in that: The monitoring component (2) is arranged on one side of the head wearable component (1), and the wearing component (3) is arranged on one side of the head wearable component (1).
3. The AI-based temporomandibular joint real-time monitoring and behavior intervention device according to claim 1, characterized in that: A warning light (4) is fixedly connected to one side of the device frame (201), and a vibration motor (5) is installed inside the device frame (201).
4. The AI-based temporomandibular joint real-time monitoring and behavior intervention device according to claim 1, characterized in that: A mobile battery (6) is installed inside the device frame (201), and a wiring hole (7) is provided on the top of the device frame (201).
5. The AI-based temporomandibular joint real-time monitoring and behavior intervention device according to claim 1, characterized in that: The bottom of the device frame (201) is fixedly connected to a wireless charging seat (8), and the top of the device frame (201) is provided with a charging hole (9).
6. The AI-based temporomandibular joint real-time monitoring and behavior intervention device according to claim 1, characterized in that: The wearing assembly (3) further comprises a limiting ring (303), wherein the limiting ring (303) is fixedly connected to one side of the head wearing piece (1), and a Velcro (304) is fixedly connected to one side of the adjusting strap (302).
7. The AI-based temporomandibular joint real-time monitoring and behavior intervention device according to claim 1, characterized in that: The adjusting belt (302) is mounted on the side surface of the headwear piece (1) via a fixing block, and the maximum diameter of the adjusting belt (302) is smaller than the inner diameter of the limiting ring (303).
8. The AI-based temporomandibular joint real-time monitoring and behavior intervention device according to claim 1, characterized in that: A fitting block (10) is fixedly connected to one side of the device frame (201), and the fitting block (10) is adapted to the fitting groove (301).
9. A method for using an AI-based temporomandibular joint real-time monitoring and behavior intervention device, characterized in that: The following steps are involved: S1: By installing the monitoring component (2), the pressure sensor (205) in the monitoring brace (204) can be used to capture the bite force distribution and strength in real time. The electromyographic sensor and micro-inertial sensor integrated in the mandibular patch (207) synchronously collect the chewing electromyographic activity and mandibular movement trajectory. After these data are transmitted to the monitoring terminal compartment (202) of the device frame (201), the low-power processor performs time synchronization and feature extraction, and then uses the deep learning model to identify abnormal behaviors such as clenching teeth and unilateral chewing. Combined with the user portrait and clinical database, the TMD risk assessment results are generated. When the detection When an abnormality occurs, the vibration motor (5) and the warning light (4) will give an immediate warning. At the same time, the data will be synchronized to the mobile phone APP to generate a visual health report and push a personalized intervention plan. This design not only improves the accuracy of abnormal behavior recognition by 15% through multimodal fusion and can capture early signs of TMD, but also realizes closed-loop management from passive diagnosis to real-time monitoring intervention. The monitoring braces (204) and mandibular patch (207) are made of flexible materials with a thickness of less than 3mm and a weight of less than 8g, suitable for daily wear. The device frame (201) integrates a battery and a wireless charging base (8) with a battery life of 72 hours. 10.S2: By installing the wearing component (3), the fitting groove (301) and the fitting block (10) can be adapted to each other so that the device frame (201) can be quickly installed on one side of the head wearing component (1). The adjustment belt (302) can be adapted to users with different head circumferences through the Velcro (304), and the limiting ring (303) further fixes the adjustment belt (302) to prevent it from loosening. This structure not only ensures the stability of the monitoring component (2) in dynamic scenes such as talking and chewing, and avoids signal distortion caused by displacement of the sensor, but also supports quick disassembly and cleaning (the head wearing component (1) is washable). At the same time, the lightweight design reduces the burden of wearing.