Intelligent wall lamp health monitoring method and system based on radar induction

Abstract

The invention relates to the field of health monitoring, and discloses an intelligent wall lamp health monitoring method and system based on radar induction, and the system comprises a radar module, a data processing module, a communication module and a power module. According to the system, space echo signals are collected through a radar to generate motion characteristics and vital sign data, multi-modal data fusion analysis is achieved in combination with an external sensor, whether a user triggers an abnormal event or not is judged, alarm information is sent through visible light communication, and the power module achieves self-power supply through a photovoltaic film and a radio frequency energy collection technology. According to the invention, multifunctional, all-weather and non-contact health monitoring can be realized, privacy protection is enhanced, and requirements of families and public places are met.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of health monitoring, more particularly to a radar-sensing-based intelligent wall lamp health monitoring method and system. BACKGROUND

[0002] With the trend of population aging, the health and daily care of the elderly are becoming increasingly important. Health monitoring, accidental falls of the elderly, and the elderly going out alone require a lot of manpower and resources. Many wearable and non-wearable devices have emerged on the market to monitor the health, falls, and outings of the elderly.

[0003] Wearable devices are usually in the form of wristbands or bracelets. These wearable devices have the problem of not being comfortable to wear during use, and there are certain deficiencies in all-weather monitoring. Non-wearable devices are usually based on cameras and radars. The camera solution has the problem of needing to take real-time photos of user activities during monitoring, which can easily cause privacy concerns. The radar solution usually focuses on implementing one of the functions of fall detection or people counting, and has certain limitations in actual daily care due to the single function. SUMMARY

[0004] To address the deficiencies in the prior art, the present application aims to provide a radar-sensing-based intelligent wall lamp health monitoring system that realizes multi-functional, all-weather, and contactless health monitoring through multi-modal data fusion, adaptive algorithm optimization, and privacy-enhanced design.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0006] A radar-sensing-based intelligent wall lamp health monitoring method, comprising the following steps: receiving spatial echo signals collected by a built-in radar module of a wall lamp, and generating motion feature data and vital sign data of a user according to the echo signals; performing multi-modal fusion analysis on the motion feature data in combination with auxiliary data collected by an external sensor to generate a behavior pattern of the user; determining whether a preset abnormal event is triggered by the user using the vital sign data and the behavior pattern; and if the abnormal event is triggered, sending alarm information to a bound terminal through a built-in communication module of the wall lamp.

[0007] In some embodiments, the motion feature data of the user is generated according to the spatial echo signal, including the following steps: converting the echo signal into a digital signal; performing joint time domain and frequency domain analysis on the digital signal to extract a distance-velocity distribution atlas of a target unit; performing segmented filtering processing on the distance-velocity distribution atlas to screen out a target unit corresponding to the user; performing angle solving on the target unit to obtain its position coordinates in space; and generating motion trajectory data and speed distribution data of the user according to dynamic changes of the position coordinates.

[0008] In some embodiments, the vital sign data of the user is generated according to the spatial echo signal, including the following steps: extracting phase change information of a static target from the echo signal; performing multi-frame accumulation processing on the phase change information to obtain a low-frequency vibration signal; decomposing the low-frequency vibration signal into a respiratory component and a heartbeat component using a preset frequency segmentation algorithm; and performing periodic analysis on the respiratory component and the heartbeat component respectively to calculate the respiratory rate and heart rate values of the user.

[0009] In some embodiments, the motion feature data is subjected to multi-modal fusion analysis in combination with auxiliary data collected by an external sensor, including the following steps: obtaining body temperature distribution data of the user from an infrared thermal imaging sensor; obtaining voiceprint feature data of the user from a microphone array; performing weighted fusion of the motion feature data, the body temperature distribution data and the voiceprint feature data to generate a comprehensive behavior pattern; and classifying the comprehensive behavior pattern using a machine learning model to identify a specific behavior state of the user.

[0010] In some embodiments, the preset abnormal events include a fall risk event and a health abnormal event; and whether the user triggers the preset abnormal event is determined according to the vital sign data and the behavior pattern, including the following steps: calculating a center of gravity offset of the user according to the motion trajectory data; when the center of gravity offset exceeds a first threshold value, determining whether the user has a fall risk in combination with the speed distribution data; determining whether the user has a health abnormality according to the respiratory rate and heart rate values; and if the fall risk and the health abnormality are detected simultaneously, determining that the user triggers a composite abnormal event.

[0011] In some embodiments, the preset abnormal events further include an environmental abnormal event; and whether the user triggers the preset abnormal event is determined according to the vital sign data and the behavior pattern, further including the following steps: obtaining bed surface pressure distribution data of the user from a pressure sensor; determining whether the user has a bed fall risk according to the bed surface pressure distribution data; obtaining an air quality parameter from an environmental sensor; and when the air quality parameter is lower than a second threshold value, determining that the user triggers an environmental abnormal event.

[0012] In some embodiments, the wall lamp built-in communication module realizes data transmission through visible light communication technology; specifically, the LED light source integrated on the surface of the wall lamp generates light pulse signals through high-frequency flickering coding; the light pulse signals carry the vital sign data and behavior pattern information of the user; the external medical device parses the light pulse signals through a photoelectric receiver to complete data interaction.

[0013] In some embodiments, the wall lamp built-in power module realizes self-power supply through energy recovery technology; specifically, the wall lamp is covered with a photovoltaic film to convert environmental light energy into electrical energy; a radio frequency energy collection circuit is arranged inside the wall lamp to convert environmental electromagnetic waves into direct current; the photovoltaic film and the radio frequency energy collection circuit jointly supply power to the wall lamp to ensure continuous operation of the system.

[0014] To achieve the above-mentioned purposes, the application further provides an intelligent wall lamp health monitoring system based on radar induction, comprising a radar module, a data processing module, a communication module and a power module; the radar module is used for collecting space echo signals and generating motion feature data and vital sign data; the data processing module is used for combining external sensor data to perform multi-modal fusion analysis on the motion feature data and judging whether an abnormal event is triggered; the communication module is used for sending alarm information to a bound terminal through visible light communication technology; and the power module is used for supplying power to the system through energy recovery technology.

[0015] To achieve the above-mentioned purposes, the application further provides a computer readable storage medium, wherein the computer readable storage medium stores an intelligent wall lamp health monitoring program based on radar induction, and the program is executed by a processor to realize the intelligent wall lamp health monitoring method based on radar induction as described in any one of the above.

[0016] The application has the following beneficial effects:

[0017] By combining radar data with external sensor data such as infrared thermal imaging and voiceprint analysis, a comprehensive behavior pattern is generated, improving monitoring accuracy and application range; a machine learning model is used to dynamically adjust signal processing parameters to adapt to different user body types, environmental interference and medical scenarios, ensuring the reliability of the monitoring results; edge computing and visible light communication technology are used to realize data desensitization to avoid user privacy leakage; the radar module, light module and sensor module can be flexibly combined and upgraded to meet the diversified needs of families and public places; photovoltaic film and radio frequency energy collection technology are used to realize self-power supply of the system, reducing maintenance costs and improving the sustainability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 FIG. 1 is a schematic diagram of the overall structure of the intelligent wall lamp health monitoring system in the embodiments of the application.

[0019] Figure 2 Fig. 1 is a schematic diagram of the working principle of the radar module in an embodiment of the present application.

[0020] Figure 3 Fig. 2 is a schematic diagram of the process of generating user motion feature data in an embodiment of the present application.

[0021] Figure 4 Fig. 3 is a flowchart of the multi-modal data fusion analysis in an embodiment of the present application.

[0022] Figure 5 Fig. 4 is a schematic diagram of the abnormal event judgment logic in an embodiment of the present application.

[0023] Reference signs: 1, radar module; 2, data processing module; 3, communication module; 4, power module; 5, spatial echo signal; 6, motion feature data; 7, vital sign data; 8, infrared thermal imaging sensor; 9, microphone array; 10, LED light source; 11, light pulse signal. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0025] It should be noted that when a component is referred to as being “fixed” to another component, it can be directly on the other component or there can be intervening components. When a component is referred to as being “connected” to another component, it can be directly connected to the other component or there can be intervening components. When a component is referred to as being “disposed” on another component, it can be directly disposed on the other component or there can be intervening components. The terms “vertical”, “horizontal”, “left”, “right”, and similar expressions used herein are for illustrative purposes only.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term “and / or” includes any and all combinations of one or more of the associated listed items.

[0027] The present application provides an intelligent wall lamp health monitoring system and method based on radar sensing, combined with the attached Figure 1 to the attached Figure 5 The specific embodiments of the present application will be described in detail.Figure 1 The overall structure of the intelligent wall lamp health monitoring system in the embodiment of the present application is shown, which shows the layout relationship of the radar module 1, the data processing module 2, the communication module 3 and the power module 4. The radar module 1 is located at the top of the wall lamp, close to the surface of the wall lamp, for collecting space echo signals 5; the data processing module 2 is arranged in the central region inside the wall lamp, connected with the radar module 1 through a flexible circuit board, for receiving and processing the data generated by the radar module 1; the communication module 3 is integrated in the bottom region of the wall lamp, connected with the data processing module 2 through a hard PCB board, for realizing the data transmission function; the power module 4 is distributed in the back of the wall lamp, connected with the radar module 1, the data processing module 2 and the communication module 3 through wires respectively, for supplying power to the whole system.

[0028] Figure 2 The working principle of the radar module 1 in the embodiment of the present application is shown, which shows the process of converting the space echo signal 5 into motion feature data 6 and vital sign data 7. The radar module 1 scans the surrounding environment by emitting millimeter wave signals and receives the reflected space echo signals 5. After the space echo signals 5 are converted into digital signals by an analog-to-digital converter, they enter the time and frequency domain joint analysis unit. The time domain analysis extracts the distance information of the target unit, and the frequency domain analysis extracts the speed information of the target, forming a distance-velocity distribution map. Then, the segmented filter processes the distance-velocity distribution map to screen out the target unit corresponding to the user. The angle calculation unit receives the information of the target unit and calculates its position coordinates in space, finally generating the user's motion trajectory data and velocity distribution data as motion feature data 6. At the same time, the phase change information of the static target unit is extracted, and after multi-frame accumulation processing, a low-frequency vibration signal is generated. The frequency division algorithm decomposes the low-frequency vibration signal into respiratory and heartbeat components, and through periodic analysis of the two components, the user's respiratory rate and heart rate values are calculated as vital sign data 7.

[0029] Figure 3 The flow chart of multi-modal data fusion analysis in the embodiment of the present application is described, which describes the specific steps of generating a comprehensive behavior pattern combined with external sensor data. The infrared thermal imaging sensor 8 is installed on the side of the wall lamp, for obtaining the user's body temperature distribution data; the microphone array 9 is embedded in the middle of the wall lamp, for collecting the user's voiceprint feature data. The data processing module 2 receives the motion feature data 6 from the radar module 1 and the auxiliary data from the infrared thermal imaging sensor 8 and the microphone array 9, and generates a comprehensive behavior pattern through a weighted fusion algorithm. The motion feature data 6 mainly reflects the user's dynamic behavior, such as walking, standing or falling, etc.; the body temperature distribution data supplements the user's physiological state information, and the voiceprint feature data provides the basis for voice-related identity recognition and emotion analysis. The machine learning model classifies the comprehensive behavior pattern to identify the specific behavior state of the user, such as normal activity, sleep state or abnormal behavior.

[0030] Figure 4 For the schematic diagram of the abnormal event judgment logic in the embodiment of the application, including the detection method of fall risk, health abnormality and environmental abnormality event. The data processing module 2 calculates the center of gravity offset of the user according to the motion trajectory data, and when the center of gravity offset exceeds the first threshold value, it is judged whether the user has a fall risk in combination with the speed distribution data. If the user's respiratory rate and heart rate value exceeds the preset range, it is determined that the user has a health abnormality. If fall risk and health abnormality are detected at the same time, it is determined that the user triggers a composite abnormal event. In addition, a pressure sensor is installed under the bed surface or seat to obtain the bed surface pressure distribution data of the user; the environmental sensor is arranged at the back of the wall lamp to monitor the air quality parameters. When the bed surface pressure distribution data shows that the user has a bed fall risk, or the air quality parameters are lower than the second threshold value, the data processing module 2 determines that the user triggers an environmental abnormality event.

[0031] Figure 5 For the data transmission schematic diagram of the visible light communication technology in the embodiment of the application, the process of LED light source 10 interacting with external equipment through optical pulse signal 11 is shown. The communication module 3 controls the LED light source 10 integrated on the surface of the wall lamp to generate optical pulse signal 11 in the form of high-frequency flashing, and the optical pulse signal 11 carries the user's vital sign data 7 and behavior pattern information. The external medical equipment analyzes the optical pulse signal 11 through the photoelectric receiver to complete the data interaction. This data transmission method avoids the privacy leakage problem that may be caused by traditional wireless communication, and at the same time, it realizes efficient data transmission by using the light source of the wall lamp itself.

[0032] The power module 4 supplies power to the system through energy recovery technology, specifically including photovoltaic film and radio frequency energy collection circuit. The photovoltaic film is covered on the surface of the wall lamp, which converts environmental light energy into electrical energy; the radio frequency energy collection circuit is arranged inside the wall lamp, which converts environmental electromagnetic waves into direct current. The photovoltaic film and the radio frequency energy collection circuit are connected to the energy storage battery through the diode rectifier circuit, and the energy storage battery supplies power to the radar module 1, the data processing module 2 and the communication module 3 through the voltage stabilizing circuit, ensuring the continuous operation of the system.

[0033] In this embodiment, the overall structure design of the wall lamp fully considers the modular characteristics, and the modules are connected through standard interfaces, which is convenient for later maintenance and upgrading. For example, the radar module 1 can adapt to various application scenarios by replacing antennas of different frequency bands; the data processing module 2 supports loading different machine learning models to optimize algorithm performance; the communication module 3 can select different data transmission protocols according to needs; the photovoltaic film and the radio frequency energy collection circuit of the power module 4 can also be flexibly adjusted and configured according to environmental conditions.

[0034] The application scenarios of the embodiment include family endowment care and public health management. In the family scenario, the wall lamp is installed on the wall of the bedroom or living room, and monitors the daily activities and health status of the old people in real time. When an abnormal event is detected, the communication module 3 sends alarm information to the bound terminal through the light pulse signal 11, reminding the family members or medical staff to handle it in time. In public places such as hospital wards or nursing home activity areas, multiple wall lamps work cooperatively to form a health monitoring network covering the entire area, and improve the monitoring accuracy and efficiency through data fusion analysis.

[0035] The embodiment realizes a multi-functional, all-weather, contactless health monitoring system through the specific structure and operation principle described above. The radar module 1 is responsible for collecting basic data, the data processing module 2 generates a comprehensive behavior pattern through multi-modal data fusion and adaptive algorithm optimization, the communication module 3 uses visible light communication technology to ensure data transmission safety, and the power module 4 realizes self-power supply through energy recovery technology. The modules work closely together to form a complete health monitoring solution.

[0036] In order to better enable relevant persons in the art to fully understand and implement the present application, the specific implementation principles of the present application are supplemented as follows in combination with specific application scenarios.

[0037] In the family endowment care scenario, the intelligent wall lamp is installed above the wall of the bedroom, close to the bedside. When the old people enter the room and start their daily activities, the radar module 1 scans the surrounding environment by emitting millimeter wave signals and receives the reflected space echo signals 5. After the space echo signals 5 are converted into digital signals by the analog-to-digital converter, they enter the time and frequency domain joint analysis unit. The time domain analysis extracts the distance information of the target unit, and the frequency domain analysis extracts the speed information of the target, forming a distance-velocity distribution map. Then, the segmented filter processes the distance-velocity distribution map to screen out the target unit corresponding to the user. The angle solving unit calculates the position coordinates of the target unit in space according to its information, and generates the user's motion trajectory data and speed distribution data as motion feature data 6. At the same time, the phase change information of the static target unit is extracted, and after multi-frame accumulation processing, a low-frequency vibration signal is generated. The frequency division algorithm decomposes the low-frequency vibration signal into respiratory and heartbeat components, and through periodic analysis of the two components, the user's respiratory rate and heart rate values are calculated as vital sign data 7.

[0038] Meanwhile, the infrared thermal imaging sensor 8 acquires the user's body temperature distribution data, and the microphone array 9 collects the user's voiceprint feature data. The data processing module 2 receives the motion feature data 6 from the radar module 1 and the auxiliary data from the infrared thermal imaging sensor 8 and the microphone array 9, and generates a comprehensive behavior pattern through a weighted fusion algorithm. The motion feature data 6 reflects the user's dynamic behavior, such as walking, standing, or falling, etc.; the body temperature distribution data supplements the user's physiological state information, and the voiceprint feature data provides the basis for voice-related identity recognition and emotion analysis. The machine learning model classifies the comprehensive behavior pattern to identify the user's specific behavior state, such as normal activity, sleep state, or abnormal behavior.

[0039] If the data processing module 2 calculates the user's center of gravity offset based on the motion trajectory data and finds that it exceeds the first threshold value, it judges whether the user has a falling risk in combination with the speed distribution data. If the user's respiratory rate and heart rate values exceed the preset range, it is determined that the user has a health abnormality. If both falling risk and health abnormality are detected, it is determined that the user triggers a composite abnormal event. In addition, a pressure sensor is installed under the bed surface to acquire the user's bed surface pressure distribution data; an environmental sensor is set in the back of the wall lamp to monitor air quality parameters. When the bed surface pressure distribution data shows that the user has a bed falling risk, or the air quality parameters are below the second threshold value, the data processing module 2 determines that the user triggers an environmental abnormal event.

[0040] Once an abnormal event is detected, the communication module 3 controls the LED light source 10 integrated on the surface of the wall lamp to generate light pulse signals 11 in the form of high-frequency flashing coding, which carries the user's vital sign data 7 and behavior pattern information. The external medical device parses the light pulse signals 11 through a photoelectric receiver to complete data interaction. This data transmission method avoids the privacy leakage problem that may be caused by traditional wireless communication, and at the same time, it realizes efficient data transmission by using the light source of the wall lamp itself. The communication module 3 sends alarm information to the bound terminal through the light pulse signals 11, reminding family members or medical personnel to handle it in time.

[0041] In the whole process, the power module 4 supplies power to the system through energy recovery technology. The photovoltaic film is covered on the surface of the wall lamp, which converts environmental light energy into electrical energy; the radio frequency energy collection circuit is set inside the wall lamp, which converts environmental electromagnetic waves into direct current. The photovoltaic film and the radio frequency energy collection circuit are connected to the energy storage battery through the diode rectifier circuit, and the energy storage battery supplies power to the radar module 1, the data processing module 2, and the communication module 3 through the voltage stabilizing circuit, ensuring the continuous operation of the system.

[0042] Through the above steps, the application realizes a multi-functional, all-weather, non-contact health monitoring system. The radar module 1 is responsible for collecting basic data, the data processing module 2 generates a comprehensive behavior mode through multi-modal data fusion and adaptive algorithm optimization, the communication module 3 adopts visible light communication technology to ensure data transmission safety, and the power module 4 realizes self-power supply through energy recovery technology. The modules cooperate closely to form a complete health monitoring solution.

[0043] The above is only a preferred embodiment of the application, and the protection scope of the application is not limited to the above-mentioned embodiments. Any technical solution falling within the concept of the application shall fall within the protection scope of the application. It should be noted that, for ordinary skilled persons in the art, some improvements and refinements without departing from the principles of the application shall also be considered as falling within the protection scope of the application.

Claims

1. A method for health monitoring of a smart wall lamp based on radar sensing, characterized in that, Includes the following steps: The system receives spatial echo signals collected by the built-in radar module of the wall lamp and generates motion characteristic data and vital sign data of the user based on the echo signals. The motion feature data is analyzed using multimodal fusion based on auxiliary data collected by external sensors to generate user behavior patterns. The vital signs data and behavioral patterns are used to determine whether the user has triggered a preset abnormal event; If an abnormal event is triggered, an alarm message will be sent to the bound terminal through the wall lamp's built-in communication module.

2. The smart wall lamp health monitoring method based on radar sensing as described in claim 1, characterized in that, Generating user motion characteristic data based on the spatial echo signal includes the following steps: The echo signal is converted into a digital signal; The digital signal is subjected to joint time-domain and frequency-domain analysis to extract the range-velocity distribution map of the target unit; The distance-velocity distribution map is segmented and filtered to identify target units corresponding to the user. Angle calculations are performed on the target unit to obtain its position coordinates in space; The user's motion trajectory data and speed distribution data are generated based on the dynamic changes in the location coordinates.

3. The smart wall lamp health monitoring method based on radar sensing as described in claim 1, characterized in that, Generating the user's vital signs data based on the spatial echo signal includes the following steps: Extract the phase change information of the static target from the echo signal; The phase change information is accumulated over multiple frames to obtain a low-frequency vibration signal; The low-frequency vibration signal is decomposed into respiratory and heartbeat components using a preset frequency segmentation algorithm. The respiratory and heart rate components are analyzed periodically to calculate the user's respiratory rate and heart rate.

4. The smart wall lamp health monitoring method based on radar sensing as described in claim 1, characterized in that, The motion feature data is subjected to multimodal fusion analysis by combining auxiliary data collected by external sensors, including the following steps: Obtain user's body temperature distribution data from infrared thermal imaging sensors; Acquire user voiceprint feature data from microphone array; The motion feature data is weighted and fused with the body temperature distribution data and voiceprint feature data to generate a comprehensive behavior pattern; The comprehensive behavioral patterns are classified using machine learning models to identify the specific behavioral states of users.

5. The smart wall lamp health monitoring method based on radar sensing as described in claim 1, characterized in that, The preset abnormal events include fall risk events and health abnormal events; Determining whether a user has triggered a preset abnormal event based on the vital signs data and behavioral patterns includes the following steps: Calculate the user's center of gravity offset based on the motion trajectory data; When the center of gravity offset exceeds the first threshold, the speed distribution data is used to determine whether the user is at risk of falling. The respiratory rate and heart rate values ​​are used to determine whether the user has any health abnormalities. If both fall risk and health abnormality are detected simultaneously, the user is deemed to have triggered a compound abnormal event.

6. The smart wall lamp health monitoring method based on radar sensing as described in claim 1, characterized in that, The preset abnormal events also include environmental abnormal events; determining whether a user has triggered a preset abnormal event based on the vital signs data and behavioral patterns also includes the following steps: Data on the pressure distribution on the user's bed surface is obtained from pressure sensors; Based on the bed surface pressure distribution data, determine whether the user is at risk of falling out of bed; Obtain air quality parameters from environmental sensors; When the air quality parameter is lower than the second threshold, it is determined that the user has triggered an abnormal environmental event.

7. The smart wall lamp health monitoring method based on radar sensing as described in claim 1, characterized in that, The wall lamp has a built-in communication module that transmits data using visible light communication technology; the LED light source integrated on the wall lamp surface generates light pulse signals through high-frequency flashing encoding; the light pulse signals carry the user's vital signs data and behavioral pattern information. External medical devices analyze the light pulse signals through a photoelectric receiver to complete data interaction.

8. The smart wall lamp health monitoring method based on radar sensing as described in claim 1, characterized in that, The wall lamp has a built-in power module that achieves self-powering through energy recovery technology. Specifically, the wall lamp surface is covered with a photovoltaic film, which converts ambient light energy into electrical energy. The wall lamp is equipped with a radio frequency energy harvesting circuit, which converts ambient electromagnetic waves into direct current. The photovoltaic film and the radio frequency energy harvesting circuit together power the wall lamp, ensuring continuous operation of the system.

9. A smart wall lamp health monitoring system based on radar sensing, characterized in that, It includes a radar module (1), a data processing module (2), a communication module (3), and a power supply module (4); The radar module (1) is used to collect spatial echo signals and generate motion characteristic data and vital sign data; The data processing module (2) is used to perform multimodal fusion analysis on the motion feature data by combining external sensor data, and to determine whether an abnormal event is triggered. The communication module (3) is used to send alarm information to the bound terminal through visible light communication technology; The power module (4) is used to supply power to the system through energy recovery technology.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a radar-sensing-based smart wall lamp health monitoring program, which, when executed by a processor, implements the radar-sensing-based smart wall lamp health monitoring method as described in any one of claims 1 to 8.

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