Noise reduction sleep monitoring method for assisting sleep and earmuff device

Abstract

The invention provides a noise reduction sleep monitoring method and device of a sleep-assisting earmuff. The noise reduction sleep monitoring method comprises the following steps that 1, the earmuff device obtains and stores activity state data and environment noise data of a user; step 2, importing the active state data into an algorithm model for operation, performing drive control on a hardware device for controlling incoming and outgoing volume according to an analyzed environmental noise result, and adaptively adjusting the size of a valve for incoming volume; and step 3, processing the data in a negative feedback mode, and importing the algorithm model during next sleep monitoring. The invention has the beneficial effect of helping people who need to sleep in the daytime to better reduce the interference of the noise of the surrounding environment so as to better fall asleep.

Description

Technical Field

[0001] The present invention relates to the field of intelligent health information processing, and in particular to a noise-reducing sleep monitoring method and an earmuff device for assisting sleep. Background Art

[0002] Sleep disorders are manifestations of abnormal sleep quantity and abnormal behavior during sleep, and also manifestations of disturbance of the normal rhythm of sleep and wakefulness. They can be caused by a variety of factors and are often related to physical diseases, including sleep disorders and parasomnias. Sleep is closely related to human health. Surveys show that many people suffer from sleep disorders or sleep-related diseases, and the proportion of adults with sleep disorders is as high as 30%. Experts point out that sleep is an extremely important physiological function to maintain human life and is essential to the human body.

[0003] There are many reasons for the above-mentioned sleep problems. For example, when people who work night shifts sleep, the noise around them is too loud during the day, affecting their sleep; their sleep at night is affected by the noisy surroundings; they have light sleep, that is, they will be woken up by the slightest noise; and they have physical or mental difficulty falling asleep. Summary of the invention

[0004] The present invention provides a method and device for automatically controlling the volume of the sound according to the environment, posture data and the state of the user, which is achieved through the following technical solution.

[0005] The embodiment of the present invention relates to a noise reduction sleep monitoring method for assisting sleep, comprising the following steps: Step 1: Obtain and store user activity status data and environmental noise data; Step 2: Import the activity status data into the algorithm model for calculation. According to the analyzed environmental noise results, drive and control the hardware device for controlling the volume in and out, and adaptively adjust the valve size of the volume in. Step 3: The data is processed by negative feedback and imported into the algorithm model during the next sleep monitoring.

[0006] The embodiment of the present invention also relates to an earmuff device for assisting sleep, wherein the earmuff device has a built-in hardware device, including a three-axis accelerometer, a microphone, and a circuit board for controlling the volume of the earmuff, and the software is responsible for driving the circuit, collecting and processing data, wherein: The three-axis accelerometer is used to monitor the user's physical activity status; The volume of ambient sound that the microphone can capture; The driving motor circuit is used to control the volume of the earmuffs, and the valve size of the earmuffs' volume intake is controlled by the rotation angle of the motor.

[0007] The beneficial effects of the present invention are: it can help people who need to sleep during the day to better reduce the interference of ambient noise and thus fall asleep better, and will not cause them to miss important calls or alarm clocks and other effective sound information. It can also automatically record and analyze sleep data for optimization and adjustment, thereby improving sleep quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 4 is a flow chart of a noise reduction sleep monitoring method according to an embodiment of the present invention.

[0009] Figure 2 4 is a structural block diagram of a smart earmuff according to an embodiment of the present invention.

[0010] Figure 3 4 is a structural block diagram of an intelligent noise reduction sleep monitoring system according to an embodiment of the present invention. Implementation

[0011] The embodiments of the present invention are described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0012] The present invention provides a noise reduction sleep monitoring system for earmuffs that assists sleep. The system includes software and hardware, the hardware is an intelligent noise reduction earmuff, the earmuff has a built-in hardware device, including a three-axis accelerometer, a microphone, and a circuit board for controlling the volume of the earmuff, and the processor is responsible for driving the circuit, collecting and processing data.

[0013] In the embodiment of the present invention, the three-axis accelerometer is used to obtain the user's posture and body movement data, that is, the body activity status. The microphone is used to detect environmental sounds. The data is processed and Before going to bed, the user can put on earmuffs and turn on the sleep mode. At this time, the system will start to monitor the user's body activity status and the surrounding noise level detected by the current microphone, and identify whether it is a valid sound (mobile phone call sound, alarm sound, etc.) by processing the voice data. The hardware device that controls the input and output volume will adaptively adjust the decibel level of the noise entering the ear, so as to achieve the purpose of reducing the interference of excessive external invalid noise on the user's sleep.

[0014] At the same time, the earmuffs will automatically record the user's sleep status and sleep quality during sleep, and use the algorithm to provide feedback and learn based on the data results, and optimize and adjust the noise control system entering the ear to provide a customized solution for different customers.

[0015] like Figure 2As shown, in the embodiment of the present invention, the hardware configuration of the smart earmuff 100 is: including a shell, a main control circuit board 10 and a motor driving device 20, and the main control circuit board integrates a three-axis sensor 11, a microphone 12, a power control module 13, a communication module 14, a driving motor circuit 15, and a processor 16 component.

[0016] Among them, the three-axis accelerometer is used to monitor the user's physical activity status. The microphone can capture the volume of the surrounding environment. The drive motor circuit is a hardware device that can control the volume of the earmuffs. The valve size of the earmuffs' volume inlet is controlled by the angle of rotation of the motor. The cooperation of these hardware devices can effectively reduce the impact of noise on users and help users fall asleep better. The communication module can upload all the data during the sleep process monitored by the earmuffs to the APP of the mobile phone 200 through Bluetooth wireless communication so that it can be displayed to the user for viewing.

[0017] like Figure 3 As shown, it is a structural block diagram of the intelligent noise reduction sleep monitoring system of an embodiment of the present invention. The system acquires and collects the original data of the three-axis accelerometer and the microphone by controlling the hardware device, and then filters the collected data multiple times to obtain processed data, and then performs statistical analysis on these data in terms of time, size, frequency, etc. and imports them into the algorithm model for calculation.

[0018] The output results are divided into sleep duration, sleep quality, physical activity, breathing and noise level of the surrounding environment. The system will upload the data to the APP of the mobile phone 200 through Bluetooth communication, so that users can understand their sleep status more intuitively. Finally, the system will store these data in the memory, and import the algorithm model in the next sleep monitoring through negative feedback, so as to adjust and optimize some parameters to improve the noise reduction effect next time, help users better understand their sleep status and improve sleep quality.

[0019] The system of the present invention also has a machine learning function. The system can automatically record and analyze the user's 1-2 weeks of sleep data, and learn how to better adapt to the noise level requirements of different users based on the feedback results of the data. Therefore, the system has good scalability and intelligence, and can continuously improve the noise reduction effect and sleep quality.

[0020] like Figure 1 As shown, a noise reduction sleep monitoring method using a sleep-assisting earmuff is provided, comprising the following steps: S1. Obtain and store user activity status data and environmental noise data; S2. Importing the activity status data into the algorithm model for calculation, and driving and controlling the hardware device for controlling the in-and-out volume according to the analyzed environmental noise results, so as to adaptively adjust the volume of the in-ear sound; S3. Process the data through negative feedback and import the algorithm model during the next sleep monitoring.

[0021] In S2 of the embodiment of the present invention, the algorithm model is an embedded algorithm in the processor, and the steps of processing data by the algorithm model include: First, the input data is preprocessed to convert the raw data into a format suitable for processing by the algorithm model; Secondly, the model is trained using labeled training data. During the training process, the algorithm model adjusts and optimizes the parameters of the next driving in and out volume device according to the user's current sleep state over a period of time. When the user's sleep state reaches the preset effect, the current parameters of the model are saved as input parameters for the next training.

[0022] In S2 of the embodiment of the present invention, negative feedback is a control mechanism used in the system to adjust the volume or duration of the earmuffs in and out so that the user's sleep state gradually tends to the desired state or goal. Through the negative feedback mechanism, the system can automatically correct deviations and achieve self-regulation to better adapt to and respond to external environments or requirements.

[0023] The specific process of negative feedback.

[0024] ① Set goals to determine the user's desired state or goal for sleep status and duration.

[0025] ②Monitor and measure the user's actual sleep output or behavior through a three-axis sensor.

[0026] ③Compare the target value with the actual output and calculate the difference or error between them.

[0027] ④ Based on the difference between the target and actual output, an error signal is generated. This signal represents the current deviation or error of the system and is used to guide the adjustment of the system's behavior.

[0028] ⑤ Based on the error value between the expected result and the measured result, a control algorithm or logic is used to generate corresponding control signals. These control signals can be used to adjust the noise reduction effect of the microphone.

[0029] The entire process is a cyclic process that continuously detects actual output, calculates errors, generates control signals, and adjusts system behavior. In each iteration, the system will make fine adjustments based on the user's historical sleep status, gradually reducing the error and bringing the user's sleep status closer to the desired state or goal.

[0030] Automatically record and analyze the user's sleep data for a period of time, and learn based on the feedback from the data to better adapt to the noise level requirements of different users. The learning steps are: Perform data cleaning, feature selection and data transformation operations on activity status data and environmental noise data; The algorithm model adjusts and optimizes parameters based on the input data, evaluates whether the data results meet the preset accuracy and precision, and applies it to the next input data for prediction or classification.

[0031] In the embodiment of the present invention, the specific implementation process is: Wear earmuffs to start monitoring: When the user wears earmuffs, the three-axis sensor and microphone in the earmuffs can sense the user's physical activity status and the external environmental noise level, and store this data in the watch body memory.

[0032] Import algorithm model: The data collected by the three-axis sensor is regularly imported into the algorithm model for calculation. When it is detected that the user begins to fall asleep, the previous feedback data is imported into the algorithm model for parameter adjustment and optimization. Then the real-time data collected by the microphone is imported into the algorithm model for calculation.

[0033] The system will drive and control the hardware device that controls the volume of the input and output according to the results of the environmental noise analysis, so as to adaptively adjust the volume of the in-ear sound. When valid sounds such as alarms or incoming calls are detected, the volume of the in-ear sound will be increased so that the user can receive valid information. Otherwise, the volume of the in-ear sound will be reduced to reduce the interference of invalid external noise received by the user.

[0034] Data storage and upload: When the algorithm detects that the user has woken up, the system will count and analyze the sleep data and store the data in the external FLASH to prevent loss. When the user connects the earmuffs through the APP, the system will wirelessly upload the sleep data to the APP for display.

[0035] Provide sleep assessment and suggestions: The APP will integrate various data indicators of the user during sleep, provide users with detailed sleep assessment and suggestions, and help users better understand their sleep conditions. Users can also fill out a feedback form on their sleep conditions, such as whether they are disturbed by environmental noise during sleep, etc. The APP will send the feedback to the earmuff system via Bluetooth communication, and the system will import the algorithm model the next time they fall asleep, and the algorithm model will adjust the corresponding parameters.

[0036] Advantages and disadvantages of the system: The advantage of this system is that it can help people who need to sleep during the day to reduce the interference of ambient noise and fall asleep better, without missing important calls or alarm clocks and other effective sound information. At the same time, the system can also automatically record and analyze sleep data for optimization and adjustment, thereby improving sleep quality.

[0037] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A noise reduction sleep monitoring method for assisting sleep, characterized in that: The steps include: Step 1: The earmuff device obtains and stores the user's activity status data and environmental noise data; Step 2: Import the activity status data and microphone data into the algorithm model; Step 3: The data is processed by negative feedback and imported into the algorithm model during the next sleep monitoring.

2. The noise reduction sleep monitoring method according to claim 1, characterized in that: The algorithm model includes: First, the input data is preprocessed to convert the raw data into a format suitable for processing by the algorithm model; Secondly, the model is trained using labeled training data. During the training process, the algorithm model adjusts and optimizes the parameters of the next driving in and out volume device according to the user's current sleep state over a period of time. When the user's sleep state reaches the preset effect, the current parameters of the model are saved as input parameters for the next training.

3. The noise reduction sleep monitoring method according to claim 2, characterized in that: According to the analyzed environmental noise result, the earmuff device drives and controls the hardware device for controlling the in-and-out volume, thereby adaptively adjusting the volume of the in-ear sound.

4. The noise reduction sleep monitoring method according to claim 3, characterized in that: When the earmuff device detects valid sounds such as an alarm clock or an incoming call, it increases the volume of the sound entering the ear so that the user can receive valid information. Otherwise, it will reduce the volume of the sound entering the ear to reduce the interference of invalid external noise received by the user.

5. The noise reduction sleep monitoring method according to claim 1, characterized in that: The earmuff device automatically records and analyzes the user's sleep data for a period of time, and learns how to better adapt to the noise level requirements of different users based on the feedback results of the data. The learning steps are: Perform data cleaning, feature selection and data transformation operations on activity status data and environmental noise data; The algorithm model adjusts and optimizes parameters based on the input data, evaluates whether the data results meet the preset accuracy and precision, and applies it to the next input data for prediction or classification.

6. The noise reduction sleep monitoring method according to claim 1, characterized in that: The specific process of the negative feedback includes the following steps: Set expectations or goals for the user's sleep status and duration; The user's actual sleep output or behavior is monitored and measured through a three-axis sensor; Compare the target value with the actual output and calculate the difference or error between them; An error signal is generated according to the difference between the target and the actual output, wherein the error signal represents the current deviation or error of the system and is used to guide the behavior of adjusting the system; Based on the error value between the expected result and the measured result, a control algorithm or logic is used to generate a corresponding control signal, and the control signal is used to adjust the noise reduction effect of the microphone.

7. The noise reduction sleep monitoring method according to claim 1, characterized in that: Monitor the user's physical activity status and the surrounding noise level detected by the current microphone, and identify whether it is valid sound by processing the voice data, and adaptively adjust the decibel level of noise entering the ear based on the valid sound.

8. An earmuff device for assisting sleeping, characterized in that: For implementing the noise reduction sleep monitoring method according to any one of claims 1 to 7, the earmuff device comprises: The three-axis accelerometer is used to monitor the user's physical activity status; The volume of ambient sound that the microphone can capture; The driving motor circuit is used to control the volume of the earmuffs, and the valve size of the earmuffs' volume intake is controlled by the rotation angle of the motor.

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