TW202144994A - Notification device, wearable device and notification method - Google Patents

Abstract

A notification device includes a pressure sensor, a microcontroller and an output device. The pressure sensor is used to detect the environment to provide a pressure signal. The microcontroller is connected to the pressure sensor to receive the pressure signal. The microcontroller is used to calculate a dynamic threshold of the pressure signal in a period of time. When a magnitude of the pressure signal is greater than the dynamic threshold, the microcontroller sends a first feedback signal to the output device. The output device is connected to the microcontroller. The output device is used to provide a first feedback action according to the first feedback signal.

Description

Notification device, wearable device and notification method

The present disclosure relates to a notification device and a wearable device provided with the notification device.

In some workplaces, it is not convenient to communicate directly through voice. For example, there are hearing-impaired people in the workplace, or the workplace is noisy. On these occasions, workers remain isolated from sound, which is not conducive to communication. If you use other devices on the market to assist communication, it may affect your work. In this regard, how to provide a portable and real-time notification device is one of the problems to be solved by those in the related art.

One aspect of the present disclosure relates to a notification device.

According to an embodiment of the present disclosure, the notification device includes a pressure sensor, a microcontroller, and an output device. The pressure sensor is used to detect the environment to provide pressure signal. The microcontroller is connected to the pressure sensor to receive the pressure signal. The microcontroller is used to calculate the dynamic threshold of the pressure signal within a period of time. When the magnitude of the pressure signal is greater than the dynamic threshold, the microcontroller sends a first feedback signal to the output device. The output device is connected to the microcontroller. The output device is used for providing a first feedback action according to the first feedback signal.

In one or more embodiments, the output device includes a light-emitting device, a vibrator, a sound amplifier, or a text-graphic display device.

In one or more embodiments, the pressure sensor is a sound sensor. The dynamic average is the average volume of the volume over a period of time.

In some embodiments, the notification device further includes a distance sensor connected to the microcontroller and the circuit board. The sound sensor and the distance sensor are integrated on the circuit board.

In some embodiments, the notification device further includes a server. The server is connected to the microcontroller via a network. The microcontroller transmits the sound signal to the server. The server recognizes and classifies the type of the sound signal, so as to transmit the second feedback signal to the microcontroller according to the type of the sound signal. The output device provides a second feedback action according to the second feedback signal.

In some embodiments, the server further includes a voice recognition module, a classification module, and a microprocessor. The sound recognition module is used for recognizing sound signals. The classification module is used for classifying the type of the recognized sound signal. The microprocessor is used for providing the second feedback signal according to the type of the sound signal.

An aspect of the present disclosure relates to a wearable device having the aforementioned notification device.

According to an embodiment of the present disclosure, a wearable device includes the aforementioned notification device and clothing. The pressure sensor, microcontroller and output device of the notification device are arranged on the clothes.

An aspect of the present disclosure relates to a notification method, which can be implemented by the notification device as described above.

In an embodiment of the present disclosure, the notification method includes the following processes. The environment is detected to provide pressure signals, and the pressure signals are processed to obtain the dynamic average value of the pressure signals within a period of time. Set dynamic thresholds based on dynamic averages. Check whether the current volume of the sound signal exceeds the dynamic threshold. If the dynamic average of the sound signal exceeds the threshold, a feedback signal is sent to the output device. The output device provides feedback action according to the feedback signal.

In one or more embodiments, the pressure signal is a sound signal, and the dynamic average is an average volume of the sound signal over a period of time.

An aspect of the present disclosure is related to the notification method, which can also be implemented by the notification device as described above.

In an embodiment of the present disclosure, the notification method includes the following processes. Detect the environment for analog sound signals. The sound recognizes the content of the analog sound signal, and thereby classifies the type of the analog sound signal. The feedback signal is output according to the type of the analog sound signal. Make the output device make a feedback action according to the feedback signal.

To sum up, the present disclosure provides a notification device, a wearable device using the notification device, and a corresponding notification method, so as to notify the user in real time according to the volume of the environment, so that the user can easily perceive changes in the environment.

The above descriptions are only used to describe the problems to be solved by the present disclosure, the technical means for solving the problems, and their effects, etc. The specific details of the present disclosure will be introduced in detail in the following embodiments and related drawings.

The following examples are described in detail with the accompanying drawings, but the provided examples are not intended to limit the scope of the present disclosure, and the description of the structure and operation is not intended to limit the order of its execution. Any recombination of elements The structure and the resulting device with equal efficacy are all within the scope of the present disclosure. In addition, the drawings are for illustrative purposes only, and are not drawn on the original scale. For ease of understanding, the same elements or similar elements in the following description will be described with the same symbols.

In addition, the terms (terms) used in the entire specification and the scope of the patent application, unless otherwise specified, usually have the ordinary meaning of each term used in this field, in the content disclosed herein and in the special content . Certain terms used to describe the present disclosure are discussed below or elsewhere in this specification to provide those skilled in the art with additional guidance in the description of the present disclosure.

In this document, terms such as "first", "second" and the like are only used to distinguish elements or operation methods with the same technical terms, and are not intended to represent an order or limit the present disclosure.

In addition, words like "include", "include", "provide" and the like are all open-ended restrictions in this document, meaning including but not limited to.

Further, in this article, unless there is a special limitation on the article in the context, otherwise "a" and "the" can generally refer to a single one or a plurality. It will be further understood that the "comprising", "" "include", "have" and similar words, designate its recited features, regions, integers, steps, operations, elements and/or components, but do not exclude its recited or additional one or more of its other features, regions, Integers, steps, operations, elements, components, and/or groups thereof.

Please refer to Figure 1. FIG. 1 is a block diagram of a notification device 1 according to an embodiment of the present disclosure. As shown in FIG. 1 , the notification device 1 includes a sound sensor 10 , a microcontroller 20 , and an output device 30 . Through the notification device 1, the user can be notified in real time according to environmental changes.

The sound sensor 10 is used to detect the environment to receive sound signals in the environment. When the notification device 1 is used in an environment such as a warehouse or a factory, the received sound signal is, for example, the sound of engineering equipment or the human voice of other workers, which is an analog signal. In some embodiments, the sound sensor 10 is, for example, a microphone sensing module (eg, a capacitive type), or the capacitive microphone sensing modules can also be simply arranged in an array.

A microcontroller (microcontroller, or microcontroller unit, MCU for short) 20 is connected to the sound sensor 10 . The microcontroller 120 has the advantages of small size, easy portability, and can be used to implement simple arithmetic functions. The sound sensor 10 can transmit the sound signal to the microcontroller 20 , and the microcontroller 20 can simply process the sound signal from the sound sensor 10 . The microcontroller 20 may also integrate a function for judging the volume of the sound signal. In this way, the microcontroller 20 can record a sound signal for a period of time, and provide a feedback signal according to the volume change of the sound signal.

The output device 30 is connected to the microcontroller 20 for providing feedback action according to the feedback signal. The output device 30 may include a light-emitting device, a vibrator, a sound amplifier, or a text-graphic display device. The text icon device displays text or other icons to directly remind the user more intuitively. Text-graphic display devices include small portable displays.

In this way, the notification device 1 can detect the environment through the sound sensor 10 to provide sound signals. The microcontroller 20 connected to the sound sensor 10 processes the dynamic volume average value of the sound signal over a period of time. The average dynamic volume refers to the average volume of the sound signal in the previous period. Based on the dynamic average, a dynamic threshold can be predetermined. If the volume of the current sound signal is greater than the dynamic threshold calculated by the dynamic average value of the previous period, it means that the environment has changed, there may be danger, or there is a need for communication around, the microcontroller 20 provides a feedback signal to the output device 30 , so that the output device 30 provides feedback actions to notify the user.

In some embodiments, the notification device 1 may also use other types of pressure sensors instead of the sound sensor 10 . The sound sensor 10 is a kind of pressure sensor, and is used for sensing the sound pressure change in the sound transmission in the environment, converting it into a sound signal, and calculating the dynamic average value to obtain the dynamic threshold value. In some embodiments, other types of pressure sensors, such as air pressure sensors, may be used in the notification device 1 . Taking an air pressure sensor as an example, the dynamic average value of air pressure in a period of time can be sensed. Once the current air pressure value is greater than the dynamic average value calculated in the previous period, the microcontroller 20 can send a feedback signal to make the output device 30 provide feedback action to immediately notify the user using the notification device 1 .

To further illustrate how the notification device 1 notifies the user, please refer to FIG. 2 . FIG. 2 illustrates a flowchart of a notification method 600 according to an embodiment of the present disclosure. The notification method 600 includes processes 610-650.

In the process 610, the sound sensor 10 of the notification device 1 can be used to detect the surrounding environment of the user, so as to obtain a sound signal in a period of time.

In the process 620, the sound signal can be processed by the microcontroller 20 connected to the sound sensor 10, so as to obtain the dynamic threshold value under a period of time. The microcontroller 20 may first calculate the dynamic average of the volume of the sound signal in a period of time according to the sound signal. For example, the sound sensor 10 can obtain the dynamic average of the volume during the period from 3 seconds ago to 1 second ago. When the notification device 1 is activated, the dynamic average of the volume may change continuously.

According to the dynamic average value of the volume, the microcontroller 20 can define a dynamic threshold value of the volume of the sound signal, so as to determine whether the volume of the sound signal has a large change in a short period of time. In some embodiments, the dynamic threshold may be set as a dynamic average. In some embodiments, a dynamic threshold value different from the dynamic average value may be set according to the size of the dynamic average value. For example, when the dynamic average of the sound signal volume is less than a specific decibel, set the dynamic threshold to a value greater than the dynamic average; and when the dynamic average of the audio volume is greater than a specific decibel, directly set the dynamic threshold equal to the dynamic average.

Going through the process 620, the dynamic average value of the volume of the received sound signal is set. In the process 630, it can be confirmed whether the current volume of the sound signal exceeds the dynamic threshold value according to the current volume of the sound signal. If so, enter the process 640 to transmit the feedback signal to the output device 30 . If not, go back to the process 610, and continue to detect the environment to provide the sound signal.

For example, in an embodiment, the microcontroller 20 calculates that the dynamic average of the volume of the sound signal is a certain decibel (eg, 60 decibels) from 3 seconds ago to 1 second ago, and the microcontroller 20 will The dynamic average of the sound signal volume is set as the dynamic threshold (process 620). Then, once the current volume of the sound signal is greater than the dynamic threshold (eg, greater than 60 dB), it is determined that the process 630 is yes, and the process 640 is entered, and the microcontroller 20 can immediately provide a feedback signal to the output device 30 .

In this way, in the process 650 , the output device 30 can provide an appropriate feedback action according to the feedback signal from the microcontroller 20 , so as to notify the user who uses the notification device 1 . The notification method 600 can be implemented on a mobile device. For example, a mobile device such as a smartphone has a microphone, a processor, and a vibrator that vibrates the phone. The application program (APP) installed on the smart phone enables the microphone to act as the sound sensor 10, the processor of the mobile phone to function as the microcontroller 20, and the vibrator of the mobile phone to act as the output device 30 to notify the vibration to provide feedback action.

Please refer to Figure 3. FIG. 3 illustrates a block diagram of a notification device 100 according to an embodiment of the present disclosure. The notification device 100 is established on the basis of the notification device 1 , and in addition to the functions of the notification device 1 , it can further provide the function of intelligent notification and warning. As shown in FIG. 3 , the notification device 100 includes a sound sensor 110 , a microcontroller 120 , a server 130 , an output device 150 and a distance sensor 160 . In this embodiment, the sound sensor 110 , the output device 150 and the distance sensor 160 are connected to the microcontroller 120 , and the server 130 is disposed at the remote end, for example, connected to the microcontroller 120 through a network. Server 130 can be used for complex calculations. Since the server 130 can be disposed at the remote end, when using the notification device 100 , only the sound sensor 110 , the microcontroller 120 , the output device 150 and the distance sensor 160 need to be carried. In some embodiments, the network is, for example, a wireless network shared using the user's mobile phone. In some embodiments, the microcontroller 120 can be connected to the network by means of Bluetooth communication. In some embodiments, the network may be another type of wireless network (Wi-Fi), such as Zigbee. In some embodiments, the network may also be the fourth generation mobile communication technology (4G) narrow band Internet of things (narrow band Internet of things, NBIoT) or LTE-M technology. In some embodiments, the network may be provided by fifth generation mobile communication technology (5G) to achieve faster transmission rates and interactions.

The sound sensor 110 is similar to the sound sensor 10 of FIG. 1 . The sound sensor 110 is used to detect the environment to receive sound signals in the environment. For example, when the notification device 100 is used in an environment such as a warehouse or a factory, the received sound signal is, for example, the sound of engineering equipment or the human voice of other workers, which is an analog signal. Specifically, in some embodiments, the sound sensor 110 is, for example, a microphone sensing module. The microphone sensing module is, for example, a capacitive type. In some embodiments, the capacitive microphone sensing modules can also be simply arranged in an array.

After the sound sensor 110 receives the sound signal of the environment, in order to facilitate analysis, the analog sound signal can be processed to filter the noise. In some embodiments, other devices for filtering noise may also be provided on the sound sensor 110 .

The microcontroller 120 is similar to the microcontroller 20 of FIG. 1 . The microcontroller 120 is connected to the sound sensor 110 . The microcontroller 120 has the advantages of small size, easy portability, and can be used to implement simple arithmetic functions. Further, the microcontroller 120 can be connected to a remote server 130 through a network. And through the connection with the microcontroller 120 , the sound sensor 110 can transmit the sound signal to the microcontroller 120 . In some embodiments, the network may be provided by, for example, a cell phone.

The server 130 is disposed at the remote end to perform more complex operations. Please refer to Figure 3 and Figure 4 at the same time. FIG. 4 illustrates a block diagram of a server 130 according to an embodiment of the present disclosure. In this embodiment, the server 130 includes a voice recognition module 135 , a classification module 140 and a processor 145 . In some embodiments, the voice recognition module 135 , the classification module 140 and the processor 145 are computer components within the server 130 . In some embodiments, the voice recognition module 135 , the classification module 140 and the processor 145 may be integrated into the same hardware.

The sound recognition module 135 is used to recognize the sound signal. The classification module 140 is used for classifying the type of the recognized sound signal. The processor 145 provides the feedback signal according to the type of the sound signal. Please refer to the following for the specific operation method. The microcontroller 120 can be used to receive the feedback signal of the remote server 130 through the remote transmission of the network.

The output device 150 is connected to the microcontroller 120 to provide feedback action according to the feedback signal. The output device 150 is similar to the output device 30 in FIG. 1 and includes a light-emitting device, a vibrator, a sound amplifier, or a text-graphic display device. Text-graphic display devices include small portable displays. In order to cope with the environment in which it is inconvenient to communicate with voice, in some embodiments, the feedback action of the output device 150 does not include voice feedback.

The distance sensor 160 is connected to the microcontroller 120 to sense the distance between the notification device 100 and an object. For example, the distance sensor 160 is, for example, an ultrasonic distance sensing device. In some embodiments, the distance sensor 160 uses infrared rays for distance sensing, or uses a millimeter-wave radar or a submillimeter-wave radar. Due to the short wavelength used, it can have a wider range. Sensing range, able to detect objects in a larger angular range.

Please refer to Figure 5. FIG. 5 is a flow chart of a notification method 200 provided by the notification device 100 according to an embodiment of the present disclosure, illustrating the specific flow of the notification device 100 from receiving an environmental sound signal to issuing a feedback action for warning.

In the process 210 of the notification method 200, the sound sensor 110 of the notification device 100 detects the environment to obtain an analog sound signal.

Continuing with process 210, in process 220, microcontroller 120 transmits an analog audio signal to server 130, eg, through a network.

In the process 230 , the server 130 recognizes the analog sound signal according to the sound recognition module 135 . Through the identification of the sound identification module 135, the server 130 can obtain the sound contained in the analog sound signal, such as the warning sound from a person and the specific content of the warning sound, or the sound of engineering equipment.

In the process 240 , the server 130 can classify the type of the analog sound signal through the classification module 140 . In the process 250, the server 130 outputs a feedback signal according to the type of the sound signal. In other words, a type of sound signal can correspond to a feedback signal. The type of sound signal referred to here is classified according to the response method after receiving the sound signal, for example, it is used to warn of danger or to call for communication.

In some embodiments, the sound signals in the working environment can be classified into multiple types, the types of the sound signals correspond to a situation, and the situations correspond to a feedback action. The types of sound signals are limited, and can be customized to increase according to the situation.

For example, in some implementations, there is only one type of sound signal "at risk." The notification device 100 receives the sound signal (process 210 ), and after uploading to the server (process 220 ) and completing the identification of the sound signal (process 230 ), learns that the content of the sound signal is to notify the user that there is danger (the content of the sound signal may be At this time, the notification device 100 can classify the sound signal into a type of “dangerous”, so that the server 130 outputs a corresponding feedback signal to the output device 150 to notify the notification device 100 of the User is at risk.

Specifically, in another practical example, there are six types of sound signals, including flashing left in danger, flashing right in danger, vibrating, other types of danger, moving to the right, and alerting someone to call. For example, the notification device 100 receives the sound signal (process 210 ), after uploading to the server (process 220 ) and completing the identification of the sound signal (process 230 ), the notification device 100 learns that the content of the sound signal is to notify the user that the right side is dangerous, It should flick to the left, and the notification device 100 can classify the sound signal as a type that is dangerous to flick to the left (process 240 ). Then, the server 130 outputs a feedback signal flashing to the left (process 250).

In some embodiments, the distance sensor 160 can also provide information about the environment around the user of the notification device 100 , so that the server 130 can make more accurate judgments. For example, in some implementations, the large-sized instrument for work moves from the right rear to the user of the notification device 100 , the sound sensor 110 detects the sound message of the sound of the large-sized instrument, and the distance sensor 160 senses the sound of the large instrument. When it is detected that an object is approaching from the right rear, the server 130 can identify and classify the type of the sound signal according to the above information is to flash to the left, so as to provide a feedback signal of flashing to the left.

Continuing with process 250, at process 260, the microcontroller 120 receives a feedback signal from the server 130 remotely via the network.

In the process 270, the output device 150 connected to the microcontroller 120 performs a feedback action according to the feedback signal. For example, the output device 150 may be a vibrator disposed on the left and right shoulders of the user. When the microcontroller 120 receives the feedback action of flashing to the left, the vibrator on the left shoulder of the user vibrates, and the vibrator on the left shoulder vibrates in real time through tactile sensation. A warning is issued to the user of the notification device 100 .

In some embodiments, the notification device 100 may be further connected to the main console. The main console can be used to manage one or more notification apparatuses 100 at the same time, or a wearable device provided with the notification apparatuses 100 . For example, the main console can actively send a feedback signal to a specific notification device 100 to directly drive the output device to provide alerts. In this way, the active notification is provided in the above manner, which can further strengthen the warning function of the notification device 100 . In some embodiments, the main console may further set one or more notification devices 100 into a plurality of different groups, so as to notify a specific group or all the notification devices 100 in different situations in a high-noise environment.

In the present embodiment, the voice recognition module 135 and the classification module 140 can be customized through machine learning, so as to achieve the recognition and classification of voice signals in a customized way, so as to respond to different types of working environments, Please see below for details.

Please refer to Figure 6. FIG. 6 is a flowchart illustrating a training method 300 for training the voice recognition module 135 according to an embodiment of the present disclosure.

As shown in the figure, in the process 310, the environment is detected by the sound sensor 110 to obtain an analog sound signal. The user of the notification device 100 can select different detection environments according to actual needs.

In some embodiments, the sound sensor 110 can detect the signal according to the signal detection theory (SDT) by means of sound dynamic detection.

In the process 320, after the sound sensor 110 detects the analog sound signal in the environment, the analog sound signal is converted into a time-domain digital sound file through digital processing. In some embodiments, digital processing may be performed by microcontroller 120 . In some embodiments, the digital processing may also be processed remotely by the server 130 . In some embodiments, the time-domain digital sound file can be further divided into several specific sound frames according to time through frame blocking processing, and the signals in the sound frames can be processed and analyzed.

Following the process 320, in the process 330, the time-domain digital audio file is converted into a frequency-domain digital audio file. Specifically, the server 130 or other computer devices connected to the server 130 can perform fast Fourier transform (FFT) on the time-domain digital audio files to convert the time-domain digital audio files into frequency-domain digital audio files. files. In some embodiments, by creating a frequency-domain digital sound file, it can be further used to obtain a spectrogram, which corresponds to the intensities of the time-domain digital sound file at different frequencies at different times.

Continuing the process 330, in the process 340, a sound feature value extraction module is used to extract the feature values of the frequency-domain digital sound file. The sound feature value extraction module is arranged in the server 130 . The eigenvalues of frequency-domain digital sound files correspond to different sounds. For example, the sound produced by the engineering equipment has different characteristics from the human voice, and these characteristics will be expressed in, for example, a spectrogram or a spectrogram of the sound. By analyzing the spectrogram or spectrogram of the frequency-domain digital sound file, the characteristic values of the frequency-domain digital sound file can be extracted from it, so as to distinguish the difference between the sound emitted by the engineering equipment and the human voice.

For example, the sound feature value extraction module includes the use of Mel-Frequency Cepstral Coefficients (MFCCs) method. Through the calculation module of the sound feature value extraction module, the frequency-domain digital sound file can be converted into a corresponding corresponding Mel-Frequency Cepstrum (MFC) to obtain the corresponding Mel-cepstrum coefficients. The Mel cepstral coefficient can be used as the characteristic value of the frequency-domain digital sound file, so as to obtain the sound corresponding to the frequency-domain digital sound file, such as the sound of engineering equipment or human voice. In some embodiments, the sound feature value extraction module may use the Deep Neural Networks (DNN) technology in the field of artificial intelligence to extract the feature values of the frequency-domain digital sound file. Deep neural network technology has good performance in image recognition. Therefore, conceptually, the sound corresponding to the image of the frequency-domain digital sound file can be identified through image recognition by converting the frequency-domain digital sound file into an image, so as to obtain the corresponding feature value.

Specifically, in one embodiment, the server 130 includes a convolutional neural network (CNN) model. In the deep neural network technology, the convolutional neural network module can effectively realize the function of image recognition. The convolutional neural network model can pre-input a sequence of spectrograms provided by other sounds to complete the training of image recognition. A sequence of spectrograms may refer to frequency intensity distributions arranged in a sequence and corresponding to different times. For example, for the sound of a work tool or a human voice, a plurality of sets of corresponding series of spectrograms can be provided as the basis for image recognition. In this way, after completing the learning of image recognition, another sequence of spectrograms can be input into the convolutional neural network model, and the convolutional neural network model can obtain the other sequence of spectrograms through image recognition. The sounds are similar, so that a corresponding eigenvalue can be output. In some embodiments, the sound used to train the convolutional neural network model is sampled in an actual working environment, thereby creating a customized recognition scheme according to the actual environment. The user of the notification device 100 can detect the analog sound signal in the environment as required, convert it into a frequency domain digital sound file for input, and then perform training according to the existing human voice or tool sound file.

As such, another embodiment of the process 340 may be implemented as follows. First, convert the frequency-domain digital audio file into a sequence of spectrograms. A spectrogram presents the intensity of different frequencies as a function of time. Here, a sequence of frequency intensity distribution diagrams of the frequency-domain digital audio files at different times can be output. Then, a sequence of spectrograms of the frequency-domain digital sound file is input into the convolutional neural network model in the sound feature value extraction module, that is, the feature value of the frequency-domain digital sound file can be output.

In the process 350, the voice recognition module 135 can be trained according to the frequency-domain digital voice file and its characteristic values. The training voice recognition module 135 can be applied to deep neural networks in the field of artificial intelligence. The eigenvalues of the frequency-domain digital sound file give the sound that corresponds to the human voice or tools. When the characteristic value of the frequency-domain digital sound file indicates that it is a human voice, the message content corresponding to the frequency-domain digital sound file is further input, so as to train the voice recognition module 135 . When the characteristic value of the frequency-domain digital sound file indicates that it is the sound of tools and instruments, corresponding context information can be provided. In this way, when the training sound recognition module 135 receives the sound signal, it can identify whether the sound signal is a human voice or the sound of a tool. Sounds provide contextual information. In some embodiments, the microcontroller 120 can be directly connected to a single-chip computer, and under the premise of being easy to carry, edge computing of voice recognition can be realized. Examples of single-chip computers include a raspberry pi.

Please refer to Figure 7. FIG. 7 illustrates a flowchart of a training method 400 for training a classification module 140 according to an embodiment of the present disclosure. Similar to the voice recognition module 135, the classification module 140 can also achieve customized training through a deep neural network. The classification module 140 is used for distinguishing the types of different analog sound signals, so as to provide appropriate feedback signals.

In process 410, an analog audio signal is input. In the process 420, the input analog audio signal can be recognized by, for example, the audio recognition module 135.

Then, in the process 430, context information corresponding to the analog sound signal is input. For example, when an analog sound signal is input, it is recognized that someone has issued a warning to flash to the left, and the corresponding situation at this time must be input to flash to the left.

In process 440, the classification module 140 can be trained according to the analog sound signal and its corresponding context. Specifically, the recognized analog sound signal is used as input, and the corresponding specific situations are used as training targets, so that the classification module 140 can be trained to classify the recognized analog sound signal into different situations. A different situation is, for example, the situation to flick to the left as described above. Different situations correspond to different types of sound signals. In this way, the notification device 100 is substantially combined with the wireless network, and the setting of the AI identification parameters can also be personalized, and the server 130 has to receive different context information for retraining. This is one embodiment of the IoT architecture of the overall service of the notification device 100 of the present disclosure. Besides, the microcontroller 120 can also implement the alert function outside the IoT architecture. For example, the microcontroller 120 integrated with the function of judging the volume of the sound signal can be used to check the abnormal change of the ambient volume, so as to send out another feedback signal for warning notification. The specific process is similar to that shown in FIG. 2 , and the notification device 100 can perform the same functions as the notification device 1 . In this way, the notification device 100 can also play the role of warning notification in an environment not connected to the Internet.

Please refer to Figure 8, Figure 9 and Figure 10. FIGS. 8 to 10 respectively illustrate a front view, a back view and a perspective view of the inside of the pocket of a smart vest 500 as a wearable device according to an embodiment of the present disclosure. In this embodiment, the notification device 100 is disposed on the vest 505 to serve as a smart vest 500 . In some embodiments, garments other than vest 505 may also be used.

Refer to FIGS. 8 and 9 at the same time. As shown, the smart vest 500 includes a front side 510 , a back side 530 , and a shoulder 520 connecting the front side 510 and the back side 530 . The front side 510 is provided with a pocket 513 for accommodating a mobile phone and providing a network. The back 530 of the smart vest 500 is also provided with a pocket 533 . The pocket 533 is used to accommodate and fix the components of the notification device 100 , including the sound sensor 110 , the distance sensor 160 and the circuit board 170 .

As shown in FIG. 10 , the sound sensor 110 , the microcontroller 120 , the power supply module 180 for supplying power, and the distance sensor 160 of the notification device 100 are integrated on the support plate 170 . The power supply module 180 includes a battery and a switch. The wires may be integrated on the circuit board 170, inside the interlayer, or on the opposite side.

In this embodiment, the sound sensor 110 , the distance sensor 160 and the circuit board 170 are disposed in the pocket 533 of the back 530 of the smart vest 500 . Since the user's eyes are not easy to focus on the back, the sensor 110 for detecting ambient sound and the distance sensor 160 are arranged on the back 530 of the smart vest 500, so that the notification device 100 can better play the role of the notification device 100 to detect danger and issue a warning. effect. In some embodiments, the exposed portions of the sound sensor 110 and the distance sensor 160 are provided with waterproof structures to adapt to different environmental changes. In this embodiment, the output device 150 disposed on the smart vest 500 includes a light bar 153 and a vibrator 156 . The vibrator 156 is connected to the circuit board 170 by wires 185 .

In conclusion, the present disclosure provides a notification device and a wearable device using the notification device. The notification device can detect the volume of the environment in a period of time, so as to notify the user immediately when the volume of the environment changes. The notification device can also be connected to the server remotely by using the microcontroller network. This is not only easy to carry, but also the server can identify and classify the received sound signal, so as to provide feedback according to the type of the sound signal. The type of sound signal is, for example, human voice or sound of engineering equipment. The wearable device is, for example, a smart vest combined with a notification device, which is easy to wear. By arranging output devices such as vibrators and light bars on it, users can easily perceive environmental changes by means other than sound, which is conducive to real-time communication and warning. The notification device is further configured to facilitate customized training to provide more accurate identification and warning effects in different working environments.

Although the present disclosure has been disclosed as above in embodiments, it is not intended to limit the present disclosure. Anyone with ordinary knowledge in the art can make various changes and modifications without departing from the spirit and scope of the present disclosure. Therefore, the present disclosure The scope of protection shall be determined by the scope of the appended patent application.

1,100: Notification Device 10,110: Sound sensor 20,120: Microcontrollers 30,150: Output device 130: Server 135: Voice recognition module 140: Classification Module 145: Processor 150: Output device 153: Light Bar 156: Vibrator 160: Distance sensor 170: circuit board 180: Power supply module 185: Wire 200: Notification method 210~270: Process 300: Training Methods 310~350: Process 400: Training Methods 410~440: Process 500: Wisdom Vest 505: Vest 510: front 513: Pocket 520: Shoulder 530: Back 533: Pocket 600: notification method 610~650: Process

The advantages and drawings of the present disclosure should be better understood from the following embodiments and with reference to the drawings. The descriptions of these drawings are merely exemplary embodiments, and therefore should not be construed to limit individual embodiments or to limit the scope of the claimed invention. FIG. 1 illustrates a block diagram of a notification device according to an embodiment of the present disclosure; FIG. 2 illustrates a flowchart of a notification method according to an embodiment of the present disclosure; FIG. 3 illustrates a block diagram of a notification device according to an embodiment of the present disclosure; FIG. 4 illustrates a block diagram of a server according to an embodiment of the present disclosure; 5 is a flowchart illustrating a notification method provided by a notification device according to an embodiment of the present disclosure; 6 is a flowchart illustrating a training method for training a voice recognition module according to an embodiment of the present disclosure; 7 is a flowchart illustrating a training method for training a classification module according to an embodiment of the present disclosure; and FIGS. 8 to 10 respectively show a front view, a back view and a perspective view of the inside of a pocket of a smart vest as a wearable device according to an embodiment of the present disclosure.

1: Notification device

10: Sound sensor

20: Microcontroller

30: Output device

Claims (11)

A notification device, comprising: a pressure sensor for detecting the environment to provide a pressure signal; a microcontroller connected to the pressure sensor to receive the pressure signal, wherein the microcontroller is used to calculate a dynamic threshold value of the pressure signal in a period of time, when the magnitude of the pressure signal is greater than the dynamic threshold value, the The microcontroller transmits a first feedback signal to the output device; and An output device is connected to the microcontroller, wherein the output device is used for providing a first feedback action according to the first feedback signal. The notification device of claim 1, wherein the output device includes a light-emitting device, a vibrator, a sound amplifier, or a text-graphic display device. The notification device according to claim 1, wherein the pressure sensor is a sound sensor, and the dynamic average value is an average volume value in the period. The notification device of claim 3, further comprising a distance sensor connected to the microcontroller and a circuit board, the sound sensor and the distance sensor are integrated on the circuit board. The notification device of claim 3, further comprising a server, wherein the server is connected to the microcontroller through a network, the microcontroller transmits the sound signal to the server, and the server recognizes and classifies The type of the sound signal transmits a second feedback signal to the microcontroller according to the type of the sound signal, and the output device provides a second feedback action according to the second feedback signal. The notification device of claim 3, further comprising a server, wherein the server is connected to the microcontroller through a network, the microcontroller transmits the sound signal to the server, and the server recognizes and classifies The type of the sound signal transmits a second feedback signal to the microcontroller according to the type of the sound signal, and the output device provides a second feedback action according to the second feedback signal. The notification device of claim 6, wherein the server further comprises: a sound identification module for identifying the sound signal; a classification module for classifying the identified type of the sound signal; and a processor for providing the second feedback signal according to the type of the sound signal. A wearable device, comprising: a notification device as described in claim 1; and A piece of clothing, wherein the pressure sensor, the microcontroller and the output device of the notification device are disposed on the clothing. A notification method that includes: detect the environment to provide a pressure signal; processing the pressure signal to obtain a dynamic average of the pressure signal over a period of time; Set a dynamic threshold value according to the dynamic average value; Confirm whether the current volume of the sound signal exceeds the dynamic threshold; if the dynamic average of the sound signal exceeds the threshold, sending a feedback signal to an output device; and The output device provides a feedback action according to the feedback signal. The notification method of claim 9, wherein the pressure signal is a sound signal, and the dynamic average value is a volume average value of the sound signal during the period. A notification method that includes: Detect the environment to obtain an analog sound signal; The sound identifies the content of the analog sound signal and thereby classifies the type of the analog sound signal; outputting a feedback signal according to the type of the analog sound signal; and Make an output device perform a feedback action according to the feedback signal.

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