A fall monitoring method, a wearable device

Abstract

An embodiment of the present application provides a fall monitoring method and a wearable device. The fall monitoring method first detects wearing data and determines a wearing status parameter according to the wearing data. Secondly, when the wearing status parameter indicates that the user is wearing the wearable device, it acquires the motion data of the wearable device. Thirdly, when the motion data is greater than the preset data, it acquires environmental sound data. Finally, it determines whether the user has fallen according to the motion data and the environmental sound data. By acquiring the motion data and the environmental sound data of the wearable device, the present application determines whether the change trends of the motion data and the environmental sound data of the wearable device are the same to determine the motion state of the wearable device in the direction pointing to the center of the earth, and further determines whether the user has fallen, improving the accuracy of monitoring the user's fall event.

Description

Technical Field

[0001] This application relates to the technical field of smart wearable devices, and more particularly to a fall monitoring method and a wearable device. Background Art

[0002] In recent years, with the acceleration of the aging of the social population, many problems have emerged. For example, the mobility, hearing ability, and reaction ability of the elderly are all declining, and there are often incidents where the elderly accidentally fall or suddenly become ill and cannot receive timely treatment after falling. Therefore, the elderly also have a strong demand for well-being monitoring services. At present, the hearing aids on the market only have basic hearing aid functions and cannot effectively determine the user's fall events, resulting in serious consequences.

[0003] Therefore, there is an urgent need for a fall monitoring method and a wearable device to solve the above technical problems. Summary of the Invention

[0004] The embodiments of this application provide a fall monitoring method and a wearable device to solve the technical problem that the existing hearing aids cannot effectively determine the user's fall events.

[0005] This application proposes a fall monitoring method for use in a wearable device. The fall monitoring method includes:

[0006] Detecting the wearing data and determining the wearing state parameters according to the wearing data;

[0007] When the wearing state parameters indicate that the user is wearing the wearable device, obtaining the motion data of the wearable device;

[0008] When the motion data is greater than the preset data, obtaining the environmental sound data;

[0009] Determining whether the user has fallen according to the motion data and the environmental sound data.

[0010] In the fall monitoring method of this application, the step of obtaining the motion data of the wearable device includes:

[0011] Obtaining and determining the reference acceleration of the wearable device pointing to the center of the earth at different times according to the speeds of the wearable device at different times;

[0012] Determining the motion data of the wearable device according to the reference accelerations of the wearable device at different times.

[0013] In the fall monitoring method of this application, the wearable device includes a microphone. The step of obtaining the environmental sound data when the motion data is greater than the preset data includes:

[0014] When the reference acceleration is greater than the preset acceleration, send a low-frequency enhancement instruction to the microphone;

[0015] Use the microphone to obtain air flow data passing through the wearable device to determine the environmental sound data.

[0016] In the fall monitoring method of the present application, the step of using the microphone to obtain air flow data passing through the wearable device to determine environmental sound data includes:

[0017] Obtain the correlation between the wind speed and the air flow data;

[0018] Obtain the wind speed passing through the wearable device, and based on the correlation between the wind speed and the air flow data, determine the interference data;

[0019] Use the microphone to obtain air flow data passing through the wearable device;

[0020] Determine the environmental sound data according to the air flow data and the interference data.

[0021] In the fall monitoring method of the present application, the step of determining whether the user has fallen according to the motion data and the environmental sound data includes:

[0022] Obtain the environmental sound data and the motion speed at different times;

[0023] When the motion speed and the environmental sound data are positively correlated, determine that the user has fallen.

[0024] In the fall monitoring method of the present application, the wearable device includes a microphone, and the step of obtaining environmental sound data when the motion data is greater than the preset data further includes:

[0025] When the reference acceleration is greater than the preset acceleration, send a low-frequency enhancement instruction to the microphone;

[0026] Use the microphone to obtain the user's collision data and the air flow data passing through the wearable device to determine the environmental sound data.

[0027] In the fall monitoring method of the present application, after the step of determining whether the user has fallen according to the motion data and the environmental sound data, it further includes:

[0028] Obtain the first moment when the microphone obtains the collision data and the second moment when the velocity of the wearable device mutates in the direction of the earth's center;

[0029] When the difference between the first moment and the second moment is within a preset range, it is determined that the user has fallen.

[0030] In the fall monitoring method of the present application, after the step of determining whether the user has fallen according to the motion data and the environmental sound data, the method further includes:

[0031] When the user is in a fallen state, physiological characteristic data of the user is acquired;

[0032] When the physiological characteristic data is greater than a preset range, the physiological characteristic data of the user is uploaded to an alarm platform.

[0033] In the fall monitoring method of the present application, after the step of determining whether the user has fallen according to the motion data and the environmental sound data, the method further includes:

[0034] When the user is in a fallen state, a help inquiry is sent to the user;

[0035] When the help voice data of the user is received, or the user does not reply within a preset time, the location information and fall data of the user are uploaded to an alarm platform.

[0036] The present application also provides a wearable device, which includes:

[0037] A wearing unit, configured to detect wearing data and determine a wearing state parameter according to the wearing data;

[0038] A motion data unit, configured to acquire motion data of the wearable device when the wearing state parameter indicates that the user wears the wearable device;

[0039] An environmental sound unit, configured to acquire environmental sound data when the motion data is greater than preset data;

[0040] A fall determination unit, configured to determine whether the user has fallen according to the motion data and the environmental sound data.

[0041] Beneficial effects: The embodiment of the present application provides a fall monitoring method and a wearable device. The fall monitoring method first detects wearing data and determines a wearing state parameter according to the wearing data. Secondly, when the wearing state parameter indicates that the user is wearing the wearable device, it obtains the motion data of the wearable device. Thirdly, when the motion data is greater than the preset data, it obtains environmental sound data. Finally, it determines whether the user has fallen according to the motion data and the environmental sound data. By obtaining the motion data and the environmental sound data of the wearable device, the present application determines whether the change trends of the motion data and the environmental sound data of the wearable device are the same to determine the motion state of the wearable device pointing to the center of the earth, and further determines whether the user has fallen, improving the accuracy of determining the user's fall event. Description of the Drawings

[0042] The following will clearly show the technical solutions and other beneficial effects of the present application by describing the specific embodiments of the present application in detail with reference to the accompanying drawings.

[0043] Figure 1 It is a schematic diagram of the interaction scenario of the wearable device of the present application.

[0044] Figure 2 It is a flowchart of a monitoring method based on a wearable device of the present application.

[0045] Figure 3 It is the first structural diagram of the wearable device of the present application.

[0046] Figure 4 It is a flowchart of the intelligent self-rescue system of the wearable device of the present application.

[0047] Figure 5 It is the second structural diagram of the wearable device of the present application.

[0048] Figure 6 It is a schematic diagram of the structure of the hearing aid provided by the embodiment of the present application. Specific Embodiments

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0050] Please refer to Figure 1 , Figure 1Schematic diagram of the interaction scenario of the wearable device provided by the embodiment of the present application. The system can include the first terminal, the second terminal and the data server connected and communicated through the Internet composed of various gateways, etc., which will not be elaborated here.

[0051] In this embodiment, the first terminal may be a wearable device, such as a hearing aid, a Bluetooth headset, smart glasses, a smart watch, a smart bracelet, a smart ring, etc.; the second terminal may include a mobile terminal, a personal computer, a tablet computer, etc.; the data server includes a local server and / or a remote server, etc. The data server may be deployed on the local server, or may be partially or fully deployed on the remote server.

[0052] In this embodiment, the first terminal first obtains the wearing data, determines whether the user wears the wearable device according to the wearing data, secondly, when the user wears the wearable device, obtains the motion data of the wearable device, thirdly, when the motion data is greater than the preset data, obtains the environmental sound data of the wearable device, and finally determines whether the user falls according to the motion data and the environmental sound data of the wearable device; after determining that the user falls, the first terminal will send a voice to the user for help and consultation; when receiving the user's help voice data, or when the user does not reply within the preset time, the first terminal may send the user's location information and fall data to the second terminal and / or the data server.

[0053] It should be noted that Figure 1 The shown scenario schematic diagram is an example. The server and scenario described in the embodiment of the present application are for more clearly explaining the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided by the embodiment of the present application. Those skilled in the art know that with the evolution of the interaction system and the emergence of new service scenarios, the technical solution provided by the embodiment of the present application is equally applicable to similar technical problems. The following will be described in detail respectively. It should be noted that the description order of the following embodiments does not serve as a limitation on the preferred order of the embodiments.

[0054] Please refer to Figure 2 and Figure 3 , the present application proposes a fall monitoring method for use in the wearable device 400. The fall monitoring method includes:

[0055] 101: Detect the wearing data and determine the wearing state parameter according to the wearing data;

[0056] 102: When the wearing state parameter indicates that the user wears the wearable device, obtain the motion data of the wearable device;

[0057] 103: When the motion data is greater than the preset data, obtain the environmental sound data;

[0058] 104: Determine whether the user has fallen according to the motion data and the environmental sound data.

[0059] In this embodiment, the present application determines whether the change trends of the motion data and the environmental sound data of the wearable device 400 are the same by obtaining the motion data and the environmental sound data of the wearable device 400, so as to determine the motion state of the wearable device 400 pointing to the center of the earth, and then determine whether the user has fallen, improving the accuracy of determining the user's fall event.

[0060] In this embodiment, the wearable device 400 is described by taking a hearing aid as an example, aiming at the determination of sudden situations when the elderly use a hearing aid, such as a fall event.

[0061] In this embodiment, when the user wears the wearable device 400, the wearable device 400 receives a wearing instruction, makes the wearable device 400 in a working state, and enables the wearable device 400 to trigger the fall monitoring function.

[0062] In this embodiment, the wearing state parameter may include wearing data and carrying data. For example, when the user wears a hearing aid, the hearing aid contacts the user's skin, thereby triggering the fall monitoring function of the wearable device 400; or, when the hearing aid is taken out of the charging case, the hearing aid is automatically connected to the mobile terminal and triggers the fall monitoring function of the wearable device 400; or, when the hearing aid identifies that the user is in a motion state, such as walking, that is, when the user carries the hearing aid on the body without wearing it, the hearing aid can automatically trigger the fall monitoring function to avoid sudden fall events when the user is in motion.

[0063] In this embodiment, during the user's fall, the center of gravity of the human body will suddenly fall in the direction pointing to the center of the earth. Therefore, at the moment of falling, there is an acceleration of the center of gravity of the human body pointing to the center of the earth. By determining whether the wearable device 400 has an acceleration pointing to the center of the earth, the motion state of the user can be effectively determined at the initial stage of the fall. Therefore, the step of obtaining the motion data of the wearable device 400 includes: obtaining and determining the reference acceleration of the wearable device 400 pointing to the center of the earth according to the speeds of the wearable device 400 at different times; determining the motion data of the wearable device 400 according to the reference accelerations of the wearable device 400 at different times..

[0064] In this embodiment, please refer to Figure 3, the wearable device 400 may include a triaxial acceleration sensor 310, which can obtain the velocities of the user at different times on the X-axis, Y-axis, and Z-axis through the triaxial acceleration sensor 310, and obtain the acceleration values of the user at different times on the X-axis, Y-axis, and Z-axis according to the velocities of the user at different times on the X-axis, Y-axis, and Z-axis. At the same time, the acceleration value in the Z direction is used as the reference acceleration, that is, this reference acceleration is the acceleration pointing to the center of the earth when the user falls.

[0065] In this embodiment, during the user's fall, due to the action of gravity, the center of gravity of the human body has an acceleration pointing to the center of the earth, and the acceleration of the user in the Z-axis direction will mutate from 0. This acceleration can be used as one of the criteria for determining whether the user has fallen. Therefore, the wearable device 400 of the present application uses the acceleration pointing to the center of the earth as the user's motion data, and determines the initial motion state of the user according to the mutation of this motion data.

[0066] In this embodiment, please refer to Figure 3 , the wearable device 400 may further include a triaxial angular velocity sensor 320, which can collect the changes in the angular velocities of the user at different times in the X-axis, Y-axis, and Z-axis directions through the triaxial angular velocity sensor 320, and obtain the inclination information of the human body in combination with the changes in the acceleration of the user. Among them, the human body inclination angle is defined as the angle between the human body and the horizontal plane, which is about 0 degrees when the human body stands and about 90 degrees when lying flat.

[0067] In this embodiment, when the user makes a sudden downward movement such as squatting or lowering the head, the acceleration of the wearable device 400 in the Z-axis direction will also mutate. Therefore, only the change in the acceleration in the Z-axis direction cannot be used as the standard for the user's fall. And in this embodiment, the changes in the angular velocities of the user at different times in the X-axis, Y-axis, and Z-axis directions can be collected through the triaxial angular velocity sensor 320, and the posture of the user at different times can be accurately determined. At the same time, in combination with the change in the acceleration in the Z-axis direction, the accuracy of determining whether the user has fallen can be further improved.

[0068] In the above embodiment, when the user falls, the acceleration of the wearable device 400 in the Z-axis direction will mutate, but the change in acceleration only represents the motion condition of the wearable device 400 in the Z-axis direction. For example, when the user takes an elevator, the acceleration of the wearable device 400 in the Z-axis direction will mutate, but the user does not have a fall event. Therefore, the fall event cannot be accurately determined only through the triaxial acceleration sensor 310. The present application can use the environmental sound data during the user's fall as a further determination basis.

[0069] In this embodiment, please refer to Figure 3, the wearable device 400 may include a microphone 330. The microphone 330 in a hearing aid is mainly used to receive external sounds, convert the sounds into digital signals, and after amplifying the corresponding digital signals through an amplifier, convert the amplified digital signals into sound waves and transmit them to the user. Therefore, the step of obtaining ambient sound data when the motion data is greater than the preset data includes: when the reference acceleration is greater than the preset acceleration, sending a low-frequency enhancement instruction to the microphone 330; using the microphone 330 to obtain air flow data passing through the wearable device 400 to determine the ambient sound data.

[0070] In this embodiment, when the reference acceleration is less than the preset acceleration, for example, when the preset acceleration is 0, it is determined that the user has not fallen; when the reference acceleration is less than the preset acceleration, it is determined that the acceleration of the user in the Z-axis direction has a sudden change. When the user falls, it will affect the flow of air around the user, and according to the different speeds of the user in the Z-axis direction, the degree of air disturbance is different; therefore, when the user falls, the air flow speed passing through the wearable device 400 is different, and the air flow speed and the sound of the air are positively correlated. Therefore, the present application can obtain the air flow sound passing through the wearable device 400 through the microphone 330, and combine the change law of the acceleration of the wearable device 400 in the Z-axis direction to determine whether the user has fallen.

[0071] In this embodiment, since the sound of air flow belongs to low-frequency sound data, the conventional microphone 330 cannot accurately identify the change of the air flow sound. The present application can send a low-frequency enhancement instruction to the microphone 330 when the reference acceleration is less than the preset acceleration, increasing the function of the microphone 330 to receive low-frequency sound data, ensuring the accuracy of obtaining ambient sound data; in addition, since the air flow speed and the sound of the air are positively correlated, the greater the sound of the air, the greater the air flow speed, and the greater the speed of the user descending in the corresponding direction. At the same time, the sound is converted into a digital signal, and by analyzing the increased gradient of the digital signal and combining the change of the acceleration in the Z-axis direction, the accuracy of determining whether the user has fallen can be further improved.

[0072] This embodiment can increase the function of the microphone 330 to obtain low-frequency sound data when the reference acceleration is less than the preset acceleration, so that the microphone 330 can accurately obtain the air flow sound passing through the wearable device 400 at different times, and combine the change of the acceleration in the Z-axis direction to further improve the accuracy of determining whether the user has fallen.

[0073] When acquiring ambient sound data, since low-frequency sound data is acquired, there is noise in the environment where the user is located interfering with the microphone 330, affecting the accuracy of the microphone 330 in acquiring ambient sound data. For example, when the user is outdoors, the wind sound will also pass through the microphone 330.

[0074] In this embodiment, the step of using the microphone 330 to acquire air flow data passing through the wearable device 400 to determine the ambient sound data may include: acquiring the correlation between the wind speed and the air flow data; acquiring the wind speed passing through the wearable device 400, and determining interference data based on the correlation between the wind speed and the air flow data; using the microphone 330 to acquire the air flow data passing through the wearable device 400; and determining the ambient sound data according to the air flow data and the interference data.

[0075] In this embodiment, before acquiring the ambient sound data, the correlation between different ambient wind speeds and air flow data may be imported into the wearable device 400, and when the user's reference acceleration is less than the preset acceleration, the interference data of the wearable device 400 at the current moment may be determined according to the wind speed of the environment where the user is located, and the air flow data monitored by the microphone 330 may be corrected to obtain the ambient sound data, and then the motion state of the user may be determined according to the ambient sound data.

[0076] For example, when the user is walking outdoors and the ambient wind speed is a level 3 wind of 5 m / s, the microphone 330 acquires the interference data of the user when not falling according to the ambient wind speed, and converts the sound data into a first digital signal; when the user falls, there is an acceleration in the Z-axis direction. At this time, the function of the microphone 330 to acquire low-frequency sound data is increased, and at the same time, the air flow data generated by the fall and the interference data generated by the environment are acquired. Secondly, the low-frequency sound data acquired by the microphone 330 is converted into a second digital signal. The processor 500 of the wearable device 400 processes the first digital signal and the second digital signal to obtain the ambient sound data generated by the user's fall, and then the motion state of the user may be determined according to the ambient sound data.

[0077] In this embodiment, since the ambient wind is a vector, for example, a user may be affected by ambient winds from different directions, and when the user falls, only the air in the Z-axis direction is disturbed. Therefore, in order to ensure the measurement accuracy of the air flow data generated by the user's fall by the wearable device 400, the microphone 330 can measure only the interference data generated by the wind speed in the Z-axis direction and perform noise reduction processing on the ambient winds from other directions. For example, the microphone 330 can simultaneously measure the interference data at different times in the X-axis, Y-axis, and Z-axis directions, and only convert the interference data in the Z-axis direction into corresponding digital signals. When the user falls, the interference data and air flow data at different times in the X-axis, Y-axis, and Z-axis directions can be measured simultaneously, and only the interference data and air flow data in the Z-axis direction are converted into corresponding digital signals. By processing the digital signals, the ambient sound data generated in the Z-axis direction due to the user's fall can be obtained, and the user's motion state can be determined based on the ambient sound data in the Z-axis direction subsequently.

[0078] In the above embodiment, the step of determining whether the user has fallen according to the motion data and the ambient sound data includes: obtaining the ambient sound data and the motion speed of the wearable device 400 at different times; and determining that the user has fallen when the motion speed and the ambient sound data are positively correlated.

[0079] In this embodiment, during the user's fall, as time increases, the speed at which the user descends gradually increases, and the ambient sound data received by the microphone 330 is positively correlated with the user's speed in the Z-axis direction, that is, the greater the falling speed, the greater the air flow rate passing through the microphone 330. Therefore, in this application, the user's motion state can be determined based on the change trends of the user's motion speed and the ambient sound data.

[0080] For example, the user obtains the speeds of the user in the X-axis, Y-axis, and Z-axis at different times through the triaxial acceleration sensor 310, and simultaneously obtains the ambient sound data in the Z-axis direction at different times through the microphone 330. Secondly, the motion speed and the ambient sound data of the user in the Z-axis direction at different times are fitted. If the motion speed and the ambient sound data are positively correlated, it can be determined that the user has fallen.

[0081] In the above embodiment, when the user's fall is a soft collision, for example, when the user falls on an elastic object such as a sofa or a bed, the acceleration of the wearable device 400 in the Z-axis direction will change suddenly, and at the same time, the angular velocities of the user in the X-axis, Y-axis, and Z-axis directions at different times will also change in the same way as when falling. Therefore, it is impossible to accurately determine the fall event only through the triaxial acceleration sensor 310 and the triaxial angular velocity sensor 320.

[0082] In this embodiment, when the motion data is greater than the preset data, the step of obtaining the ambient sound data may further include: when the reference acceleration is greater than the preset acceleration, sending a low-frequency enhancement instruction to the microphone 330; using the microphone 330 to obtain the user's collision data and the air flow data passing through the wearable device 400 to determine the ambient sound data.

[0083] In this embodiment, when the user falls, in addition to the air turbulence generated by the acceleration in the Z-axis direction, there is also a collision sound generated by the user's hard collision. Therefore, the preliminary motion state of the user, such as whether there is a motion trend pointing to the center of the earth, can be determined according to the air flow data. At the same time, by obtaining the collision data, it can be judged whether the user has a hard collision, eliminating the situation of the user having a soft collision and improving the accuracy of monitoring the user's fall.

[0084] In this embodiment, during the user's fall, there are also interfering collision data. For example, when the user has a soft collision, there will also be a relatively slight collision sound. However, the sound data of soft collisions and hard collisions are somewhat different. Therefore, the wearable device 400 of the present application can obtain in advance the collision data of the human body with elastic objects, the ground, stools, walls and other objects, and match the collision data obtained by the microphone 330 with the collision data in the database to determine whether the user has a hard collision, so as to improve the accuracy of monitoring the user's fall.

[0085] In this embodiment, when the user has a hard collision, it is mainly the collision of the human bones with hard objects. Therefore, there may be a certain deviation when the collision sound is transmitted to the microphone 330 through the air. Therefore, the wearable device 400 of the present application may include an oscillator for obtaining collision data. When the user has a hard collision, the user's bones collide with hard objects, and the collision sound is directly transmitted to the oscillator in the wearable device 400 through the bones inside the human body to obtain the collision data, improving the accuracy of obtaining the collision data.

[0086] In the monitoring method of the present application, after the step of determining whether the user has fallen according to the motion data and the ambient sound data, it further includes: obtaining the first moment when the microphone 330 obtains the collision data and the second moment when the velocity of the wearable device 400 changes suddenly in the direction pointing to the center of the earth; when the difference between the first moment and the second moment is within a preset range, it is determined that the user has fallen.

[0087] In this embodiment, when a hard collision occurs to the user, due to the action of force, the velocity of the user in the Z-axis direction will suddenly change to 0. Since the motion state of the wearable device 400 is the same as that of the user, at the moment of the collision, the velocities of both the user and the wearable device 400 in the Z-axis direction will suddenly change to 0. Therefore, this application can assist in determining whether the user has fallen based on whether the difference between the first moment when the user has a collision and the second moment when the velocity of the wearable device 400 in the Z-axis direction suddenly changes to 0 is within a preset range.

[0088] In this embodiment, since the error between the sound received by the microphone 330 due to the collision and the sudden change of the velocity of the wearable device 400 in the Z-axis direction to 0 is at the microsecond level, it can be considered that the two occur simultaneously, that is, the difference between the first moment and the second moment can be 0.

[0089] This application introduces the microphone 330 to obtain the first moment of the collision data and the second moment when the velocity of the wearable device 400 in the Z-axis direction suddenly becomes 0, and further determines whether the user has fallen based on whether the difference between the first moment and the second moment is within a preset range, improving the accuracy of the wearable device 400 in monitoring the user's fall event.

[0090] In this embodiment, when a hard collision occurs to the user, the velocity of the user itself in the Z-axis direction will suddenly change to 0. However, due to the effect of inertia, the wearable device 400 still has a velocity in the Z-axis direction. At this time, the force between the wearable device 400 and the user causes the velocity of the wearable device 400 in the Z-axis direction to suddenly become 0. Therefore, this application can also introduce the third moment when the force exerted by the wearable device 400 on the user occurs, and determine whether the user has fallen based on whether the difference between any two of the first moment, the second moment, and the third moment is within a preset range, further improving the accuracy of the wearable device 400 in monitoring the user's fall event.

[0091] In this embodiment, when the user falls, in addition to the direct injuries caused by the fall, there are also indirect injuries caused by the fall. For example, when physiological characteristics such as heart rate, blood oxygen, and body temperature are abnormal, it may lead to irreparable accidents.

[0092] In the monitoring method of this application, after the step of determining whether the user has fallen according to the motion data and the environmental sound data, it may further include: when the user is in a fallen state, obtaining the physiological characteristic data of the user; when the physiological characteristic data is greater than the preset range, uploading the physiological characteristic data of the user to the alarm platform.

[0093] In this embodiment, the wearable device 400 may include a physiological feature module 340 for acquiring physiological feature data, and the physiological feature data may include heart rate data, blood oxygen data, body temperature data, respiratory data, blood pressure data, blood glucose data, etc.

[0094] In this embodiment, when the user falls, the user's heart rate and respiration may mutate due to the sudden fall. Therefore, when determining that the user has fallen, the monitoring frequency of the heart rate and respiration of the wearable device 400 for the user can be enhanced. When the physiological feature data exceeds the corresponding preset range, the physiological feature data of the user is uploaded to the alarm platform.

[0095] In this embodiment, the wearable device 400 can monitor the physiological feature data of the user in real time, and when the physiological feature data exceeds the corresponding preset range, the physiological feature data can be sent to the alarm platform and the guardian or doctor can be notified. For example, the normal range of heart rate is 60 - 100 beats per minute, and the heart rate of the elderly is slower than that of the young. For example, the abnormal range of heart rate can be set at less than 50 beats per minute and higher than 110 beats per minute, then this abnormal situation can be reported.

[0096] In the existing wearable device 400, when an abnormal health condition of the user is detected, there are different degrees of health abnormalities. At this time, the actual state of the user cannot be accurately judged only based on the relevant data. Therefore, it is necessary to determine whether to rescue the user according to the user's own needs.

[0097] In the monitoring method of this application, after the step of determining whether the user has fallen according to the motion data and the environmental sound data, it further includes: when the user is in a fallen state, sending a help consultation to the user; when receiving the user's help voice data, or when the user does not reply within the preset time, uploading the user's location information and fall data to the alarm platform.

[0098] In this embodiment, the alarm platform may be a medical cloud platform.

[0099] In this embodiment, please refer to Figure 3 , the wearable device 400 of this application includes a voice module 350 and a positioning module 360. The positioning module 360 is used to perform real-time positioning on the user's location, and the voice module 350 is used to enable the user to communicate with the guardian or doctor in real time.

[0100] Please refer to Figure 4 , Figure 4The flowchart of the intelligent self - rescue system of the wearable device of the present application. When the wearable device 400 detects that the user has fallen, it can send a help consultation to the user in real time through the voice module. If the user needs help, it triggers the alarm function of the wearable device 400, and directly sends the help information to the cloud platform, and then sends it to the guardian and doctor through this platform, for example, sending it to the guardian and doctor in the form of a text message or voice; or, when the user replies that they do not need help, the alarm function does not need to be triggered. In addition, the guardian or doctor can determine whether to rescue the user according to the physiological characteristic data of the user. For example, when the body temperature or heart rate of the user exceeds the preset range, the doctor or guardian can directly rescue the user; or, when the user does not reply to the help consultation within the preset time or does not ask for help for multiple help consultations, for example, when the user does not reply within 30 s, the wearable device 400 can upload the location information and fall data of the user to the cloud platform to rescue the user.

[0101] The embodiments of the present application provide a fall monitoring method and a wearable device. The fall monitoring method first detects the wearing data, and determines the wearing state parameter according to the wearing data. Secondly, when the wearing state parameter indicates that the user wears the wearable device 400, it obtains the motion data of the wearable device 400. Thirdly, when the motion data is greater than the preset data, it obtains the environmental sound data. Finally, according to the motion data and the environmental sound data, it determines whether the user has fallen; the present application determines whether the change trends of the motion data and the environmental sound data of the wearable device 400 are the same by obtaining the motion data and the environmental sound data of the wearable device 400, to determine the motion state of the wearable device 400 pointing to the center of the earth, and then determine whether the user has fallen, improving the accuracy of determining the user's fall event.

[0102] Please refer to Figure 5 , the present application also proposes a wearable device 400, which includes a wearing unit 410, a motion data unit 420, an environmental sound unit 430, and a fall determination unit 440.

[0103] In this embodiment, the wearing unit 410 can be used to detect the wearing data and determine the wearing state parameter according to the wearing data; the motion data unit 420 can be used to obtain the motion data of the wearable device 400 when the wearing state parameter indicates that the user wears the wearable device 400; the environmental sound unit 430 can be used to obtain the environmental sound data when the motion data is greater than the preset data; the fall determination unit 440 can be used to determine whether the user has fallen according to the motion data and the environmental sound data.

[0104] In the wearable device 400 of the present application, the wearable device 400 is further configured to obtain and determine the reference acceleration of the wearable device 400 pointing to the center of the earth according to the speeds of the wearable device 400 at different times; and determine the motion data of the wearable device 400 according to the reference accelerations of the wearable device 400 at different times.

[0105] In the wearable device 400 of the present application, the wearable device 400 is further configured to send a low-frequency enhancement instruction to the microphone 330 when the reference acceleration is greater than a preset acceleration; and obtain the air flow data passing through the wearable device 400 by using the microphone 330 to determine the environmental sound data.

[0106] In the wearable device 400 of the present application, the wearable device 400 is further configured to obtain the correlation between the wind speed and the air flow data; obtain the wind speed passing through the wearable device 400, and determine the interference data based on the correlation between the wind speed and the air flow data; obtain the air flow data passing through the wearable device 400 by using the microphone 330; and determine the environmental sound data according to the air flow data and the interference data.

[0107] In the wearable device 400 of the present application, the wearable device 400 is further configured to obtain the environmental sound data and the motion speed at different times; and determine that the user has fallen when the motion speed and the environmental sound data are positively correlated.

[0108] In the wearable device 400 of the present application, the wearable device 400 is further configured to send a low-frequency enhancement instruction to the microphone 330 when the reference acceleration is greater than a preset acceleration; and obtain the collision data of the user and the air flow data passing through the wearable device 400 by using the microphone 330 to determine the environmental sound data.

[0109] In the wearable device 400 of the present application, the wearable device 400 is further configured to obtain the first time when the microphone 330 obtains the collision data and the second time when the speed of the wearable device 400 pointing to the center of the earth changes suddenly; and determine that the user has fallen when the difference between the first time and the second time is within a preset range.

[0110] In the wearable device 400 of the present application, the wearable device 400 is further configured to obtain the physiological characteristic data of the user when the user is in a fallen state; and upload the physiological characteristic data of the user to the alarm platform when the physiological characteristic data is greater than a preset range.

[0111] In the wearable device 400 of the present application, the wearable device 400 is further configured to send a help consultation to the user when the user is in a falling state; when receiving the user's help voice data, or when the user does not reply within a preset time, upload the user's location information and fall data to an alarm platform.

[0112] In specific implementation, each of the above units can be implemented as an independent entity, or can be combined arbitrarily to be implemented as the same or several entities. For the specific implementation processes of the above device and each unit, as well as the beneficial effects achieved, reference can be made to the corresponding descriptions in the method embodiments applied to the nodes in the blockchain. For the sake of convenience and brevity of description, they will not be elaborated here.

[0113] Figure 6 is a schematic structural diagram of a hearing aid provided by an embodiment of the present application. As Figure 6 shown, the hearing aid 50 of this embodiment includes: one or more processors 500 (only one is shown in the figure), a memory 501, and a computer program 502 stored in the memory 501 and executable on the at least one processor 500. When the processor 500 executes the computer program 502, the steps in the above method embodiments for updating various audio playback parameters are implemented.

[0114] The hearing aid may include, but is not limited to, a processor 500 and a memory 501. Those skilled in the art can understand that Figure 6 this is only an example of the hearing aid 50 and does not constitute a limitation on the hearing aid 50. It may include more or fewer components than those shown in the figure, or combine some components, or different components. For example, the hearing aid may further include input / output devices, network access devices, buses, etc.

[0115] The so-called processor 500 may be a central processing unit (CPU), and this processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0116] The memory 501 may be an internal storage unit of the hearing aid 50, such as a hard disk or memory of the hearing aid 50. The memory 501 may also be an external storage device of the hearing aid 50, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the hearing aid 50. Further, the memory 501 may also include both an internal storage unit of the hearing aid 50 and an external storage device. The memory 501 is used to store the computer program and other programs and data required by the hearing aid. The memory 501 may also be used to temporarily store data that has been output or is to be output.

[0117] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the above division of each functional unit and module is used as an example. In practical applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working process of the units and modules in the above device can refer to the corresponding process in the foregoing method embodiment and will not be elaborated here.

[0118] The embodiment of the present application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the foregoing method embodiments can be implemented.

[0119] The embodiment of the present application also provides a computer program product. When the computer program product runs on the hearing aid combination, the hearing aid combination is enabled to execute the steps in the foregoing fall detection method embodiments; or when the computer program product runs on the audio source device, the audio source device is enabled to execute the steps in the foregoing fall detection method embodiments.

[0120] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0121] Those of ordinary skill in the art will realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0122] In the embodiments provided in this application, it should be understood that the disclosed device / hearing aid combination / audio source device and method can be implemented in other ways. For example, the device / hearing aid combination / audio source device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in electrical, mechanical or other forms.

[0123] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0124] When the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of this application, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0125] The above-described embodiments are only used to illustrate the technical solutions of this application, rather than to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of this application, and should all be included in the protection scope of this application.

Claims

1. A fall monitoring method, characterized in that, Used in a wearable device, the wearable device includes a microphone, and the fall monitoring method includes: Detecting wearing data and determining a wearing state parameter according to the wearing data; When the wearing state parameter indicates that the user is wearing the wearable device, obtaining the motion data of the wearable device; When the motion data is greater than a preset data, obtaining environmental sound data; Determining whether the user has fallen according to the motion data and the environmental sound data; The step of obtaining the motion data of the wearable device includes: Obtaining and determining a reference acceleration pointing to the center of the earth of the wearable device at different times according to the speeds of the wearable device at different times; Determining the motion data of the wearable device according to the reference accelerations of the wearable device at different times; The step of obtaining environmental sound data when the motion data is greater than a preset data includes: When the reference acceleration is greater than a preset acceleration, sending a low-frequency enhancement instruction to the microphone; Using the microphone to obtain air flow data passing through the wearable device to determine the environmental sound data.

2. The fall monitoring method according to claim 1, characterized in that, The step of using the microphone to obtain air flow data passing through the wearable device to determine environmental sound data includes: Obtaining the correlation between wind speed and air flow data; Obtaining the wind speed passing through the wearable device and determining interference data based on the correlation between wind speed and air flow data; Using the microphone to obtain air flow data passing through the wearable device; Determining the environmental sound data according to the air flow data and the interference data.

3. The fall monitoring method according to claim 2, characterized in that, The step of determining whether the user has fallen according to the motion data and the environmental sound data includes: Obtaining the environmental sound data and motion speed at different times; When the motion speed and the environmental sound data are positively correlated, determining that the user has fallen.

4. The fall monitoring method according to claim 1, characterized in that, The wearable device includes a microphone, and the step of obtaining environmental sound data when the motion data is greater than a preset data further includes: When the reference acceleration is greater than a preset acceleration, sending a low-frequency enhancement instruction to the microphone; Using the microphone to obtain the user's collision data and air flow data passing through the wearable device to determine the environmental sound data.

5. The fall monitoring method according to claim 4, characterized in that, After the step of determining whether the user has fallen according to the motion data and the environmental sound data, it further includes: Obtaining a first moment when the microphone obtains the collision data and a second moment when the speed of the wearable device pointing to the center of the earth changes suddenly; When the difference between the first moment and the second moment is within a preset range, determining that the user has fallen.

6. The fall monitoring method according to claim 1, characterized in that, After the step of determining whether the user has fallen according to the motion data and the environmental sound data, it further includes: When the user is in a fallen state, obtaining the user's physiological characteristic data; When the physiological characteristic data is greater than a preset range, uploading the user's physiological characteristic data to an alarm platform.

7. The fall monitoring method according to claim 1, characterized in that, After the step of determining whether the user has fallen according to the motion data and the environmental sound data, it further includes: When the user is in a fallen state, send a help consultation to the user; When receiving the user's help voice data, or when the user does not reply within a preset time, upload the user's location information and fall data to the alarm platform.

8. A wearable device, characterized in that,The wearable device includes a microphone, and further includes: A wearing unit, configured to detect wearing data and determine a wearing state parameter according to the wearing data; A motion data unit, configured to obtain the motion data of the wearable device when the wearing state parameter indicates that the user wears the wearable device; An ambient sound unit, configured to obtain ambient sound data when the motion data is greater than a preset data; A fall determination unit, configured to determine whether the user has fallen according to the motion data and the ambient sound data; The step of obtaining the motion data of the wearable device includes: Obtain and determine the reference acceleration of the wearable device pointing to the center of the earth at different times according to the speeds of the wearable device at different times; Determine the motion data of the wearable device according to the reference acceleration of the wearable device at different times; The step of obtaining ambient sound data when the motion data is greater than a preset data includes: When the reference acceleration is greater than a preset acceleration, send a low-frequency enhancement instruction to the microphone; Use the microphone to obtain the air flow data passing through the wearable device to determine the ambient sound data.

Images