CN115644854A

CN115644854A - Fall monitoring method and wearable device

Info

Publication number: CN115644854A
Application number: CN202211352059.8A
Authority: CN
Inventors: 宫春伟; 黄烈超
Original assignee: Xian TCL Software Development Co Ltd
Current assignee: Xian TCL Software Development Co Ltd
Priority date: 2022-10-31
Filing date: 2022-10-31
Publication date: 2023-01-31

Abstract

The embodiment of the application provides a fall monitoring method and wearable equipment, wherein the fall monitoring method comprises the steps of firstly detecting wearing data, determining wearing state parameters according to the wearing data, secondly acquiring motion data of the wearable equipment when the wearing state parameters represent that a user wears the wearable equipment, thirdly acquiring environment sound data when the motion data are larger than preset data, and finally determining whether the user falls or not according to the motion data and the environment sound data; according to the method and the device, whether the change trend of the motion data and the change trend of the environment sound data of the wearable device are the same or not is judged by acquiring the motion data and the environment sound data of the wearable device, so that the motion state of the wearable device in the direction pointing to the geocentric region is determined, whether the user falls down is determined, and the accuracy rate of monitoring the falling event of the user is improved.

Description

Fall monitoring method and wearable device

Technical Field

The application relates to the technical field of intelligent wearable equipment, in particular to a fall monitoring method and wearable equipment.

Background

In recent years, with the aging of the social population, many problems are revealed, for example, the mobility, hearing ability and reaction ability of the elderly are reduced, and the elderly often fall accidentally and cannot be cured in time after sudden illness falls, so that the elderly also have strong need for health care and supervision. However, hearing aids on the market at present only have a basic hearing aid function, and cannot effectively determine falling events of users, so that serious consequences are caused.

Therefore, there is a need for a fall monitoring method and a wearable device to solve the above technical problems.

Disclosure of Invention

The embodiment of the application provides a fall monitoring method and wearable equipment, which are used for solving the technical problem that the existing hearing aid cannot effectively judge the fall event of a user.

The application provides a fall monitoring method, which is used for wearable equipment, and the fall monitoring method comprises the following steps:

detecting wearing data and determining wearing state parameters according to the wearing data;

when the wearing state parameter represents that the user wears the wearable device, acquiring motion data of the wearable device;

when the motion data is larger than preset data, acquiring environmental sound data;

determining whether the user has fallen according to the motion data and the ambient sound data.

In the fall monitoring method of the present application, the step of acquiring the motion data of the wearable device includes:

acquiring and determining reference acceleration of the wearable equipment pointing to the geocentric at different moments according to the speeds of the wearable equipment at different moments;

determining motion data of the wearable device according to the reference acceleration of the wearable device at different moments.

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

when the reference acceleration is larger than a preset acceleration, sending a low-frequency enhancement instruction to the microphone;

acquiring air flow data through the wearable device with the microphone to determine the ambient sound data.

In the fall monitoring method of the present application, the step of acquiring air flow data through the wearable device with the microphone to determine ambient sound data comprises:

acquiring the association relation between the wind speed and the air flow data;

acquiring the wind speed passing through the wearable equipment, and determining interference data based on the incidence relation between the wind speed and the air flow data;

acquiring air flow data through the wearable device using the microphone;

determining the ambient sound data from the air flow data and the interference data.

In the fall monitoring method of the present application, the step of determining whether a user falls or not based on the motion data and the ambient sound data comprises:

acquiring the environmental sound data and the movement speed at different moments;

determining that the user has fallen when the speed of movement and the ambient sound data are positively correlated.

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

acquiring collision data of a user and air flow data through the wearable device with the microphone to determine the ambient sound data.

In the fall monitoring method of the present application, after the step of determining whether the user falls or not based on the motion data and the ambient sound data, the method further includes:

acquiring a first moment when the microphone acquires the collision data and a second moment when the wearable device suddenly changes speed pointing to the geocentric;

and when the difference value of the first moment and the second moment is within a preset range, determining that the user falls.

when the user falls down, acquiring physiological characteristic data of the user;

and when the physiological characteristic data is larger than the preset range, uploading the physiological characteristic data of the user to an alarm platform.

when the user falls down, help seeking consultation is sent to the user;

and when the help-seeking voice data of the user is received or the user does not reply within the preset time, uploading the position information and the falling data of the user to the alarm platform.

The application also provides a wearable device, which includes:

the wearing unit is used for detecting wearing data and determining wearing state parameters according to the wearing data;

the wearable device comprises a wearable device body, a wearable state parameter unit and a motion data unit, wherein the wearable device body is used for providing a wearable state parameter for a user;

the environment sound unit is used for acquiring environment sound data when the motion data is larger than preset data;

and the falling judging unit is used for determining whether the user falls or not according to the motion data and the environmental sound data.

Has the advantages that: the embodiment of the application provides a fall monitoring method and wearable equipment, wherein the fall monitoring method comprises the steps of firstly detecting wearing data, determining wearing state parameters according to the wearing data, secondly acquiring motion data of the wearable equipment when the wearing state parameters represent that a user wears the wearable equipment, thirdly acquiring environment sound data when the motion data are larger than preset data, and finally determining whether the user falls or not according to the motion data and the environment sound data; this application is through obtaining wearable equipment the motion data with environment sound data judges whether wearable equipment's motion data is the same with the trend of change of environment sound data to confirm that wearable equipment is in the motion state of pointing to the geocentric, and then confirm whether the user tumbles, improved the rate of accuracy of falling the incident and judging to the user.

Drawings

The technical solution and other advantages of the present application will become apparent from the detailed description of the embodiments of the present application with reference to the accompanying drawings.

Fig. 1 is an interaction scenario diagram of a wearable device according to the present application.

Fig. 2 is a schematic flowchart of a monitoring method based on a wearable device according to the present application.

Fig. 3 is a first structural view of the wearable device of the present application.

Fig. 4 is a flowchart of the intelligent self-rescue system of the wearable device of the present application.

Fig. 5 is a second structural view of the wearable device of the present application.

Fig. 6 is a schematic structural diagram of a hearing aid provided in an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Referring to fig. 1, fig. 1 is a schematic view of an interaction scenario of a wearable device according to an embodiment of the present application, where the system may include a first terminal, a second terminal, and a data server that are connected to each other through an internet composed of various gateways and the like, which are not described again.

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, or the like; the second terminal may comprise a mobile terminal, a personal computer, a tablet computer, etc.; the data server includes a local server and/or a remote server, and the data server may be deployed on the local server or may be partially or completely deployed on the remote server.

In this embodiment, a first terminal first acquires wearing data, determines whether a user wears a wearable device according to the wearing data, then acquires motion data of the wearable device when the user wears the wearable device, acquires environmental sound data of the wearable device when the motion data is larger than preset data, and finally determines whether the user falls down according to the motion data and the environmental sound data of the wearable device; when the user falls down, the first terminal sends voice to the user for help-seeking consultation; when the help-seeking voice data of the user is received or the user does not reply within the preset time, the first terminal can send the position information and the falling data of the user to the second terminal or/and the data server.

It should be noted that the scenario schematic diagram shown in fig. 1 is an example, and the server and the scenario described in the embodiment of the present application are used to illustrate the technical solution of the embodiment of the present application more clearly, and do not constitute a limitation to the technical solution provided in the embodiment of the present application, and it is known by a person of ordinary skill in the art that the technical solution provided in the embodiment of the present application is also applicable to similar technical problems with the evolution of an interactive system and the occurrence of a new service scenario. The following are detailed below. It should be noted that the following description of the embodiments is not intended to limit the preferred order of the embodiments.

Referring to fig. 2 and 3, the present application provides a fall monitoring method for use in a wearable device 400, the fall monitoring method comprising:

101: detecting wearing data and determining wearing state parameters according to the wearing data;

102: when the wearing state parameter represents that the user wears the wearable device, acquiring motion data of the wearable device;

103: when the motion data is larger than preset data, acquiring environmental sound data;

104: and determining whether the user falls or not according to the motion data and the environmental sound data.

In this embodiment, the motion data and the environmental sound data of the wearable device 400 are obtained, and whether the change trends of the motion data and the environmental sound data of the wearable device 400 are the same or not is determined, so that the motion state of the wearable device 400 in the direction of the geocentric region is determined, whether the user falls or not is determined, and the accuracy of determining the falling event of the user is improved.

In this embodiment, the wearable device 400 is described by taking a hearing aid as an example, and is used for determining that an emergency, such as a fall event, occurs when the elderly use the hearing aid.

In this embodiment, when the user wears the wearable device 400, the wearable device 400 receives a wearing instruction to put the wearable device 400 in an operating state, so that the wearable device 400 triggers a fall monitoring function.

In this embodiment, the wearing status parameter may include wearing data and carrying data. For example, when the user wears the hearing aid, the hearing aid is in contact with 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 box, the hearing aid is automatically connected with the mobile terminal and triggers the fall monitoring function of the wearable device 400; alternatively, the hearing aid may automatically trigger the fall monitoring function when the hearing aid identifies that the user is in motion, e.g. walking, i.e. when the user is carrying the hearing aid on his body and not wearing it, to avoid a sudden fall incident while the user is in motion.

In this embodiment, the user falls at the in-process that falls, and the human focus can fall to the direction that points to the geocentric suddenly, and consequently at the instant of falling, the human focus has the acceleration of pointing to the geocentric, through judging whether wearable equipment 400 has the acceleration of pointing to the geocentric, can carry out effectual judgement to user's motion state at the early stage of falling. Thus, the step of acquiring motion data of the wearable device 400 comprises: acquiring and determining reference acceleration of the wearable device 400 pointing to the geocentric at different moments according to the speeds of the wearable device 400 at different moments; determining motion data of the wearable device 400 according to the reference accelerations of the wearable device 400 at different moments in time. .

In this embodiment, please refer to fig. 3, the wearable device 400 may include a three-axis acceleration sensor 310, which may acquire the speeds of the user at different times on the X-axis, the Y-axis, and the Z-axis through the three-axis acceleration sensor 310, and obtain the acceleration values of the user at different times on the X-axis, the Y-axis, and the Z-axis according to the speeds of the user at different times on the X-axis, the Y-axis, and the Z-axis, and meanwhile, use the acceleration value in the Z-direction as a reference acceleration, that is, the reference acceleration is an acceleration pointing to the geocentric when the user falls down.

In this embodiment, during a fall of a user, due to the action of gravity, the gravity center of the human body has an acceleration pointing to the geocentric, the acceleration of the user in the Z-axis direction will suddenly change from 0, and the acceleration can be used as one of the criteria for determining whether the user falls, so the wearable device 400 of the present application uses the acceleration pointing to the geocentric as the motion data of the user, and determines the initial motion state of the user according to the sudden change of the motion data.

In this embodiment, referring to fig. 3, the wearable device 400 may further include a three-axis angular velocity sensor 320, and the three-axis angular velocity sensor 320 may collect the change of the angular velocity of the user at different times in the X-axis, Y-axis and Z-axis directions, and obtain the tilt angle information of the human body by combining the change of the acceleration of the user. The human body inclination angle is defined as the included angle between the human body and the horizontal plane, and the human body is about 0 degree when standing and about 90 degrees when lying.

In this embodiment, when the user performs a sudden downward motion such as squat or lowering his head, the acceleration of the wearable device 400 in the Z-axis direction also changes abruptly, and therefore the change in acceleration in the Z-axis direction alone cannot be used as a criterion for the fall of the user. In this embodiment, the three-axis angular velocity sensor 320 may be used to acquire the angular velocity changes of the user at different times in the X-axis, Y-axis and Z-axis directions, so as to accurately determine the posture of the user at different times, and the change of the acceleration in the Z-axis direction is combined to further improve the accuracy of determining whether the user falls down.

In the above embodiment, when a user falls, the acceleration of the wearable device 400 in the Z-axis direction may suddenly change, but the change of the acceleration only represents the motion condition of the wearable device 400 in the Z-axis direction, for example, when the user is riding in an elevator, the acceleration of the wearable device 400 in the Z-axis direction will suddenly change, but the user does not have a fall event, so that the fall event cannot be accurately determined only by the three-axis acceleration sensor 310, and the environmental sound data of the user in the fall process can be used as a further basis for determining the fall event.

In this embodiment, referring to fig. 3, the wearable device 400 may include a microphone 330, and the microphone 330 in the hearing aid is mainly used for receiving external sound, converting the sound into a digital signal, amplifying the corresponding digital signal by an amplifier, and converting the amplified digital signal into a sound wave to be transmitted to the user. Therefore, when the motion data is larger than the preset data, the step of acquiring the environmental sound data includes: when the reference acceleration is greater than a preset acceleration, sending a low-frequency enhancement instruction to the microphone 330; air flow data through the wearable device 400 is acquired using the microphone 330 to determine the ambient sound data.

In this embodiment, when the reference acceleration is smaller than a preset acceleration, for example, when the preset acceleration is 0, it is determined that the user does not fall; when the reference acceleration is smaller than the preset acceleration, the fact that the acceleration of the user in the Z-axis direction changes suddenly is judged, when the user falls down, the flow of air around the user is influenced, and the disturbance degree of the air is different according to different speeds of the user in the Z-axis direction; therefore, when the user falls, the air flow speed passing through the wearable device 400 is different, and the air flow rate and the sound of the air are in positive correlation, so that the air flow sound passing through the wearable device 400 can be acquired by the microphone 330, and whether the user falls is determined by combining the change rule of the acceleration of the wearable device 400 in the Z-axis direction.

In this embodiment, because the sound of the air flow belongs to the low-frequency sound data, the conventional microphone 330 cannot accurately identify the change of the air flow sound, and the method and the device can send a low-frequency enhancement instruction to the microphone 330 when the reference acceleration is smaller than the preset acceleration, increase the function of the microphone 330 for receiving the low-frequency sound data, and ensure the accuracy of acquiring the environmental sound data; in addition, since the air flow velocity and the sound of the air are in positive correlation, the larger the sound of the air is, the larger the air flow velocity is, the higher the speed of the user descending towards the corresponding direction is, and meanwhile, the sound is converted into a digital signal, and by analyzing the increasing gradient of the digital signal and combining the change of the acceleration in the Z-axis direction, the accuracy of judging whether the user falls or not can be further improved.

In this embodiment, when the reference acceleration is smaller than the preset acceleration, the function of the microphone 330 for acquiring low-frequency sound data is added, so that the microphone 330 can accurately acquire air flowing sounds passing through the wearable device 400 at different times, and the accuracy of determining whether the user falls down can be further improved by combining the change of the acceleration in the Z-axis direction.

When the ambient sound data is acquired, since the low frequency sound data is acquired, noise exists in the environment where the user is located to interfere with the microphone 330, which affects the accuracy of the microphone 330 in acquiring the ambient sound data, for example, when the user is outdoors, wind sound also passes through the microphone 330.

In this embodiment, the step of acquiring air flow data passing through the wearable device 400 by using the microphone 330 to determine the environmental sound data may include: acquiring the association relation 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 incidence relation between the wind speed and the air flow data; acquiring air flow data through the wearable device 400 using the microphone 330; determining the ambient sound data from the air flow data and the interference data.

In this embodiment, before obtaining the environmental sound data, the association relationship between different environmental wind speeds and the air flow data may be imported into the wearable device 400, and when the reference acceleration of the user is smaller than the preset acceleration, the interference data of the wearable device 400 at the current moment is determined according to the wind speed of the environment where the user is located, and the air flow data monitored by the microphone 330 is corrected to obtain the environmental sound data, and then the motion state of the user is determined according to the environmental sound data.

For example, when the user walks outdoors, the microphone 330 acquires interference data of the user when the user does not fall according to the ambient wind speed, which is a level 3 wind at the ambient wind speed of 5m/s, and converts the sound data into a first digital signal; when the user falls, acceleration in the Z-axis direction exists, at this time, a function of acquiring low-frequency sound data by the microphone 330 is added, meanwhile, air flow data generated by the fall and interference data generated by the environment are acquired, then, the low-frequency sound data acquired by the microphone 330 are 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 environment sound data generated by the fall of the user, and then, the motion state of the user is determined according to the environment sound data.

In this embodiment, since the ambient wind is a vector, for example, the user may receive ambient wind from different directions, and when the user falls, the user only disturbs air in the Z-axis direction, in order to ensure the measurement accuracy of the wearable device 400 on the air flow data generated by the user falling, the microphone 330 may only measure interference data generated by the wind speed in the Z-axis direction, and perform noise reduction on the ambient wind from other directions, for example, the microphone 330 may simultaneously measure 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 down, the interference data and the air flow data at different moments in the X-axis direction, the Y-axis direction and the Z-axis direction can be measured at the same time, only the interference data and the air flow data in the Z-axis direction are converted into corresponding digital signals, the digital signals are subjected to data processing to obtain the environmental sound data generated in the Z-axis direction due to the fact that the user falls down, and then the motion state of the user is judged according to the environmental sound data in the Z-axis direction.

In the above embodiment, the step of determining whether the user has fallen from the motion data and the ambient sound data comprises: acquiring the environmental sound data and the motion speed of the wearable device 400 at different moments; determining that the user has fallen when the speed of motion and the ambient sound data are positively correlated.

In this embodiment, during the falling process of the user, the falling speed of the user is gradually increased along with the increase of time, and the ambient sound data received by the microphone 330 is positively correlated with the speed of the user in the Z-axis direction, that is, the greater the falling speed is, the greater the air flow rate passing through the microphone 330 is, so that the exercise state of the user can be determined according to the exercise speed of the user and the variation trend of the ambient sound data.

For example, the user obtains the speed of the user at different times on the X-axis, the Y-axis and the Z-axis through the three-axis acceleration sensor 310, and obtains the environmental sound data at different times in the Z-axis direction through the microphone 330, and then fits the movement speed and the environmental sound data of the user at different times in the Z-axis direction, and if the movement speed and the environmental sound data are positively correlated, it can be determined that the user falls.

In the above embodiment, when the fall of the user is a soft impact, for example, 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 changes abruptly, and the angular velocities of the user at different times in the X-axis, Y-axis and Z-axis directions also change in the same way as the fall, so that the fall event cannot be accurately determined only by the three-axis acceleration sensor 310 and the three-axis angular velocity sensor 320.

In this embodiment, when the motion data is greater than the preset data, the step of acquiring the environmental sound data may further include: when the reference acceleration is greater than a preset acceleration, sending a low-frequency enhancement instruction to the microphone 330; the microphone 330 is utilized to acquire the user's impact data and air flow data through the wearable device 400 to determine the ambient sound data.

In the embodiment, when the user falls, besides the turbulent flow of the air generated by the acceleration in the Z-axis direction, the collision sound generated by the hard collision of the user also exists, so that the initial motion state of the user can be determined according to the air flow data, for example, whether the initial motion state has a motion trend pointing to the geocentric, and meanwhile, the acquisition of the collision data can judge whether the user has the hard collision, so that the situation that the user has the soft collision is eliminated, and the accuracy of the fall monitoring of the user is improved.

In this embodiment, when the user falls, there is also collision data with interference, for example, when the user has a soft collision, a slight collision sound will also occur, but the sound data of the soft collision and the hard collision have a certain difference, so the wearable device 400 of the present application can obtain the collision data of the human body and the objects such as the elastic object, the ground, the stool, the wall, and the like in advance, and match the collision data obtained by the microphone 330 with the collision data in the database, so as to determine whether the user has a hard collision, so as to improve the accuracy of monitoring the fall of the user.

In this embodiment, when a user has a hard collision, mainly a human bone collides with a hard object, so that there may be a certain deviation in the transmission of the collision sound to the microphone 330 through the air, and therefore, the wearable device 400 of the present application may include a vibrator for acquiring the collision data. When the user is in hard collision, the bone of the user and a hard object are in hard collision, and collision sound is directly transmitted to the vibrator in the wearable device 400 through the bone inside the human body so as to acquire collision data and improve the accuracy of acquiring the collision data.

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, the method further includes: a first time instant at which the microphone 330 acquires the impact data and a second time instant at which the wearable device 400 abruptly changes at a velocity directed toward the geocenter are acquired; and when the difference value between the first moment and the second moment is within a preset range, determining that the user falls.

In this embodiment, when a user has a hard collision, the speed of the user in the Z-axis direction suddenly changes to 0 due to the action of force, and the motion state of the wearable device 400 is the same as that of the user, so when a collision occurs, the speed of the user and the wearable device 400 in the Z-axis direction suddenly changes to 0 at the moment of the collision, and therefore, the present application can assist in determining whether the user falls down according to whether the difference between the first moment when the user collides and the second moment when the speed of the wearable device 400 in the Z-axis direction suddenly changes to 0 is within a preset range.

In the present embodiment, since the sound emitted by the impact received by the microphone 330 and the error of sudden change of the velocity of the wearable device 400 to 0 in the Z-axis direction are on the order of microseconds, it can be considered that both occur simultaneously, i.e., the difference between the first time and the second time may be 0.

According to the wearable device 400, the first moment when the microphone 330 acquires the collision data and the second moment when the speed of the wearable device 400 in the Z-axis direction suddenly reaches 0 are introduced, whether the user falls is further judged by taking the difference value between the first moment and the second moment as the basis within the preset range, and the accuracy of monitoring the falling event of the user by the wearable device 400 is improved.

In this embodiment, when the user has a hard collision, the speed of the user himself in the Z-axis direction suddenly changes to 0, but due to the inertia, the wearable device 400 also has a speed in the Z-axis direction, and the speed of the wearable device 400 in the Z-axis direction suddenly changes to 0 due to the force acting between the wearable device 400 and the user. Therefore, the third moment when the acting force exerted on the user by the wearable device 400 is applied can be introduced, and whether the user falls or not is judged according to the fact that whether the difference value between any two of the first moment, the second moment and the third moment is within the preset range or not, so that the accuracy of monitoring the falling event of the user by the wearable device 400 is further improved.

In this embodiment, when a user falls, in addition to an injury directly caused by the fall, there is also an injury indirectly caused by the fall, and for example, when physiological characteristics such as heart rate, blood oxygen, body temperature, etc. are abnormal, an irrecoverable accident may be caused.

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, the method may further include: when the user falls down, acquiring physiological characteristic data of the user; and when the physiological characteristic data is larger than the preset range, uploading the physiological characteristic data of the user to an alarm platform.

In this embodiment, the wearable device 400 may include a physiological characteristic module 340 for acquiring physiological characteristic data, which may include heart rate data, blood oxygen data, body temperature data, respiration data, blood pressure data, blood glucose data, and the like.

In this embodiment, when the user falls, the sudden change may occur to user's rhythm of the heart and breathing because of the unexpected fall, consequently when judging that the user falls, can strengthen wearable equipment 400 to the monitoring frequency of user's rhythm of the heart and breathing, when this physiological characteristic data surpassed corresponding preset range, uploaded user's physiological characteristic data to the warning platform.

In this embodiment, the wearable device 400 may monitor physiological characteristic data of the user in real time, and when the physiological characteristic data exceeds a corresponding preset range, may send the physiological characteristic data to an alarm platform, and notify a guardian or a doctor. For example, the normal range of the heart rate is 60 to 100 beats/minute, the heart rate of the elderly is slower than that of the younger, and for example, the abnormal range of the heart rate can be set to be lower than 50 beats/minute and higher than 110 beats/minute, and the abnormal condition can be reported.

In the existing wearable device 400, when the health condition of the user is monitored to be abnormal, health abnormalities of different degrees exist, and at the moment, the actual state of the user cannot be accurately judged only according to related data, so that the user needs to be rescued according to the self requirement of the user.

In the monitoring method of the present application, after the step of determining whether a user falls or not according to the motion data and the environmental sound data, the method further includes: when the user falls down, help seeking consultation is sent to the user; and when the help-seeking voice data of the user is received or the user does not reply within the preset time, uploading the position information and the falling data of the user to the alarm platform.

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

In this embodiment, please refer to fig. 3, the wearable device 400 of the present application includes a voice module 350 and a positioning module 360, wherein the positioning module 360 is configured to position a position of a user in real time, and the voice module 350 is configured to enable the user to communicate with a guardian or a doctor in real time.

Referring to fig. 4, fig. 4 is a flowchart of the intelligent self-rescue system of the wearable device of the present application. When the wearable device 400 monitors that the user falls down, help seeking consultation can be sent to the user in real time through the voice module, if the user needs to seek help, the alarm function of the wearable device 400 is triggered, help seeking information is directly sent to the cloud platform and sent to a guardian and a doctor through the platform, for example, the help seeking information is sent to the guardian and the doctor in the form of short messages or voice; or when the user replies that help is not required, the alarm function does not need to be triggered, and in addition, a guardian or a doctor can judge whether to rescue the user according to the physiological characteristic data of the user, for example, when the body temperature or the heart rate of the user exceeds a preset range, the doctor or the guardian can directly rescue the user; alternatively, when the user does not reply to the help inquiry within a preset time or does not reply to the help inquiry for a plurality of times, for example, the user does not reply within 30s, the wearable device 400 may upload the location information and the fall data of the user to the cloud platform to help the user.

The embodiment of the application provides a fall monitoring method and wearable equipment, wherein the fall monitoring method comprises the steps of firstly detecting wearing data, determining wearing state parameters according to the wearing data, secondly acquiring motion data of the wearable equipment 400 when the wearing state parameters represent that a user wears the wearable equipment 400, thirdly acquiring environmental sound data when the motion data are larger than preset data, and finally determining whether the user falls or not according to the motion data and the environmental sound data; this application is through obtaining wearable equipment 400 the motion data with the ambient sound data, it is the same whether to judge wearable equipment 400's motion data and ambient sound data's trend of change to confirm wearable equipment 400 at the motion state of pointing to the geocentric, and then confirm whether the user tumbles, improved the rate of accuracy of falling incident judgement to the user.

Referring to fig. 5, the present application also provides a wearable device 400, which includes a wearing unit 410, a motion data unit 420, an ambient sound unit 430, and a fall determination unit 440.

In this embodiment, the wearing unit 410 may be configured to detect wearing data and determine a wearing status parameter according to the wearing data; the motion data unit 420 may be configured to obtain motion data of the wearable device 400 when the wearing state parameter represents that the wearable device 400 is worn by the user; the ambient sound unit 430 may be configured to obtain ambient sound data when the motion data is greater than preset data; the fall determination unit 440 may be configured to determine whether a user has fallen based on the motion data and the ambient sound data.

In the wearable device 400 of the present application, the wearable device 400 is further configured to obtain and determine, according to the speeds of the wearable device 400 at different times, reference accelerations of the wearable device 400 pointing to the geocentric at different times; determining motion data of the wearable device 400 according to the reference accelerations of the wearable device 400 at different moments in time.

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; air flow data through the wearable device 400 is acquired with the microphone 330 to determine the ambient sound data.

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; acquiring the wind speed passing through the wearable device 400, and determining interference data based on the incidence relation between the wind speed and the air flow data; acquiring air flow data through the wearable device 400 using the microphone 330; determining the ambient sound data from the air flow data and the interference data.

In the wearable device 400 of the present application, the wearable device 400 is further configured to obtain the ambient sound data and the motion speed at different time instances; determining that the user has fallen when the speed of motion and the ambient sound data are positively correlated.

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; the microphone 330 is utilized to acquire the user's impact data and air flow data through the wearable device 400 to determine the ambient sound data.

In the wearable device 400 of the present application, the wearable device 400 is further configured to obtain a first time when the microphone 330 obtains the collision data and a second time when the wearable device 400 abruptly changes speed towards the geocentric; and when the difference value of the first moment and the second moment is within a preset range, determining that the user falls.

In the wearable device 400 of the present application, the wearable device 400 is further configured to obtain physiological characteristic data of the user when the user is in a fall state; and when the physiological characteristic data is larger than the preset range, uploading the physiological characteristic data of the user to an alarm platform.

In the wearable device 400 of the present application, the wearable device 400 is further configured to send help consultation to the user when the user is in a fall state; when the help-seeking voice data of the user are received or the user does not reply within the preset time, the position information and the falling data of the user are uploaded to the alarm platform.

In specific implementation, the above units may be implemented as independent entities, or may be implemented as one or several entities by any combination. For the above specific implementation processes of the apparatus and each unit, and the achieved beneficial effects, reference may be made to the corresponding description in the foregoing method embodiment applied to the node of the block chain, and for convenience and simplicity of description, details are not repeated here.

Fig. 6 is a schematic structural diagram of a hearing aid provided in an embodiment of the present application. As shown in fig. 6, the hearing aid 50 of this embodiment includes: one or more processors 500 (only one of which is shown), a memory 501, and a computer program 502 stored in the memory 501 and executable on the at least one processor 500. The processor 500 implements the steps of the above-mentioned audio playing parameter updating method embodiments when executing the computer program 502.

The hearing aid may include, but is not limited to, a processor 500, a memory 501. It will be appreciated by those skilled in the art that fig. 6 is merely an example of a hearing aid 50 and does not constitute a limitation of the hearing aid 50 and may include more or less components than shown, or combine certain components, or different components, e.g. the hearing aid may also include input and output devices, network access devices, buses, etc.

The Processor 500 may be a Central Processing Unit (CPU), and may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 501 may be an internal storage unit of the hearing aid 50, such as a hard disk or a 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 memory Card (Flash Card), or the like, provided on the hearing aid 50. Further, the memory 501 may also comprise both an internal memory unit and an external memory device of the hearing aid 50. The memory 501 is used for storing 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.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the above-mentioned apparatus may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

The embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps that can be implemented in the foregoing method embodiments.

The embodiment of the present application further provides a computer program product, when the computer program product runs on a hearing aid combination, the steps in the embodiment of the fall detection method can be implemented when the hearing aid combination is executed; or cause an audio source device to perform the steps as in the above-described fall detection method embodiments, when said computer program product is run on the audio source device.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in the present application, it should be understood that the disclosed device/hearing aid combination/audio source apparatus and method may be implemented in other ways. For example, the above described device/hearing aid combination/audio source device embodiments are merely illustrative, e.g. the division of the modules or units is only a logical division, and in practice there may be other divisions, e.g. multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer-readable medium may contain suitable additions or subtractions depending on the requirements of legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer-readable media may not include electrical carrier signals or telecommunication signals in accordance with legislation and patent practice.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.

Claims

1. A fall monitoring method for use with a wearable device, the fall monitoring method comprising:

when the motion data are larger than preset data, acquiring environmental sound data;

2. A fall monitoring method as claimed in claim 1, wherein the step of obtaining motion data of the wearable device comprises:

3. A fall monitoring method according to claim 2, wherein the wearable device comprises a microphone, and the step of obtaining ambient sound data when the motion data is greater than preset data comprises:

4. A fall monitoring method as claimed in claim 3, wherein the step of acquiring air flow data through the wearable device using the microphone to determine ambient sound data comprises:

acquiring air flow data through the wearable device using the microphone;

5. A fall monitoring method as claimed in claim 4, wherein the step of determining whether a user has fallen from the movement data and the ambient sound data comprises:

6. A fall monitoring method according to claim 3, wherein the wearable device comprises a microphone, and the step of acquiring ambient sound data when the motion data is greater than preset data further comprises:

7. A fall monitoring method as claimed in claim 6, wherein after the step of determining whether a user has fallen from the movement data and the ambient sound data, further comprising:

acquiring a first moment when the microphone acquires the collision data and a second moment when the wearable device suddenly changes speed pointing to the geocenter;

8. A fall monitoring method as claimed in claim 1, wherein after the step of determining whether a user has fallen from the motion data and the ambient sound data, the method further comprises:

9. A fall monitoring method as claimed in claim 1, wherein after the step of determining whether a user has fallen from the motion data and the ambient sound data, further comprising:

when the user falls down, help seeking consultation is sent to the user;

10. A wearable device, comprising: