CN110495874B

CN110495874B - Information processing method and electronic equipment

Info

Publication number: CN110495874B
Application number: CN201910931795.0A
Authority: CN
Inventors: 胡永登; 吕晓; 刘微微
Original assignee: Lenovo Beijing Ltd
Current assignee: Lenovo Beijing Ltd
Priority date: 2019-09-29
Filing date: 2019-09-29
Publication date: 2024-06-18
Anticipated expiration: 2039-09-29
Also published as: CN110495874A

Abstract

The application relates to an information processing method and electronic equipment, wherein the method collects human body state data at least comprising first motion data of non-forced feet of a human body through at least one wearable device bound to the human body, further identifies the human body state based on the human body state data, and automatically triggers a notification event under the condition of abnormal human body state. Based on the scheme of the application, the notification event is obviously not required to be triggered manually by a person with abnormal state, such as a falling person, and the human body state information is acquired through the wearable device bound to the human body, so that the requirement on the aspect of a detection area is avoided, the specific detection area is not required, the falling situation of special personnel such as the old and the patient can be effectively detected in any place/any area, and the alarm notification event is automatically triggered. In addition, the human body state detection is performed based on the motion data of the non-forced human body feet, so that the detection result has higher accuracy.

Description

Information processing method and electronic equipment

Technical Field

The application belongs to the technical field of intelligent monitoring, and particularly relates to an information processing method and electronic equipment.

Background

In a daily monitoring scenario for special people such as the old and the patient, the method is a very realistic problem of knowing the falling situation of the people such as the old or the patient and rapidly notifying emergency contacts to take relevant safety/rescue measures.

At present, after people such as old people or patients fall down, a common solution is that a user triggers a notification event through a certain device such as a mobile phone or a bracelet, and the like, wherein the method requires that the user is awake and uses electronic devices such as the mobile phone or the bracelet to execute corresponding operations; another common solution is to judge the falling situation of the personnel and trigger an alarm notification through image recognition, ultrasonic or infrared technology, etc., and the method can only effectively detect the falling state of the personnel in a specific area of a detection device such as a camera, an ultrasonic sensor, an infrared sensor, etc. installed in a specific place (such as a hospital and a nursing home).

Therefore, the realization scheme which can effectively detect the falling situation of special personnel such as old people, patients and the like in any place/any region and automatically trigger the alarm notification event is provided, and is necessary in the field.

Disclosure of Invention

In view of the above, the present application aims to provide an information processing method and an electronic device, which are used for overcoming many limitations existing in the prior art when detecting and notifying a fall of a person, realizing that the fall condition of a special person such as an old person, a patient, etc. can be effectively detected in any place/any area, and automatically triggering an alarm notification event.

Therefore, the application discloses the following technical scheme:

An information processing method, the method comprising:

Acquiring human body state data transmitted by at least one wearable device bound to a human body; the human body state data at least comprises first motion data of a non-forced foot of a human body, wherein the non-forced foot is the other foot except the forced foot which is mainly used for supporting the weight of the human body under the condition of falling;

identifying a human body state based on the human body state data;

If the identified human body state represents the abnormality of the human body state, triggering a notification event for representing the abnormality of the human body state.

Preferably, the method for acquiring the human body state data transmitted by the at least one wearable device bound to the human body includes:

acquiring electrocardiograph data and motion data of a human body transmitted by at least one wearable device bound to the human body; the motion data at least comprises first motion data of non-force-generating feet transmitted by wearable equipment bound to the ankle part of a human body.

In the above method, preferably, the motion data further includes second motion data transmitted by a wearable device bound to a wrist of the human body.

Preferably, the method for identifying a human body state based on the human body state data includes:

determining a heart rate of the human body based on the electrocardiographic data;

determining the motion acceleration and the inclination angle of the non-forced human foot based on the first motion data;

determining whether the heart rate, the exercise acceleration, and the inclination angle satisfy a predetermined fall condition;

if the condition is satisfied, determining that the human body is in a falling state.

acquiring human body electrocardio data, non-forced foot and ankle movement data and wrist movement data record under the tumbling state;

Determining whether the acquired electrocardio data, the first motion data and the second motion data are respectively matched with the data records of the human electrocardio data, the non-forced ankle motion data and the wrist motion data;

If the two pieces of information are matched, the human body is determined to be in a falling state.

In the above method, preferably, if the identified human body state indicates that the human body state is abnormal, triggering a notification event for indicating that the human body state is abnormal includes:

And triggering a notification event for representing the human body falling based on the prestored contact information if the identified human body state represents that the human body is in the falling state.

An electronic device, comprising:

a memory for storing at least one set of instructions;

a processor for calling and executing the instruction set in the first memory, by executing the instruction set:

identifying a human body state based on the human body state data;

Preferably, the processor acquires human body state data transmitted by at least one wearable device bound to a human body, and the method specifically includes:

In the above electronic device, preferably, the processor identifies a human body state based on the human body state data, and specifically includes:

Preferably, the motion data further includes second motion data transmitted by a wearable device bound to a wrist of the human body;

the processor identifies the human body state based on the human body state data, and specifically comprises the following steps:

According to the information processing method and the electronic device, the human body state data at least comprising the first motion data of the non-forced feet of the human body is collected through the at least one wearable device bound to the human body, so that the human body state is identified based on the human body state data, and the notification event is automatically triggered under the condition that the human body state is abnormal. Based on the scheme of the application, the notification event is obviously not required to be triggered manually by a person with abnormal state, such as a falling person, and the human body state information is acquired through the wearable device bound to the human body, so that the requirement on the aspect of a detection area is avoided, the specific detection area is not required, the falling situation of special personnel such as the old and the patient can be effectively detected in any place/any area, and the alarm notification event is automatically triggered. In addition, the human body state detection is performed based on the motion data of the non-forced human body feet, so that the detection result has higher accuracy.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic diagram of a local network formed by an electronic device and a wearable device according to an alternative embodiment of the present application;

FIG. 2 is a flow chart of an information processing method according to an alternative embodiment of the present application;

FIG. 3 is a schematic illustration of an unintentional extension of a non-force-generating foot in the event of a human fall provided by an alternative embodiment of the present application;

FIG. 4 is a graph showing the acceleration of a non-force-generating foot in different human body states according to an alternative embodiment of the present application;

FIG. 5 is a schematic flow chart of an information processing method according to an alternative embodiment of the present application;

FIG. 6 is a schematic diagram of a wearable device respectively binding a wrist and a ankle of a non-stressful foot to collect human body status data according to an alternative embodiment of the present application;

FIG. 7 is a schematic diagram of human body state recognition and automatic alarm notification based on electrocardiographic data and non-power foot motion data according to an alternative embodiment of the present application;

FIG. 8 is a schematic view of three-lead electrocardiographic data provided in an alternative embodiment of the present application;

FIG. 9 is a schematic diagram of human body state recognition and automatic alarm notification based on electrocardiographic data, non-exertion foot motion data and wrist motion data according to an alternative embodiment of the present application;

FIG. 10 is a schematic flow chart of an information processing method according to an alternative embodiment of the present application;

fig. 11 is a schematic structural diagram of an electronic device according to an alternative embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

The application provides an information processing method and electronic equipment, which are mainly used for detecting the human body state at least based on the motion data of the non-forced feet of a human body acquired by wearable equipment, automatically triggering a notification event under the condition that the abnormal human body state is detected, improving the accuracy of the human body state detection by detecting the human body state based on the motion data of the non-forced feet of the human body, overcoming a plurality of limitations in the prior art when detecting the falling of people and notifying the alarm, realizing that the falling of special people such as old people, patients and the like can be effectively detected in any place/any area, and automatically triggering the alarm notification event.

In an alternative embodiment of the present application, an information processing method is provided first, where the information processing method may be applied to an electronic device, and the electronic device may be any one of, but not limited to, a smart phone, a tablet computer, a personal digital assistant, a wearable device, and a PC, or may also be a processing device/processing device that is disposed at a fixed location and capable of communicating with a wearable device (for collecting human body status data) bound to a human body, where the processing device/processing device has at least an arithmetic processing function. In the case that the information processing method is applied to a wearable device, the wearable device and the wearable device bound to a human body for acquiring human body state data may be the same device or different devices.

As shown in fig. 1, in the case that the electronic device as the execution subject of the method of the present application is a different device from the wearable device bound to the human body, the electronic device and at least one wearable device bound to the human body form a local network, in the local network, the at least one wearable device bound to the human body is used as an edge acquisition device to acquire human body state data, and the electronic device is used as a central processing device to identify the human body state by processing the human body state data acquired by the at least one wearable device.

Referring to fig. 2, a flow chart of an information processing method of the present application is shown, and in this embodiment, the information processing method includes the following processing steps:

Step 201, acquiring human body state data transmitted by at least one wearable device bound to a human body; the human body state data at least comprises first motion data of a non-force-exerting foot of a human body, wherein the non-force-exerting foot is the other foot which is mainly used for supporting the weight of the human body under the condition of falling.

The application relates to human body state detection, which mainly refers to detection of a falling state of a human body.

When a human body falls forward, as shown in fig. 3, one foot is instinctively and rapidly extended to prevent falling, while when the human body falls backward, one foot is instinctively kept to be supported and the other foot is unconsciously lifted, the extended foot for preventing falling or the unconsciously lifted foot does not support the weight of the human body (or at least does not support the weight of the human body) when the human body falls, so that the embodiment of the application refers to the foot as a non-stressed foot, while the other foot outside the non-stressed foot plays a main support role for the weight of the human body when the human body falls.

The inventor finds that the non-force foot has distinct and special movement characteristics when the human body falls, for example, the non-force foot can suddenly generate a larger acceleration when the human body falls, the acceleration characteristics of the non-force foot of the human body can be particularly referred to as fig. 4 when the human body falls, fig. 4 shows the comparison curve of the acceleration of the non-force foot under different states (falling, walking, going down stairs, sitting-standing, quick sitting, lying down, jogging, squatting-standing and mobile phone daily use), and the non-force foot can generate a larger acceleration value corresponding to the point P in fig. 4 when the human body falls; in addition, at the moment of falling, the inclination angle of the ankle or the leg corresponding to the non-stressing foot compared with the vertical/horizontal direction also has a larger change, for example, in the non-falling state, the included angle between the ankle or the leg corresponding to the non-stressing foot and the vertical/horizontal direction is relatively stable and small in change when standing, walking or sitting and lying, and the inclination angle of the non-stressing foot is changed greatly due to sudden stretching or unconscious lifting when falling.

Based on the characteristics of the non-force-exerting feet, the special motion data of the non-force-exerting feet are introduced into the detection of the falling state of the human body. In a specific implementation, the wearable device (such as a foot ring) may be bound at least to the ankle (or the lower leg of a person) of the non-stressing foot, so as to collect at least the special motion data of the non-stressing foot of the person, where the collected special motion data includes, but is not limited to, the first motion data such as acceleration and inclination data of the non-stressing foot, and then the collected first motion data is used in detection and identification of the human body state.

In the case that the execution subject of the method is a wearable device bound to a non-force foot of a human body, the first motion data collected is directly obtained by the wearable device inside the self device to perform state recognition processing, and in the case that the execution subject of the method is a wearable device different from the non-force foot, the first motion data collected by the wearable device is obtained based on a communication connection, wherein the communication connection can be, but is not limited to, a wireless communication connection based on Zigbee, wi-Fi (action hotspot), 3G/4G/5G (third/fourth/fifth generation mobile communication technology), or a wired communication connection based on a data line.

In addition, optionally, the wearable device may be bound to other parts of the human body to collect conventional motion data of the human body, which is different from the non-stressed foot, which will be described in detail below as an embodiment.

Step 202, based on the human body state data, identifying the human body state.

After acquiring human body state data which at least comprises first motion data of human body non-force-exerting feet and is transmitted by at least one wearable device, human body state identification is performed based on the human body state data.

For example, optionally, whether the human body accords with the motion characteristics in the falling state is identified based on the first motion data of the non-force-generating foot of the human body, or whether the human body accords with the motion characteristics in the falling state or the like is identified based on the first motion data (special motion data) of the non-force-generating foot of the human body and the conventional motion data of other parts, and whether the human body falls is further identified.

Step 203, if the identified human body state indicates a human body state abnormality, triggering a notification event for indicating the human body state abnormality.

Here, abnormal human body state mainly means that the human body is in a falling state.

If the human body is identified to be in a falling state, an execution main body of the method, such as a smart phone, a tablet personal computer or a fixedly arranged computing processing device which establishes communication connection with a wearable device bound to the human body, automatically triggers an alarm notification event to notify related emergency contacts, a monitoring center and/or a hospital to assist the falling person.

In the case of abnormal human body state, the embodiment automatically triggers the notification event, does not need to manually trigger the notification event by a state abnormality person such as a falling person, and does not need to target at a specific detection area because human body state information acquisition is performed through the wearable device bound to the human body, so that the falling condition of special personnel such as old people, patients and the like can be effectively detected in any place/any area without the requirement of the detection area, and the alarm notification event is automatically triggered. In addition, the non-forced feet have vivid and special movement characteristics when the human body falls down, and the detection result can have higher accuracy by detecting the human body state based on the movement data of the human body non-forced feet.

The implementation process of the information processing method of the present application is further described in detail below, and referring to the flow chart of the information processing method shown in fig. 5, the method may be implemented by the following processing process:

Step 501, acquiring electrocardiographic data and motion data of a human body, which are transmitted by at least one wearable device bound to the human body; the motion data at least comprises first motion data of non-force-generating feet transmitted by wearable equipment bound to the ankle part of a human body.

Besides the characteristic that the non-force-generating foot has a clear characteristic when the human body falls down, the human body electrocardio data can generate an obvious change at the moment of falling down of the human body, and the human body electrocardio curve is suddenly dense, and correspondingly, the human heart rate can be suddenly increased, so that the embodiment also introduces the human body electrocardio data into the identification of the human body state, and combines the human body electrocardio data and the human body motion data at least comprising the non-force-generating foot special motion data (namely the first motion data) to identify the human body state.

In practical implementation, the wearable device for acquiring the electrocardiograph data of the human body and the movement data of the human body can be acquired by binding the wearable device at the corresponding part of the human body, wherein the wearable device for acquiring the electrocardiograph data of the human body and the wearable device for acquiring the movement data of the human body can be the same device or different devices; the number of the wearable devices for collecting the human body electrocardio data and the human body motion data can be one or more, the wearable devices for collecting the human body electrocardio data and the human body motion data can be bound to the same part or different parts of the human body, and the embodiment is not limited to a specific implementation form of binding the wearable devices at corresponding parts of the human body to collect corresponding data.

The following example provides a preferred implementation:

As shown in fig. 6, corresponding wearable devices are respectively bound at the ankle part and the two wrist parts of the non-stressing foot of the human body, wherein the wearable devices at the ankle part of the non-stressing foot at least comprise motion data sensing devices such as an inclination sensor (or an angular motion detection device) and an acceleration sensor, and the wearable devices at the ankle part of the non-stressing foot at least can be correspondingly used for acquiring first motion data of the non-stressing foot of the human body as shown in a structural schematic diagram of the wearable devices at different parts in fig. 7; the wearable equipment positioned at the two wrist parts at least comprises an electrocardio data sensing device such as an electrocardio sensor and the like, and can be correspondingly used for acquiring human electrocardio data.

Preferably, in order to obtain the electrocardiographic data of the human body more comprehensively, referring to fig. 7, a function of acquiring electrocardiographic data of the human body may be further provided in the wearable device bound to the non-force-generating foot of the human body, that is, an electrocardiograph sensor is simultaneously provided in the wearable device for detecting the motion data of the non-force-generating foot, and besides, each wearable device may further include a battery, a microprocessor, a communication module and other components. The system comprises an inclination angle sensor (or an angular motion detection device), an acceleration sensor and an electrocardio sensor, wherein the inclination angle sensor (or the angular motion detection device), the acceleration sensor and the electrocardio sensor are respectively used for acquiring inclination angle data, acceleration data and human electrocardio data of a non-forced human foot, and the microprocessor is used for acquiring the acquired data and transmitting the acquired data to electronic equipment for providing a central processing function through a communication module. As shown in fig. 7, the data may be transmitted to an APP (application program) of an electronic device such as a mobile phone, for example, to provide a central processing function to identify a human body state. The APP receives the electrocardio data, acceleration and inclination angle motion data transmitted by each wearable device through a communication module of the electronic device (for example, a Zigbee communication module), and obtains the acceleration value, the inclination angle value and the heart rate value of a human body by calculating corresponding data based on an acceleration calculation module, an inclination angle calculation module and a three-lead electrocardio module of the APP.

In the wearable device, the acceleration sensor may be a three-axis acceleration sensor, the angular motion detection device may be a three-axis gyroscope, the microprocessor may be a CC2430 microprocessor, the communication module may be a Zigbee module, and in the example of fig. 7, based on the electrocardiograph data acquisition function of three wearable devices, the electronic device may finally obtain three-lead electrocardiograph data as shown in fig. 8.

Step 502, determining the heart rate of the human body based on the electrocardiographic data.

The heart rate of the human body may be determined in particular based on the three-lead electrocardiographic data.

Step 503, determining the motion acceleration and the inclination angle of the non-force-generating foot of the human body based on the first motion data.

The motion acceleration of the non-forced foot of the human body can be obtained by calculating the triaxial acceleration component data acquired by the triaxial acceleration sensor; the inclination angle of the human non-stressing foot, which generally refers to the inclination angle of the ankle of the human non-stressing foot compared with the vertical/horizontal direction, can be correspondingly determined based on the angle data acquired by the inclination angle sensor or the angle motion data acquired by the triaxial gyroscope.

Step 504, determining whether the heart rate, the exercise acceleration, and the inclination angle satisfy a predetermined fall condition.

The tumbling conditions comprise predetermined conditions which can be used for reflecting heart rate characteristics and non-exertion foot movement characteristics of a human body in a tumbling state, and correspondingly, the tumbling conditions can be subdivided into heart rate conditions and movement characteristic conditions.

As one example, the heart rate condition may be, but is not limited to:

The rate of change of heart rate exceeds a set first threshold.

Wherein the rate of change ΔR _HR isHR _i represents the heart rate value of the current sampling point at the time of heart rate sampling, and HR _(i-1) represents the heart rate value of the last sampling point at the time of heart rate sampling.

The motion profile conditions may include, but are not limited to:

1) The acceleration change proportion of the human non-forced foot exceeds a set second threshold value;

2) The change proportion of the inclination angle of the ankle of the non-forced foot of the human body compared with the vertical/horizontal direction exceeds a set third threshold value.

Wherein the acceleration change ratio DeltaR _a of the non-force-generating foot isA _i represents the acceleration value of the current sampling point at the time of acceleration sampling, and a _(i-1) represents the acceleration value of the last sampling point at the time of acceleration sampling. The change ratio delta R _ω of the inclination angle of the ankle of the non-forced foot of the human body relative to the vertical/horizontal direction is/>Omega _i represents the inclination value of the current sampling point in inclination sampling, and omega _(i-1) represents the inclination value of the last sampling point in inclination sampling.

After determining the actual heart rate of the human body and the actual acceleration and inclination angle of the non-stressed foot based on the collected electrocardio data and motion data, comparing the actual heart rate of the human body and the actual acceleration and inclination angle of the non-stressed foot with the heart rate condition and the non-stressed foot motion characteristic condition respectively, determining whether the actual heart rate of the human body meets the heart rate condition, and determining whether the actual acceleration and inclination angle of the non-stressed foot meets the condition 1) and the condition 2) in the non-stressed foot motion characteristic condition respectively so as to judge whether the actual heart rate characteristic and the actual non-stressed foot motion characteristic of the human body are consistent with the heart rate characteristic and the non-stressed foot motion characteristic represented by the falling condition.

Step 505, if yes, determining that the human body is in a falling state.

If the actual heart rate of the human body meets the heart rate condition, the actual acceleration and the inclination angle of the non-stressed foot respectively meet the condition 1) and the condition 2) in the non-stressed foot motion characteristic condition, the human body presents the heart rate characteristic and the non-stressed foot motion characteristic in the falling state, and accordingly the human body state is identified as the falling state.

Otherwise, if the actual heart rate of the human body does not meet the heart rate condition, and/or the actual acceleration and inclination angle of the non-stressed foot do not meet the condition 1) and the condition 2) in the non-stressed foot exercise characteristic condition respectively, the human body is indicated to not show the heart rate characteristic and the non-stressed foot exercise characteristic in the falling state, and the human body state is correspondingly identified as the non-falling state.

Step 506, triggering a notification event for representing a human body falling based on the pre-stored contact information if the identified human body state represents that the human body is in the falling state.

The electronic device for providing the central processing function is preset with contact information, which is typically information that can be effectively notified to a corresponding emergency contact (e.g. family), a monitoring center and/or a hospital, such as one or more mobile phone numbers, telephone numbers, or a monitoring platform IP address of a certain monitoring center, which can be the emergency contact, the monitoring center and/or the hospital.

Once the abnormal state of the human body is identified, if the human body is identified to be in a falling state, the electronic device triggers an alarm notification event for the abnormal state, such as a telephone notification (automatic playing of a notification record after being connected), a short message notification, feedback of notification information representing the abnormal state of the human body to a monitoring platform and the like, based on a corresponding communication technology (such as a mobile communication technology) according to the pre-stored contact information, so as to notify related emergency contacts, a monitoring center or a hospital to assist the falling person.

According to the embodiment, human body state detection is performed by combining the human body electrocardio data and the non-power foot motion data, and as the human body has clear heart rate characteristics and the non-power foot motion characteristics when falling down, the human body falling state can be more accurately identified by combining the two data, meanwhile, the requirement on the aspect of detection areas is avoided, the falling situation of special personnel such as old people, patients and the like can be effectively detected in any place/any area without aiming at the specific detection areas, and an alarm notification event is automatically triggered.

In an optional embodiment of the present application, when the human body state is identified, the motion data adopted may include, in addition to the special motion data (i.e. the first motion data) of the non-stressed foot of the human body, second motion data of other parts of the human body except the non-stressed foot, where optionally, the second motion data may include, but is not limited to, second motion data such as acceleration, inclination angle, etc. of the wrist of the human body transmitted by the wearable device bound to the wrist part of the human body.

In this case, as shown in fig. 9, different wearable devices bound to different parts of the human body can provide an electrocardiograph data acquisition function and a motion data acquisition function, and accordingly, the wearable devices can include an acceleration sensor, an inclination sensor (angular motion detection device) and an electrocardiograph sensor for acquiring electrocardiograph data and motion data of the human body, and besides, the wearable devices can further include a microprocessor, a communication module and a battery.

In correspondence to the above, referring to the flow chart of the information processing method shown in fig. 10, the information processing method may also be implemented by the following processing procedure:

Step 1001, acquiring electrocardiographic data and motion data of a human body, which are transmitted by at least one wearable device bound to the human body. The motion data comprise first motion data of non-force-generating feet transmitted by the wearable device bound to the ankle part of the human body and second motion data of the wrist of the human body transmitted by the wearable device bound to the wrist part of the human body.

The first motion data and the second motion data are respectively refined into acceleration data (such as triaxial acceleration components) and inclination angle data (or angular motion data) of the corresponding part of the human body.

Step 1002, acquiring data records of human body electrocardio data, non-forced foot and ankle movement data and wrist movement data in a tumbling state.

Unlike the previous embodiment, the human body state is identified by performing correlation calculation (calculating an acceleration value, calculating a heart rate, etc.) on the collected original acceleration data and the electrocardiographic data, and performing condition judgment on the calculation result, the present embodiment directly identifies whether the human body state is abnormal by matching the collected current human body state data with the history data record when the human body state is abnormal.

In this implementation manner, for the abnormal state of the falling of the human body, it is necessary to acquire the electrocardiographic data, the non-exertion ankle movement data and the history data record of the wrist movement data in the falling state of the human body as the matching basis.

It should be noted that, the historical data record of the falling state of the human body of a certain or a few individuals has sporadic property, and cannot reflect the general data rule or characteristic of the falling state of the human body, therefore, the obtained historical data record as the matching basis is preferably a large amount of historical data, and in general, the larger the data amount of the historical data record as the matching basis is, the larger the corresponding number of the source human bodies is, the larger the reference value of the historical data record as the data basis is, and the more accurate human body state recognition results can be obtained correspondingly.

Step 1003, determining whether the acquired electrocardiographic data, the first motion data and the second motion data are respectively matched with the data records of the electrocardiographic data, the non-forced ankle motion data and the wrist motion data of the human body.

Step 1004, if the human body is matched, determining that the human body is in a falling state.

The acquired current electrocardiographic data, first motion data and second motion data of the human body are respectively matched with the batch electrocardiographic data, the non-forced ankle motion data and the historical data record of the wrist motion data of the human body in a tumbling state, and the method specifically can be as follows: and matching the current data characteristics of the electrocardiographic data, the first motion data and the second motion data of the human body with the electrocardiographic data characteristics and the motion data characteristics extracted based on the batch electrocardiographic data, the non-forced ankle motion data and the historical data record (the data record in the falling state) of the wrist motion data. And determining whether the current human body state data are matched with the historical data records in the falling state of the human body according to the matching degree or the confidence degree, if so, judging that the human body is in the falling state, otherwise, judging that the human body is in the non-falling state.

In a specific implementation, preferably, a batch of historical data records (such as electrocardiographic data, non-stressed ankle motion data and wrist motion data) of the human body in a tumbling state and a batch of historical data records (such as electrocardiographic data, non-stressed ankle motion data and wrist motion data) of the human body in a non-tumbling state are obtained in advance as sample data to train an AI (ARTIFICIAL INTELLIGENCE ) processing model. The algorithm adopted in model training can be, but is not limited to, a K-means clustering algorithm, three types of data of the same human body at the same time are used as one sample in a batch of historical data records, and the model continuously learns the human body characteristics reflected by each sample data in the falling state and the non-falling state of the human body based on the training process.

After model training is completed, the obtained current electrocardiographic data, the first motion data and the second motion data of the human body can be input into the AI processing model, human body characteristics reflected by the input data are matched with human body characteristics which are learned based on big data in the model, a model processing result is output, and the output result of the model generally comprises confidence coefficient data of the human body state belonging to a tumbling state.

If the confidence value reaches a preset confidence threshold value, the human body state is judged to be a falling state, otherwise, if the confidence value does not reach the confidence threshold value, the human body state is correspondingly judged to be a non-falling state.

Step 1005, triggering a notification event for representing a human body falling based on the pre-stored contact information if the identified human body state represents that the human body is in the falling state.

In addition, when the motion of the wrist in the moment of a human body falling is compared with the motion of the wrist in a normal state, there is no particularly clear motion characteristic, for example, the wrist part may generally present diversified motion characteristics based on the performed work, labor and activity in the normal state, and the motion characteristic of the wrist in the state of a human body falling is not easily and particularly clearly distinguished from the motion characteristics in the normal state, therefore, when the condition judgment mode based on the previous embodiment is used for recognizing the state of the human body, it is difficult to set a proper judgment condition for the motion data of the wrist part, and in view of this characteristic, in the condition judgment mode based on the condition judgment mode, the embodiment of the application purposefully adopts the data of the human body state represented by the characteristics of the heart data and the special motion data of the non-powerful foot of the human body, and takes part in the recognition of the human body state, and can more accurately and effectively recognize the human body in the condition judgment mode based on the data.

The motion of the wrist at the moment of falling of the human body is more sudden in time performance than the motion of the wrist in a normal state, for example, the motion of the wrist at the moment of falling is more sudden in time performance, and generally has larger motion parameter change (the characteristic is not particularly obvious compared with the motion of the wrist in the normal state), although the characteristic is not particularly obvious compared with the motion of the wrist in the normal state, the motion of the wrist can still be captured and learned by a model from a large number of training samples in model training based on big data, which also can provide assistance for human body state recognition, for example, in a matching recognition mode based on a large number of historical data, such as a recognition mode based on an AI processing model, the human body state recognition is performed by combining the special motion data of human body electrocardio data, the motion data of a human body non-force foot and the motion data of the human body, and by combining the characteristics shown by different types of human body state data, the human body state can be recognized with high accuracy in state recognition based on an AI model.

The present application also provides an electronic device, which may be, but not limited to, any one of a smart phone, a tablet computer, a personal digital assistant, a wearable device, and a PC, or may be a processing device/processing device that is disposed at a fixed location and capable of communicating with a wearable device (for collecting human body status data) bound to a human body, where the processing device/processing device has at least an arithmetic processing function. Under the condition that the electronic equipment is a wearable equipment, the wearable equipment and the wearable equipment bound to a human body for acquiring human body state data can be the same equipment or different equipment.

As shown in fig. 1, in the case that the electronic device and the wearable device bound to the human body are different devices, the electronic device and at least one wearable device bound to the human body form a local network, in the local network, the at least one wearable device bound to the human body is used as an edge collecting device to collect human body state data, and the electronic device is used as a central processing device to identify the human body state by processing the human body state data collected by the at least one wearable device.

Referring to the schematic structural diagram of the electronic device shown in fig. 11, the electronic device may include:

a memory 1101 for storing at least a set of instructions;

a processor 1102 for calling and executing the instruction set in the first memory, by executing the instruction set:

identifying a human body state based on the human body state data;

In addition to the memory and the processor, the electronic device may be configured according to functions, including, but not limited to, a communication module (e.g., zigbee module, wi-Fi module) for communicating with a wearable device, an acceleration calculation module for performing acceleration calculation, a tilt calculation module for tilt calculation, a three-lead electrocardiograph module for acquiring three-lead electrocardiograph data, and a communication module (e.g., 3G/4G/5G module, GPRS wireless communication module, etc.) for communicating with an emergency contact, a hospital, and/or a monitoring center, etc.

Based on the characteristic that the non-force-exerting feet have distinct movement characteristics when the human body falls, the application introduces the special movement data of the non-force-exerting feet into the detection of the falling state of the human body. In a specific implementation, the wearable device (such as a foot ring) may be bound at least to the ankle (or the lower leg of a person) of the non-stressing foot, so as to collect at least the special motion data of the non-stressing foot of the person, where the collected special motion data includes, but is not limited to, the first motion data such as acceleration and inclination data of the non-stressing foot, and then the collected first motion data is used in detection and identification of the human body state.

If the human body is identified to be in a falling state, the electronic equipment automatically triggers an alarm notification event to notify related emergency contacts, a monitoring center and/or a hospital to assist the falling person.

In an alternative embodiment of the present application, the functions of the processor 1102 in the electronic device may be implemented specifically by the following processing procedures:

Acquiring electrocardiograph data and motion data of a human body transmitted by at least one wearable device bound to the human body; the motion data at least comprises first motion data of non-force-generating feet transmitted by wearable equipment bound to the ankle part of a human body; determining a heart rate of the human body based on the electrocardiographic data; determining the motion acceleration and the inclination angle of the non-forced human foot based on the first motion data; determining whether the heart rate, the exercise acceleration, and the inclination angle satisfy a predetermined fall condition; if the condition is met, determining that the human body is in a falling state; and triggering a notification event for representing the human body falling based on the prestored contact information if the identified human body state represents that the human body is in the falling state.

The following example provides a preferred implementation:

Preferably, in order to obtain the electrocardiographic data of the human body more comprehensively, referring to fig. 7, a function of acquiring electrocardiographic data of the human body may be further provided in the wearable device bound to the non-force-generating foot of the human body, that is, an electrocardiograph sensor is simultaneously provided in the wearable device for detecting the motion data of the non-force-generating foot, and besides, each wearable device may further include a battery, a microprocessor, a communication module and other components. The system comprises an inclination angle sensor (or an angular motion detection device), an acceleration sensor and an electrocardio sensor, wherein the inclination angle sensor (or the angular motion detection device), the acceleration sensor and the electrocardio sensor are respectively used for acquiring inclination angle data, acceleration data and human electrocardio data of a non-forced human foot, and the microprocessor is used for acquiring the acquired data and transmitting the acquired data to electronic equipment for providing a central processing function through a communication module. Among these, an APP (application program) for providing a central processing function to recognize a human body state, which is transmittable to an electronic device such as a mobile phone, is exemplified. The APP receives the electrocardio data, acceleration and inclination angle motion data transmitted by each wearable device through a communication module of the electronic device (for example, a Zigbee communication module), and obtains the acceleration value, the inclination angle value and the heart rate value of a human body by calculating corresponding data based on an acceleration calculation module, an inclination angle calculation module and a three-lead electrocardio module of the APP.

As one example, the heart rate condition may be, but is not limited to:

The rate of change of heart rate exceeds a set first threshold.

The motion profile conditions may include, but are not limited to:

Corresponding to the above, the functions of the processor 1102 in the electronic device may also be implemented by the following processing procedures:

And acquiring electrocardiograph data and motion data of the human body, which are transmitted by at least one wearable device bound to the human body. The motion data comprises first motion data of non-force-generating feet transmitted by wearable equipment bound to the ankle part of the human body and second motion data of the wrist of the human body transmitted by the wearable equipment bound to the wrist part of the human body; acquiring data records of human body electrocardio data, ankle movement data and wrist movement data in a tumbling state; determining whether the acquired electrocardio data, the first motion data and the second motion data are respectively matched with the data records of the human electrocardio data, the non-forced ankle motion data and the wrist motion data; if the human body is matched with the human body, determining that the human body is in a falling state; and triggering a notification event for representing the human body falling based on the prestored contact information if the identified human body state represents that the human body is in the falling state.

In a specific implementation, preferably, a batch of historical data records (such as electrocardiographic data, non-stressed ankle motion data and wrist motion data) of the human body in a tumbling state and a batch of historical data records (such as electrocardiographic data, non-stressed ankle motion data and wrist motion data) of the human body in a non-tumbling state are obtained in advance as sample data to train an AI processing model. The algorithm adopted in model training can be, but is not limited to, a K-means clustering algorithm, three types of data of the same human body at the same time are used as one sample in a batch of historical data records, and the model continuously learns the human body characteristics reflected by each sample data in the falling state and the non-falling state of the human body based on the training process.

The motion of the wrist at the moment of falling of the human body is compared with the motion of the wrist in a normal state, although the motion of the wrist has no particularly obvious characteristic difference, the difference between the motion and the motion still exists, and in model training based on big data, the motion can be captured and learned by a model from a large number of training samples, which can also provide assistance for human body state identification, therefore, in a matching identification mode based on large historical data, such as an identification mode based on an AI processing model, the embodiment combines the human body state identification by adopting the human body electrocardio data, the special motion data of the non-force foot of the human body and the motion data of the wrist of the human body, and can realize high-accuracy human body state identification in the state identification based on an AI model by combining the characteristics shown by different types of human body state data.

It should be noted that, in the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described as different from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.

For convenience of description, the above system or apparatus is described as being functionally divided into various modules or units, respectively. Of course, the functions of each element may be implemented in the same piece or pieces of software and/or hardware when implementing the present application.

From the above description of embodiments, it will be apparent to those skilled in the art that the present application may be implemented in software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present application.

Finally, it is further noted that relational terms such as first, second, third, fourth, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application, which are intended to be comprehended within the scope of the present application.

Claims

1. An information processing method, the method comprising:

Acquiring human body state data transmitted by at least one wearable device bound to a human body; the human body state data at least comprises human body foot motion data, the human body foot motion data only comprises first motion data of a non-force-generating foot of a human body, the at least one wearable device comprises a wearable device bound to a part associated with the human body foot, and the wearable device bound to the part associated with the human body foot only comprises a wearable device bound to the part associated with the non-force-generating foot of the human body; the non-forced feet are the feet which are used for preventing the human body from falling based on subconscious and instinctive extension under the condition of falling; the human body state data also comprises second motion data transmitted by the wearable device bound to the wrist part of the human body;

After acquiring human body state data comprising first motion data of a human body non-forced foot and second motion data of a human body wrist part transmitted by at least one wearable device, identifying a human body state based on the human body state data;

2. The method of claim 1, the acquiring the body state data transmitted by the at least one wearable device bound to the body, comprising:

and acquiring electrocardiograph data and motion data of the human body, which are transmitted by at least one wearable device bound to the human body.

3. The method of claim 2, the identifying a human body state based on the human body state data, comprising:

4. The method of claim 2, the identifying a human body state based on the human body state data, comprising:

Determining whether the acquired electrocardio data, the non-forced ankle movement data and the second movement data are respectively matched with the data records of the human electrocardio data, the non-forced ankle movement data and the wrist movement data;

5. The method according to claim 3 or 4, wherein if the identified human body state represents a human body state abnormality, triggering a notification event for representing a human body state abnormality comprises:

6. An electronic device, comprising:

a memory for storing at least one set of instructions;

A processor for calling and executing the instruction set in the memory, by executing the instruction set:

7. The electronic device of claim 6, the processor to obtain body state data transmitted by at least one wearable device bound to a human body, specifically comprising:

8. The electronic device of claim 7, the processor identifying a human body state based on the human body state data, comprising:

9. The electronic device of claim 7, the processor identifying a human body state based on the human body state data, comprising: