Activity safety monitoring and early warning system for old people

Abstract

The invention discloses an activity safety monitoring and early warning system for old people, and relates to the technical field of safety monitoring of old people. Comprising a server, and the server is connected with a data acquisition module, a physiological analysis module, a behavior analysis module, an early warning analysis module and an environment analysis module; the data acquisition module is used for acquiring physiological status and behavior information of old people; the physiological analysis module analyzes parameters such as heart rate, body temperature, blood oxygen and pressure to calculate activity ampere; the behavior analysis module is used for classifying and analyzing behaviors of the old people; the environment analysis module analyzes weather and road factors, and the early warning analysis module performs grade analysis on received numerical values; the early warning operation is performed on the old people through the supervision early warning value, the activity is safely and accurately monitored, the potential safety hazard of the activity is accurately monitored, the physiological state of the old people in the behavior is judged, a response can be quickly made when a special abnormal condition is detected, and whether the old people face an emergency condition or not is judged.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of safety monitoring of the elderly, in particular to an activity safety monitoring and early warning system for the elderly. BACKGROUND

[0002] The activity safety monitoring system for the elderly creates multiple benefits for society through intelligent sensors and advanced technology. First, the real-time monitoring and early warning function enables rapid response to emergencies, reducing the risk of accidents for the elderly. Second, through the analysis of intelligent algorithms, the system can predict the activity patterns and health status of the elderly, provide early intervention and personalized care, and promote health management. The positioning service facilitates the search for missing persons, increases the sense of security of the elderly, and the remote access function provides real-time information for guardians and medical personnel, strengthening the social care and support for the elderly.

[0003] At present, the activity safety monitoring of the elderly has made significant progress with existing technology. Sensing technology such as intelligent sensors and cameras is widely used in home environments to monitor the activities of the elderly in real time, analyze behavior patterns through artificial intelligence and machine learning algorithms, and timely detect abnormal situations and trigger early warnings. However, the existing activity safety monitoring of the elderly has a large monitoring and early warning error rate, cannot accurately monitor and the abnormal behavior of the elderly, resulting in the activities of the elderly not being guaranteed and potential safety hazards. SUMMARY

[0004] The purpose of the present application is to solve the problem of the existing activity safety monitoring of the elderly, which has a large monitoring and early warning error rate, cannot accurately monitor and the abnormal behavior of the elderly, resulting in the activities of the elderly not being guaranteed and potential safety hazards.

[0005] The purpose of the present application can be achieved by the following technical scheme: an activity safety monitoring and early warning system for the elderly, comprising: A data acquisition module acquires physiological state information, behavior information and information affecting the safety of the elderly during activity. As a preferred embodiment of the present application, the physiological state information collected by the data collection module includes heart rate, body temperature, blood oxygen, blood pressure, stress information and corresponding collection time; the behavior activity is collected by the intelligent collection device acceleration, behavior trajectory, gyroscope data, air pressure value, vibration frequency and high-definition camera of the real-time behavior of the old people, and behavior recognition is performed on the collected data to obtain walking, jogging, taijiquan, social dance, meditation and breathing exercise and other behavior activities, and the behavior information is formed by the behavior activity and the corresponding collection time; the influence of the old people's safety activity information includes weather type and road data; the weather type includes rain, snow, hail, haze and corresponding wind force level; the rain includes; the snow includes blizzard, heavy snow, moderate snow and small snow; the hail includes large hail, large hail, medium hail and small hail; the haze includes severe pollution, heavy pollution, moderate pollution and light pollution; the wind force level includes typhoon, gale, strong wind and road data includes road roughness, traffic flow and road visibility brightness; The physiological analysis module is used for analyzing the collected heart rate, body temperature, blood oxygen, blood pressure, stress information and corresponding collection time, the physiological analysis module analyzes, calculates the heart rate fluctuation number by heart rate fluctuation and corresponding time, obtains the body temperature fluctuation number by analyzing the body temperature temperature collection, directly compares the blood oxygen and pressure at a certain time, and calculates the oxygen pressure fluctuation number; the blood pressure delta is analyzed and brought into the blood pressure fluctuation curve to analyze and obtain the blood pressure fluctuation number, and the physiological state information activity number is obtained based on the analysis of heart rate, body temperature, blood oxygen, blood pressure and stress.

[0006] As a preferred embodiment of the present application, the analysis process of the activity number of the physiological state information of the old people is as follows: The heart rate alpha is analyzed, the mean value of the heart rate value in a certain time interval is calculated to obtain the mean value of the heart rate in a certain time, and the heart rate value is substituted into the graph to obtain the heart rate fluctuation curve; the corresponding heart rate fluctuation interval is plotted on the heart rate fluctuation curve; the heart rate fluctuation interval includes the interval maximum line and the interval minimum line; the closed region one surrounded by the interval maximum line above the heart rate fluctuation curve is calculated, the area of the closed region one is calculated, and the total area of the closed region one is calculated ; the closed region two surrounded by the interval minimum line below the heart rate fluctuation curve is calculated, the area of the closed region two is calculated, and the total area of the closed region two is calculated ; The maximum and minimum values of the heart rate in the i time interval in the heart rate fluctuation curve are identified and marked as and respectively; i=1, 2, …, n1; the formula is substituted as To obtain the heart rate motion shadow number corresponding to the heart rate of the elderly. e is a natural number. This is the preset heart rate value. Let i be the average heart rate over time interval i. The mean heart rate for time interval i is preset, and a1, a2, a3, and a4 are preset weighting coefficients. Substituting the body temperature β at a certain moment into the formula Output the body temperature motion data corresponding to the elderly person's body temperature. , Preset healthy body temperature for the elderly; when the body temperature is stable at a certain moment If the body temperature falls within the preset range, then the body temperature at that moment is marked as a normal body temperature. Conversely, an abnormal body temperature The body temperature isotropic shadow count is compared with a preset range; if the body temperature isotropic shadow count does not belong to the preset range, the body temperature isotropic shadow count is marked as an abnormal body temperature isotropic shadow count; all abnormal body temperature isotropic shadow counts are summed to obtain the total abnormal body temperature isotropic shadow count. ; blood oxygen Analyze the blood oxygen levels with pressure ε, and directly compare the blood oxygen values ​​at a specific moment. A blood oxygen level within the range of [90, 100] is considered normal. Take the preset value directly. 1; When blood oxygen A blood oxygen level within the range of [80, 90] is considered abnormal. Take the preset value directly. 2; When blood oxygen A blood oxygen level within the range of [70, 80] is considered a special abnormal condition; therefore, the blood oxygen value... Take the preset value three directly 3; Preset value one is greater than preset value two, and preset value two is greater than preset value; Then, the pressure at a specific moment is directly compared. When the pressure ε is in the range of [0, 25], the mood is comfortable; when the pressure ε is in the range of [26, 35], it is mild pressure, so the pressure value ε is directly taken as the preset value 4 ε4; when the pressure ε is in the range of [36, 49], it is moderate pressure, so the blood oxygen value ε is directly taken as the preset value 5 ε5; when the pressure ε is in the range of [50, 60], it is severe pressure, so the blood oxygen value ε is directly taken as the preset value 6 ε6. The preset value 4 is less than the preset value 5, and the preset value 5 is less than the preset value 6. Blood oxygen and pressure values ​​are calculated at a specific moment and substituted into the formula. Output the oxygen pressure monitoring data of the elderly. e is a natural number (p=1, 2, 3; o=4, 5, 6), and b1 and b2 are preset weighting factors.

[0007] Analyzing blood pressure δ, the blood pressure values ​​are substituted into the graph to obtain a blood pressure fluctuation curve. When blood pressure falls within a preset healthy blood pressure range at a given moment, it is considered normal blood pressure; otherwise, it is considered abnormal blood pressure. The time intervals within time j that exceed the preset healthy blood pressure range [q, w] are summed, and the time intervals within time j that do not exceed the preset healthy blood pressure range [q, w] are summed. The total time for abnormal and normal blood pressure is then labeled as follows: and ; through the obtained total time of abnormal blood pressure Total time to normal blood pressure within the preset healthy blood pressure range [q, w] Compare; calculate blood pressure variability from blood pressure variability curves. The presupposed blood pressure fluctuation rate for healthy elderly individuals is... Substitute into the formula , For the blood pressure monitoring data of the elderly, b3 and b4 are weighting factors.

[0008] Substitute the above data into the formula The activity ampere number COM for obtaining physiological state information, a5, a6, a7, and a8 are preset weighting factors.

[0009] The behavior analysis module categorizes behavioral activities and analyzes physiological state information. For movement behavior, based on vibration frequency, total trajectory distance, and total time collected by the data acquisition module, it calculates a behavior anomaly coefficient to assess the physiological state of the elderly during movement. For static activities, the module analyzes total time, total limb movement trajectory length, limb acceleration, and body center of gravity position, outputting an anomaly coefficient for the static activity state to assess the physiological condition of the elderly in a static state. For area-based behavioral activities, the module acquires information such as air pressure, vibration frequency, and limb acceleration to calculate an anomaly coefficient for the area-based behavior, used to determine the physiological state of the elderly during activities such as Tai Chi or social dancing. The system can respond quickly when it detects special abnormalities to determine whether the elderly person is in an emergency, such as unconsciousness, stroke, or fall.

[0010] As a preferred embodiment of the present invention, the behavior analysis module performs the following specific analysis process on the anomaly coefficients during movement, static conditions, and regional conditions: The activities of older adults are categorized into three types: mobile, sedentary, and zoned. Activities such as walking or jogging are classified as mobile; activities such as meditation or breathing exercises are classified as sedentary; and activities such as Tai Chi or social dancing are classified as zoned. When the behavior activity of the elderly is moving, the behavior information is detected, the activity coefficient COM1 of the physiological state information in the moving process is obtained, the increase and decrease based on the quiet state of the elderly are compared, and the vibration frequency of the elderly in the moving process is monitored; the formula is substituted to obtain the behavior abnormality coefficient in moving , respectively the total distance and the total time of the trajectory in moving, the vibration frequency, d1u, d2u, d3u are preset weight factors; u=1 or 2; d11, d21, d31 are preset weight factors in walking; d12, d22, d32 are preset weight factors in slow running; When the elderly are in static activity, the activity coefficient COM2 of the physiological state information in the static activity is obtained, as well as the movement trajectory of the body movement in space, the body acceleration, and the body center of gravity position; according to the formula output the behavior abnormality coefficient of the static activity state of the elderly , respectively the total time of the static activity and the total length of the body movement trajectory, the value of the body acceleration, H2 is the height value of the body center of gravity position from the ground, d4, d5, d6 and d7 are preset weight factors; S=1 or 2; d14, d15, d16, d17 are preset weight factors in meditation; d24, d25, d26, d27 are preset weight factors in breathing exercise; when rapidly decreases and is 0 and the fluctuation of H2, the value of in a certain time period is calculated, according to the comparison of the special abnormal interval of the preset , the analysis is carried out, and it is concluded that the elderly are in emergency situations such as coma, stroke, and fall; When the elderly are in regional behavior activity, the activity coefficient COM3 of the physiological state information of the elderly, the movement trajectory of the body in space, the vibration frequency, the body acceleration, and the value of the air pressure are obtained, according to the formula output the behavior abnormality coefficient of the region of the elderly , the value of the air pressure, the vibration frequency of the regional behavior activity, respectively the total time of the static activity and the total length of the body movement trajectory, the value of the body acceleration; c1, c2, c3, c4 and c5 are preset weight factors; I=1 or 2; c11, c21, c31, c41, c51 are preset weight factors in Taijiquan; c12, c22, c32, c42, c52 are preset weight factors in social dance; The environmental analysis module provides a comprehensive weather influence value evaluation for the activity safety of the elderly by detailed classification and grading of the weather type, and detailed grading of rain, snow, hail, haze, and wind force, and setting corresponding grade values in rainy and snowy conditions to more accurately reflect the influence degree of weather on the elderly. By substituting the current weather type in which the elderly are located into the formula, the corresponding weather influence value is calculated. Finally, all weather influence values in which the elderly are located are summed to obtain the total weather influence value.

[0011] As a preferred embodiment of the present application, the environmental analysis module analyzes the weather type and road data, and the specific process is as follows: The weather type is analyzed, a weather influence value corresponding to each weather type is set, the weather type is classified according to rain, snow, hail, haze, and wind force, and the rainy day is graded into heavy rain, heavy rain, moderate rain, and light rain, and corresponding grade values are set, in order, Y14, Y13, Y12, Y11; the snowy day is graded into snow, snow, snow, and snow, respectively Y24, Y23, Y22, Y21; the hail day is graded into large hail, large hail, medium hail, and small hail, respectively Y34, Y33, Y32, Y31; the haze day is graded into severe pollution, heavy pollution, moderate pollution, and light pollution, respectively Y44, Y43, Y42, Y41; the wind force is graded into typhoon, gale, strong wind, and strong wind, respectively Y54, Y53, Y52, Y51; the current weather type in which the elderly are located is analyzed and substituted into TQ=Tq1×0.6+Ysg×0.4 to obtain the corresponding weather influence value TQ; s=1, 2, 3, 4, 5; g=1, 2, 3, 4; all weather influence values in which the elderly are located are summed to obtain the total weather influence value TZ; The road data is analyzed, and when the elderly walk on a certain road, the road safety level is calculated according to the road surface flatness, traffic flow, and visual brightness; since there are differences in the safety level of each road, m is set as the road number, m=1, 2, 3, …, m1; m1 is the total number of road numbers, and substituted into the formula Output the road safety number , e1, e2, e3 are preset weight factors; PZ, CL, and SL are the corresponding values of flatness, traffic flow, and visual brightness; Finally, the activity safety number of the elderly physiological state information , the total weather influence value TZ, the behavior abnormality coefficient , and the road safety number are substituted into the formula Output the supervision early warning value ; h1, h2, h3, h4 are preset weight factors; The early warning analysis module sets an abnormal interval range by interval comparison of abnormal values, comprehensively supervises the physiological parameters and activity state of the elderly, comprehensively judges the overall abnormal values, sets each preset interval according to specific activities, identifies abnormal states, and in the dangerous alarm state, the system can quickly link the intelligent device, notify the emergency contact person and provide the life safety information and position of the elderly.

[0012] As a preferred embodiment of the present application, the early warning analysis module performs interval comparison analysis on abnormal values, and the specific process is as follows: The early warning analysis module performs interval comparison analysis on received abnormal values, the early warning analysis module presets abnormal interval ranges Bd1, Bd2 and Bd3, the early warning analysis module receives a warning value UJ, when UJ is within the Bd1 interval range, it is normal; when the received warning value UJ is within the Bd2 interval range, it is a safe warning state; when the received warning value UJ is within the Bd3 interval range, it is a dangerous alarm state; the early warning analysis module analyzes the received abnormal values, for example, supervises the warning value Abnormal is caused by heart rate The early warning analysis module analyzes the heart rate , performs preset heart rate interval comparison, when the heart rate value is within the preset interval, it is a normal state, otherwise it is an abnormal state, and then the early warning analysis module performs early warning or alarm on the heart rate abnormal state; When the elderly are in a certain activity, the corresponding interval is different, each activity is set to correspond to a preset interval range, and the activity interval is ; n is a specific activity, when the early warning analysis module receives an abnormal value of the specific activity Within the interval range, it is normal; when the early warning analysis module receives an abnormal value of the specific activity Within the interval range, it is a safe warning state; when the early warning analysis module receives an abnormal value of the specific activity Within the interval range, it is a dangerous alarm state; when the elderly are in a dangerous alarm state, the intelligent device contacts the preset emergency contact person for a call, and sends the life safety information and position information of the elderly to the emergency contact person; Through the early warning analysis module and the behavior supervision analysis module, when the elderly are in a coma, stroke, fall and other emergency situations, the intelligent device dials 120 at the first time, sends the position information to 120 through the built-in map and positioning system, and then sends the life safety information and position information of the elderly to the emergency contact person; The present application has the following advantages: 1、The application obtains the activity safety number by analyzing the physiological state information of the old people through the physiological analysis module; obtains the behavior abnormality coefficient by analyzing the behavior information of the old people through the behavior analysis module; and obtains the supervision early warning value through the activity safety number, the behavior abnormality coefficient, the weather total image value and the road safety number, so as to perform the early warning operation on the old people, facilitate the safe and accurate monitoring of the activities of the old people, and accurately monitor the safety hidden danger of the activities of the old people, thereby guaranteeing the safety of the old people.

[0013] 2、The application analyzes the heart rate of the old people, calculates the values in the heart rate fluctuation curve, obtains the heart rate safety dynamic image number, and calculates the body temperature safety dynamic image number, accurately identifies abnormal body temperature, and provides real-time monitoring of the body temperature of the old people. 3、The application classifies the moving, static and regional behaviors and analyzes the physiological state information, calculates the abnormality coefficient in the data analysis of the moving behavior, the static activity and the regional behavior process, judges the physiological state of the old people in the behavior, and can quickly respond when detecting special abnormal conditions, and judges whether the old people face an emergency situation. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to facilitate the understanding of those skilled in the art, the technical solutions of the present application will be further described below with reference to the accompanying drawings.

[0015] Figure 1 The system principle block diagram of the present application is shown in the figure. Figure 2 The heart rate fluctuation curve of the present application is shown in the figure. Figure 3 The blood pressure fluctuation curve of the present application is shown in the figure. DETAILED DESCRIPTION

[0016] The technical solutions of the present application will be described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0017] Embodiment 1 Please refer to Figure 1 The old people activity safety monitoring and early warning system shown in the figure comprises a server, the server is connected with a data acquisition module, a physiological analysis module, a behavior analysis module, a warning analysis module and an environment analysis module. The data acquisition module is used for acquiring the collection permission authorization of the elderly, and collecting and sending the physiological state information, behavior information and influence old people's safety activity information of the authorized elderly to the server. The physiological state information includes heart rate, body temperature, blood oxygen, blood pressure, stress information and corresponding collection time; the behavior activity includes acceleration, behavior trajectory, gyroscope data, air pressure value, vibration frequency and high-definition camera of real-time behavior of the elderly collected by the intelligent collection device, and behavior recognition is performed on the collected data to obtain behavior activities such as walking, jogging, taijiquan, social dance, meditation and breathing exercise, and the behavior information is composed of behavior activities and corresponding collection time; the influence old people's safety activity information includes weather type and road data; the weather type includes rain, snow, hail, haze and corresponding wind force level; the rain includes; the snow includes blizzard, heavy snow, moderate snow and small snow; the hail includes large hail, large hail, medium hail and small hail; the haze includes severe pollution, heavy pollution, moderate pollution and light pollution; the wind force level includes typhoon, gale, strong wind and road data includes road roughness, traffic flow and road visual brightness; The physiological analysis module is used for analyzing the physiological state information in the server, and the specific analysis method is as follows: The heart rate α is analyzed, the values of the heart rate in a certain time interval are averaged to obtain the average heart rate in a certain time, and then the values of the heart rate are substituted into the graph to obtain a heart rate fluctuation curve graph; the corresponding heart rate fluctuation interval is plotted on the heart rate fluctuation curve graph; the heart rate fluctuation interval includes interval maximum line and interval minimum line; the closed area one surrounded by the interval maximum line above the heart rate fluctuation curve is calculated, the area of the closed area one is calculated, and the total area of the closed area one is obtained by summing up the areas of all the closed areas The closed area two surrounded by the interval minimum line below the heart rate fluctuation curve is calculated, the area of the closed area two is calculated, and the total area of the closed area two is obtained by summing up the areas of all the closed areas The maximum and minimum values of the heart rate in the i time interval in the heart rate fluctuation curve graph are identified and marked as and respectively; i=1, 2, …, n1; n1 represents the total number of i time interval; substitute the formula: The heart rate fluctuation number corresponding to the heart rate of the elderly is obtained , e is a natural number, is a preset value of the heart rate, is the average heart rate in the i time interval, is the preset average heart rate in the i time interval, and a1, a2, a3 and a4 are preset weight coefficients; The body temperature β at a certain moment is substituted into the formula The body temperature fluctuation number corresponding to the body temperature of the elderly is output , The preset health temperature of the elderly; when the body temperature at a certain moment is in the preset temperature interval , mark the body temperature at this moment as the body temperature in the normal state , otherwise as the body temperature in the abnormal state ; compare the body temperature with the preset interval: if the body temperature does not belong to the preset interval, mark the body temperature as an abnormal body temperature; sum all abnormal body temperatures to get the total abnormal value ; Analyze blood oxygen and pressure ε, take the blood oxygen at a certain moment for direct value comparison, when the blood oxygen is in the [90, 100] interval, it is in the normal state, then the blood oxygen value directly takes the preset value one 1; when the blood oxygen is in the [80, 90] interval, it is in the abnormal state, then the blood oxygen value directly takes the preset value two 2; when the blood oxygen is in the [70, 80] interval, it is in the special abnormal state; then the blood oxygen value directly takes the preset value three 3; the preset value one is greater than the preset value two, and the preset value two is greater than the preset value; Take the pressure at a certain moment for direct value comparison, when the pressure ε is in the [0, 25] interval, the mood is relaxed, when the pressure ε is in the [26, 35] interval, it is in the mild pressure state, then the pressure value ε directly takes the preset value four ε4; when the pressure ε is in the [36, 49] interval, it is in the moderate pressure state, then the blood oxygen value ε directly takes the preset value five ε5; when the pressure ε is in the [50, 60] interval, it is in the severe pressure state, then the blood oxygen value ε directly takes the preset value six ε6; the preset value four is less than the preset value five, and the preset value five is less than the preset value six; calculate the blood oxygen and pressure value at a certain moment, and substitute it into the formula Output the oxygen pressure fluctuation of the elderly , e is a natural number, (p=1, 2, 3; o=4, 5, 6), b1, b2 are preset weight factors Analyze blood pressure δ, substitute the blood pressure value into the graph to get the blood pressure fluctuation curve, when the blood pressure is in the preset healthy blood pressure range at a certain moment, it is normal blood pressure, otherwise it is abnormal blood pressure, sum the time corresponding to the blood pressure value that exceeds the preset healthy blood pressure interval [q, w] in the j time interval in the blood pressure fluctuation curve, and sum the time corresponding to the blood pressure value that does not exceed the preset healthy blood pressure interval [q, w] in the j time interval; sum the total time of abnormal and normal blood pressure, and mark them as and ; the total time of abnormal blood pressure obtained is compared with the preset health blood pressure interval [q, w] total time of normal blood pressure ; the blood pressure fluctuation rate is calculated from the blood pressure fluctuation curve , the preset health blood pressure fluctuation rate of the elderly is , the formula is , is the blood pressure fluctuation index of the elderly, b3, b4 are weight factors.

[0018] the formula is get the activity index COM of physiological state information, a5, a6, a7, a8 are preset weight factors; analyze the behavior information through the behavior analysis module: classify the behavior activities of the elderly, including movement, static and area; when the behavior activity is walking or jogging, it is classified as movement; when the behavior activity is meditation or breathing exercise, it is classified as static; when the behavior activity is taijiquan or social dance, it is classified as area; when the behavior activity of the elderly is movement, the behavior information is detected to obtain the activity index COM1 of physiological state information in the movement process, the increase and decrease based on the quiet state of the elderly are compared, and the vibration frequency of the elderly in the movement process is monitored; the formula is get the behavior abnormality coefficient when moving , respectively, the total distance and total time of the trajectory when moving, is the vibration frequency, d1u, d2u, d3u are preset weight factors; u=1 or 2; d11, d21, d31 are preset weight factors when walking; d12, d22, d32 are preset weight factors when jogging; when the elderly are in static activity, the activity index COM2 of physiological state information in static activity and the movement trajectory of the body movement in space, the body acceleration and the body center of gravity position are obtained; according to the formula output the behavior abnormality coefficient of the static activity state of the elderly , respectively, the total time of static activity and the total length of the body activity trajectory, is the value of the body acceleration, H2 is the height value of the body center of gravity position from the ground, d4, d5, d6 and d7 are preset weight factors; S=1 or 2; d14, d15, d16, d17 are preset weight factors when meditating; d24, d25, d26, d27 are preset weight factors when breathing; in a certain time period, rapidly decrease and When the value of H2 is 0 and the fluctuation of H2 is calculated in a certain time period , according to the preset special abnormal interval comparison, analysis is carried out, and it is concluded that the elderly are in emergency situations such as coma, stroke, and fall; When the elderly are in regional behavior activities, the activity safety number COM3 of the physiological state information of the elderly, the movement trajectory of the limbs in space, the vibration frequency, the limb acceleration, and the value of the air pressure are obtained, and the regional behavior abnormality coefficient of the elderly is output according to the formula , , the value of the air pressure, the vibration frequency of the regional behavior activity, the total time of static activity and the total length of the limb activity trajectory, the value of the limb acceleration; c1, c2, c3, c4, and c5 are preset weight factors; I=1 or 2; c11, c21, c31, c41, and c51 are preset weight factors when practicing taijiquan; c12, c22, c32, c42, and c52 are preset weight factors when dancing; The environmental analysis module analyzes the weather type, sets a weather influence value corresponding to each weather type, classifies the weather type into rain, snow, hail, haze, and wind, and then divides the rainy day into heavy rain, moderate rain, light rain, and small rain, and sets the corresponding grade values in turn as Y14, Y13, Y12, and Y11; the snow day is divided into snow, snow, snow, and snow, respectively as Y24, Y23, Y22, and Y21; the hail day is divided into large hail, large hail, medium hail, and small hail, respectively as Y34, Y33, Y32, and Y31; the haze day is divided into severe pollution, heavy pollution, moderate pollution, and light pollution, respectively as Y44, Y43, Y42, and Y41; the wind size is divided into typhoon, gale, strong wind, and strong wind, respectively as Y54, Y53, Y52, and Y51; the current weather type of the elderly is analyzed and substituted into TQ=Tq1×0.6+Ysg×0.4 to obtain the corresponding weather influence value TQ; s=1, 2, 3, 4, 5; g=1, 2, 3, 4; the sum of all weather influence values of the elderly is obtained to obtain the total weather value TZ; The road data is analyzed, and when the elderly walk on a certain road, the road safety level is calculated according to the road surface flatness, traffic flow, and visual brightness; since there are differences in the safety level of each road, m is set as the road number, m=1, 2, 3, …, m1; m1 is the total number of road numbers, and the formula outputs the road safety number , e1, e2, e3 preset weight factor; PZ, CL and SL are the integral, traffic and visual field brightness, respectively; Through the early warning analysis module, the activity number of the physiological state information of the elderly , the total weather value TZ, the behavior abnormality coefficient and the road number are substituted into the formula The monitoring early warning value is outputted ; h1, h2, h3, h4 are preset weight factors; The received abnormal value is compared and analyzed in the interval, the early warning analysis module presets the abnormal interval range Bd1, Bd2, Bd3, the early warning analysis module receives the early warning value UJ, when UJ is in the Bd1 interval range, it is normal; when the received early warning value UJ is in the Bd2 interval range, it is a safe early warning state; when the received early warning value UJ is in the Bd3 interval range, it is a dangerous alarm state; the early warning analysis module analyzes the received abnormal value, for example, the monitoring early warning value The abnormality is caused by the heart rate activity number The early warning analysis module compares the heart rate activity number with the preset heart rate interval, when the heart rate value is in the preset interval, it is a normal state, otherwise it is an abnormal state, and the early warning analysis module further warns or alarms the heart rate abnormal state; When the elderly are in different intervals corresponding to certain activities, set the preset interval range corresponding to each activity, the activity interval is ; n is a specific activity, when the early warning analysis module receives the abnormal value of the specific activity in the interval range, it is normal; when the early warning analysis module receives the abnormal value of the specific activity in the interval range, it is a safe early warning state; when the early warning analysis module receives the abnormal value of the specific activity in the interval range, it is a dangerous alarm state; when the elderly are in a dangerous alarm state, the intelligent device contacts the preset emergency contact person for a call, and sends the life safety information and location information of the elderly to the emergency contact person; Through the behavior monitoring analysis module, when the current elderly are in coma, stroke, fall and other emergency situations, the intelligent device dials 120 at the first time, sends the location information to 120 through the built-in map and positioning system, and then sends the life safety information and location information of the elderly to the emergency contact person; The preferred embodiments of the application disclosed above are only to facilitate the elucidation of the application. The preferred embodiments do not describe all the details of the application and limit the application to the specific embodiments. Obviously, many modifications and variations can be made in light of the teachings above. The description is chosen and described in order to provide the best illustration of the application and its practical application to those skilled in the art and to enable those skilled in the art to best utilize the application. The application is limited only by the claims and their full scope and equivalents.

Claims

1. An activity safety monitoring and early warning system for the elderly, comprising a server, characterized in that, The server is connected with a data collection module, a physiological analysis module, a behavior analysis module, an early warning analysis module and an environment analysis module; The data collection module is used for collecting physiological state information, behavior information and information affecting the safety activities of the old people during their activities and sending the information to the server memory; The physiological analysis module is used for analyzing the physiological state information in the server, which specifically includes analyzing heart rate, body temperature, blood oxygen, stress and blood pressure to obtain heart rate activity image number, total abnormal image number, oxygen pressure activity image number and blood pressure activity image number; and analyzing and calculating the heart rate activity image number, total abnormal image number, oxygen pressure activity image number and blood pressure activity image number to obtain the activity number of the physiological state information; The specific process of analyzing the heart rate α is as follows: The values of the heart rate of a time interval are averaged to obtain the average heart rate in a certain time, and the values of the heart rate are substituted into a graph to obtain a heart rate fluctuation curve; the corresponding heart rate fluctuation interval is plotted on the heart rate fluctuation curve; the heart rate fluctuation interval includes an interval maximum line and an interval minimum line; the closed area one surrounded by the interval maximum line and above the heart rate fluctuation curve is calculated, the area of the closed area one is calculated, and the total area of all the closed areas one is summed up to obtain the total area of the region one ; the closed area two surrounded by the interval minimum line and below the heart rate fluctuation curve is calculated, the area of the closed area two is calculated, and the total area of all the closed areas two is summed up to obtain the total area of the region two ; the maximum and minimum values of the heart rate in the i time interval in the heart rate fluctuation curve are identified and marked as and respectively; i=1, 2, …, n1; n1 is the total number of i time intervals; and the formula is substituted corresponding to the heart rate of the elderly e is a natural number, is a preset heart rate value, is the average heart rate in the i time interval, is the preset average heart rate in the i time interval, and a1, a2, a3, and a4 are preset weight coefficients. The behavior analysis module is used for analyzing the behavior information of the old people to obtain a behavior abnormality coefficient of the old people; the behavior information analysis includes movement, static and regional behavior analysis; The environment analysis module is used for analyzing the information affecting the safety activities of the old people to obtain a weather influence value and a road safety number; The early warning analysis module analyzes the activity number of the physiological state information of the old people, the total image value of the weather, the behavior abnormality coefficient and the road safety number to obtain a supervision early warning value; the supervision early warning value is analyzed by grade; a preset interval range Bd1, Bd2 and Bd3 is set; when the supervision early warning value is in the Bd1 interval range, it is normal; when the supervision early warning value is in the Bd2 interval range, it is in a safety early warning state; when the supervision early warning value is in the Bd3 interval range, it is in a dangerous alarm state; the safety early warning state and the dangerous alarm state are subjected to early warning processing.

2. The safety monitoring and early warning system for the elderly's activities according to claim 1, characterized in that, The physiological state information includes heart rate, body temperature, blood oxygen, blood pressure, stress information and corresponding collection time; the behavior information includes behavior activities and corresponding collection time, and the behavior activities include walking, jogging, taijiquan, social dance, meditation and breathing exercise; the information affecting the safety activities of the old people includes weather type and road data.

3. The safety monitoring and early warning system for the elderly's activities according to claim 2, characterized in that, The physiological analysis module analyzes the body temperature, and the specific process is as follows: Substitute the body temperature β at a certain time into the formula Output the body temperature comfort index corresponding to the body temperature of the elderly , The preset healthy body temperature of the elderly When the body temperature variation number at a certain time falls within the preset body temperature interval, the body temperature at the time is marked as a normal state body temperature , and vice versa ; the body temperature variation number is compared with the preset interval: if the body temperature variation number does not belong to the preset interval, the body temperature variation number is marked as an abnormal body temperature variation number; and all abnormal body temperature variation numbers are summed to obtain an abnormal variation total value .

4. The safety monitoring and early warning system for the elderly according to claim 3, characterized in that, The physiological analysis module analyzes the blood oxygen and stress, and the specific process is as follows: the blood oxygen and stress at a certain moment are directly compared with preset interval values to obtain a preset value one or a preset value two or a preset value three corresponding to the blood oxygen and a preset value four or a preset value five or a preset value six corresponding to the blood pressure; the oxygen pressure activity image number is calculated by preset interval values.

5. The safety monitoring and early warning system for the elderly according to claim 4, characterized in that, The physiological analysis module analyzes the blood pressure, and the specific process is as follows: Summing the time corresponding to the blood pressure value exceeding the preset healthy blood pressure interval [q, w] in the j time interval in the blood pressure fluctuation graph to obtain the total abnormal blood pressure time , and comparing it with the preset healthy blood pressure interval [q, w] and the total normal blood pressure time , then calculating the blood pressure fluctuation rate from the blood pressure fluctuation graph , the preset healthy blood pressure fluctuation rate for the elderly is , and substituting the formula Output the blood pressure fluctuation graph of the elderly , b3 and b4 are weight factors; Substitute the data into the formula The activity indexes COMa5, a6, a7, a8 outputting the physiological state information are preset weight factors.

6. The safety monitoring and early warning system for the elderly's activities according to claim 1, characterized in that, The behavior analysis module analyzes the movement behavior, and the specific process is as follows: When the behavior activity of the old people is moving, the activity meter COM1 which acquires the physiological state information in moving is used to monitor the vibration frequency in moving; the formula is substituted to obtain the behavior abnormality coefficient in moving , the total distance and total time of the trajectory in moving respectively, the vibration frequency, d1u, d2u, d3u are preset weight factors; u=1 or 2; d11, d21, d31 are preset weight factors in walking; d12, d22, d32 are preset weight factors in slow running.

7. The safety monitoring and early warning system for the elderly according to claim 6, wherein, The behavior analysis module analyzes the static activity, and the specific process is as follows: The activity of the old people in the static activity is analyzed, the activity data COM2 of obtaining the physiological state information in the static activity, and the behavior data in the static state are obtained, the behavior abnormal coefficient of the static activity state of the old people is obtained according to the total time of the static activity, the total length of the limb activity track, the limb acceleration, and the height value of the body center position from the ground ; in a certain time period, according to the preset special abnormal interval comparison, it is concluded that the old people are in coma, stroke and fall.

8. The safety monitoring and early warning system for the elderly's activities according to claim 7, characterized in that, The behavior analysis module analyzes the regional behavior activity, and the specific process is as follows: When the old people carry out regional behavior activities, the activity number COM3 of acquiring physiological state information, the motion trajectory of the limbs in space, the vibration frequency, the limb acceleration and the value of air pressure, according to the formula Output the behavior abnormality coefficient of the old people in the region , The value of air pressure, The vibration frequency of the regional behavior activities, The total time of static activities and the total length of the limb activity trajectory respectively, The value of the limb acceleration; c1, c2, c3, c4 and c5 are preset weight factors; I=1 or 2; c11, c21, c31, c41, c51 are preset weight factors when doing taijiquan; c12, c22, c32, c42, c52 are preset weight factors when doing social dance.

9. The safety monitoring and early warning system for the elderly according to claim 8, wherein, The environment analysis module analyzes the regional behavior activity, and the specific process is as follows: The environment analysis module analyzes the weather type, sets a weather influence value corresponding to each weather type, classifies the weather types according to rain, snow, hail, haze and wind force to obtain corresponding weather influence values, and sums all the weather influence values of the old people to obtain a total image value of the weather; The road data is analyzed, and the road safety number is output according to road surface flatness, traffic flow and visual brightness.

10. The safety monitoring and early warning system for the elderly according to claim 1, wherein, The environment analysis module analyzes the regional behavior activities, and the specific process is: The pre-warning analysis module substitutes the activity number of physiological state information of the old people, the weather total image value, the behavior abnormality coefficient and the road number into the formula Output regulatory pre-warning value ; h1, h2, h3 and h4 are preset weight factors; the interval analysis is carried out on the abnormal value, the pre-warning analysis module presets the abnormal interval, the pre-warning analysis module receives the pre-warning value UJ, when UJ is within the interval one range, it is normal; when the received pre-warning value UJ is within the interval two range, it is a safe pre-warning state; when the received pre-warning value UJ is within the interval three range, it is a dangerous alarm state, the intelligent device contacts the emergency contact person to call, and sends the life safety information and the position to the emergency contact person; When the current old people are in coma or stroke or fall, 120 is dialed through the intelligent device, and the map and the located position are sent to 120, and the life safety information and the position of the old people are sent to the emergency contact person.

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