CN113509156A - Adaptive information processing method, system and storage medium based on behavior characteristics of old user - Google Patents
Adaptive information processing method, system and storage medium based on behavior characteristics of old user Download PDFInfo
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Abstract
The invention discloses a self-adaptive information processing method, a system and a storage medium based on behavior characteristics of an old user. The invention realizes the purpose of looking after the old people alone going out, sending an alarm when the old people meet an emergency condition, dialing a medical rescue call, and recording related data in real time before medical care personnel arrive.
Description
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a self-adaptive information processing method, a self-adaptive information processing system and a self-adaptive information processing storage medium based on behavior characteristics of an aged user.
Background
Since the 90 s of the 20 th century, the aging process of China is accelerating, and it is expected that the proportion of people aged 65 years and older to the total population will exceed 20% by 2040 years, and meanwhile, the aging trend of the aged people is becoming more and more obvious, and surveys show that the aged people aged 80 years and older are increasing at a rate of 5% per year, and it is expected that the aged people will increase to more than 7400 ten thousand by 2040 years. Due to the reasons that the modern society is fast in rhythm, children work busy, the number of solitary children is increased and the like, the number of empty-nest old people lacking in care is increased year by year, and the empty-nest old people lack in necessary care and cause accidents, so that a plurality of systems for monitoring the daily behaviors of the old people appear.
Disclosure of Invention
In order to solve the technical problems, the invention provides a self-adaptive information processing method, a self-adaptive information processing system and a storage medium based on behavior characteristics of an old user.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a self-adaptive information processing method based on the behavior characteristics of old users comprises the following steps:
s1, the old man wears a portable data collection device and acquires corresponding data information through the data collection device;
s2, classifying the data information obtained by the data collecting device;
s3, respectively extracting characteristic data according to different results of classifying the data obtained by the data collection equipment;
s4, processing the different characteristic data in the step S3 and obtaining different results;
s5, responding to different situations of the old people according to the different results in the step S4;
s6, a learning operation is performed on the result data obtained by executing the step S4.
As a preferred embodiment of the present invention, the data information acquired by the data collecting device in step S1 includes environmental information of the elderly, exercise information of the elderly, and physiological information of the elderly.
As a preferred embodiment of the present invention, the types of the data information in step S2 include an environmental information type, a motion information type, and a physiological information type.
As a preferred technical solution of the present invention, the processing of the different feature data in step S3 includes the following steps:
s41, processing the environmental information characteristic data to obtain the information of the places where the old people are located;
s42, processing the motion information characteristic data to obtain the information that the old people are moving;
s43, processing the physiological information characteristic data to obtain the current physical condition information of the old;
and S44, comprehensively analyzing the results obtained in the steps S41, S42 and S43 respectively to obtain the judgment about whether the state of the old people is normal when the old people performs the action in the specific scene.
As a preferred embodiment of the present invention, responding according to the different results in step S4 includes the following cases:
in case A, if the current state of the old man is normal through the comprehensive analysis of the step S4, the information of the place, the action, the physical condition and the like of the old man is sent to a remote contact person, and meanwhile, the going-out behavior of the old man is continuously monitored;
and B, if the current state of the old people is abnormal through the comprehensive analysis of the step S4, initiating an alarm to attract the attention of passers-by, dialing a medical rescue call and informing a remote contact person, and recording the information of the places, the actions, the physical conditions and the like of the old people in real time before the medical care personnel arrive.
As a preferred embodiment of the present invention, step S6 uses a machine learning algorithm on the result data about the location, action, and physical condition of the elderly person obtained by executing step S4, so that the elderly person can analyze and obtain information such as daily travel habits and hidden health risks.
The invention also provides a self-adaptive information processing system based on the behavior characteristics of the aged user, which comprises the following modules:
the first module is used for collecting data information of the old, and comprises the following units:
the first unit is used for collecting environmental information of the old, action information of the old and physiological index information of the old;
a second unit for transmitting the data information about the elderly person collected by the first unit to a second module, and simultaneously being capable of receiving result information transmitted from a fourth module;
the second module is used for classifying the data information obtained by the first module, and comprises the following units:
the third unit is used for receiving the data information transmitted from the first module and dividing the data information according to the environmental information class, the motion information class and the physiological information class;
a fourth unit, configured to store a result of class division performed on the data information by the third unit;
a fifth unit, configured to transmit a result of the classification of the data information by the third unit to a third module;
the third module is used for carrying out feature extraction on data information of the old people in a classified mode and comprises the following units:
a sixth unit, configured to receive data information of different categories transmitted from the second module, and extract feature data from the data information of different categories;
a seventh unit configured to store the feature data obtained by the sixth unit;
an eighth unit, configured to transmit the feature data obtained by the sixth unit to a fourth module;
the fourth module is used for processing the characteristic data of the data information of different categories and obtaining results, and comprises the following units:
a ninth unit, configured to receive feature data of different types of data information transmitted from the third module, process the feature data to obtain result data about places, actions, and physical health states of the elderly, and store the result data;
a tenth unit for comprehensively analyzing the result data about the place where the old person is, the action performed, and the health state obtained by the ninth unit, judging whether the state of the old person is normal when the old person performs the action in a specific scene, and storing the result data;
and the eleventh unit transmits a judgment result of whether the state of the tenth unit when the old people perform actions in a specific scene is normal to the first unit so as to respond to different situations encountered by the old people when the old people go out.
And the fifth module is used for performing learning operation on the result obtained by processing the feature data of the data information of different types by the fourth module.
Compared with the prior art, the invention has the following beneficial effects:
1. the self-adaptive information processing method and the self-adaptive information processing system based on the behavior characteristics of the old people realize the function of looking after the alone going-out behavior of the old people, acquire information such as places, actions and body health indexes of the old people through the portable information collecting equipment worn by the old people, and comprehensively judge whether the state of the old people is normal when the old people performs the actions in a specific scene.
2. The invention discloses a self-adaptive information processing method and a system based on behavior characteristics of an old user, which give different responses according to different conditions encountered by the old person when going out, and when the state of the old person is judged to be normal when the old person performs actions in a specific scene, the information such as the place where the old person is located, the actions performed, the physical conditions and the like is sent to a remote contact person, and meanwhile, the going-out behavior of the old person is continuously monitored; when the state of the old people is judged to be abnormal when the old people perform actions in a specific scene, an alarm is sent to attract the attention of passers-by, meanwhile, a medical rescue call is dialed, a remote contact person is notified, and information such as the places, the actions and the physical conditions of the old people is recorded in real time before medical care personnel arrive. The invention not only can look after the old people going out, but also can help the old people in time when the old people meet emergency, and provides reference data for the help of medical staff after arriving.
3. The self-adaptive information processing method and system based on the behavior characteristics of the old user disclosed by the invention use a machine learning algorithm for data about places, actions and physical health conditions of the old people when the old people go out, daily going habits of the old people can be obtained, and the old people can be reported to remote contacts in time when the going of the old people is not consistent with the habits.
Drawings
Fig. 1 is a flowchart of an adaptive information processing method based on behavior characteristics of an old user according to the present invention.
Fig. 2 is a flowchart of the method of step S2 in the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides an adaptive information processing method based on behavior characteristics of an aged user, which specifically includes the following steps:
and S1, the old man wears the portable data collection equipment and acquires corresponding data information through the data collection equipment.
Specifically, the portable data collection device worn by the elderly person in step S1 may include a portable camera device, a portable acceleration sensor device, a portable pressure sensor device, a smart band, and the like. The portable camera equipment is used for collecting image information, sound information and action information of the old people at places where the old people go out; the actions performed by the old people are obtained by analyzing the data collected by the portable acceleration sensor device worn on the waist of the old people and the portable pressure sensor device worn on the feet of the old people, and the old people are judged to be walking or moving normally or have the conditions of falling, lying down and the like; the intelligent bracelet worn on the wrist of the old can monitor the physiological indexes of the old, such as blood pressure, heart rate, blood oxygen saturation, body temperature and the like.
And S2, identifying and classifying the data information obtained by the data collecting equipment.
Specifically, in step S1, information about images and sounds of places where the elderly are located when they go out, information about images and sensors performing actions, and information about blood pressure, heart rate, body temperature, etc. representing physical health conditions are collected at the same time, so in step S2, the data information collected in step S1 is identified and classified according to the environmental information class, the exercise information class, and the physiological information class to facilitate subsequent processing and analysis of the data information, wherein the classification of the data information is implemented by a classification algorithm in machine learning, such as a naive bayes classification algorithm, an SVM algorithm, a KNN-based algorithm, etc.
And S3, respectively extracting characteristic data according to different results of classifying the data obtained by the data collection equipment.
Specifically, in step S3, data cleaning, data alignment, and data normalization are performed on the data information of the environmental information class, the exercise information class, and the physiological information class classified in step S2, and feature data are extracted from the data information by using a data feature extraction algorithm for determining the places, the performed actions, and the physical health conditions where the old people go out, wherein the data feature extraction algorithm may select a FAST algorithm, a SIFT algorithm, a SURF algorithm, and the like.
And S4, processing the different characteristic data in the step S3 and obtaining different results.
Specifically, the step S4 performs analysis processing of the data information based on the feature data extracted in the step S3, and with reference to fig. 2, includes the following steps:
s41, processing the environmental information characteristic data to obtain the information of the current old people;
s42, processing the motion information characteristic data to obtain the information that the old people are moving;
s43, processing the physiological information characteristic data to obtain the health condition information of the current old people;
and S44, comprehensively analyzing the results obtained in the steps S41, S42 and S43 respectively to obtain a judgment result about whether the state of the old people is normal when the old people performs the action in a specific scene.
Further, in step S41, the deep learning neural network is used to calculate the feature data of the acquired image and sound information of the location where the old people go out, and the location information where the old people go out is obtained by analysis, for example, the old people go out to a park, a vegetable market, a station, and the like.
Further, in step S42, the deep learning neural network is used to calculate the acquired image of the action performed by the old person going out and the feature data of the sensor information, and analyze the action performed by the old person going out, such as determining whether the old person is walking normally, exercising, sitting or standing, or whether the old person has an abnormal action such as falling down, lying down for a long time, etc.
Further, step S43 is to calculate the data characteristics of the acquired physiological index information of the old people when going out, such as heart rate, blood pressure, body temperature, and blood oxygen saturation, and analyze to obtain the health status of the old people, for example, when the old people has a condition of too fast heart rate, blood pressure and body temperature higher than normal level, and blood oxygen saturation is decreased, the old people may have a problem in health.
Further, in step S44, the information of the location where the elderly people are going out is analyzed and obtained in step S41, the information of the actions performed by the elderly people is analyzed and obtained in step S42, and the information of the health condition of the elderly people analyzed and obtained in step S43 is processed to determine whether the state of the elderly people during the actions performed in a specific scene is normal, which will be exemplified in different situations that the elderly people may encounter when going out.
Case a: when the old man goes out alone to the station and takes a car, the station environment is often noisy, the air quality is not good, and the personnel of coming and going are more, has certain safe risk, consequently is very necessary to effective nurse to the old man's action of going out at the station. In this case, the system analyzes that the place where the old people go out is the station in step S41, if the system analyzes that the current exercise state of the old people is walking in step S42, and the system analyzes that the physiological indexes such as heart rate, blood pressure and the like of the old people are all in the daily average range in step S43, the system comprehensively judges that the going-out behavior of the old people at the station is normal at the moment; if the current exercise state of the old people is recumbent through the analysis of the system in the step S42, and the physiological indexes of the old people, such as heart rate, blood pressure and the like, which are obtained through the analysis of the system in the step S43 are not in the daily average range, the system comprehensively judges that the old people are abnormal in the going-out behavior of the station at the moment, and the system needs to process the abnormal situation in the next step.
Case B: when the old people alone go out to the park to exercise, the environment temperature of the park changes along with seasons and weather, the old people cannot easily grasp the exercise intensity, and factors such as the environment temperature and the exercise intensity affect the health of the old people, so that the old people need to be effectively nursed when going out to exercise. In this case, the system analyzes that the place where the old people go out is the park in step S41, if the system analyzes that the old people are currently performing exercise such as running in step S42, and the system analyzes that the physiological indexes such as blood pressure and heart rate of the old people are increased to a certain extent outside the daily average range in step S43, the system comprehensively judges that the old people are doing exercise in the park and the going-out behavior of the old people is normal at the moment; if the system analyzes that the old people are currently performing exercise such as running in step S42, and the system analyzes that the physiological indexes such as blood pressure and heart rate of the old people are not in the daily average range such as rapidly rising in a short time in step S43, the system comprehensively judges that the old people are exercising in the park and the old people are abnormal in going-out behavior, and the system needs to process the abnormal situation in the next step.
Case C: when the old people need to pass through the crossroad when going out alone, the old people are necessary to effectively nurse the behavior of the old people because the road condition of the crossroad is complex, the old people are slow to move, and the old people have certain risks when passing through the road alone. In this case, at the time, the system analyzes to obtain that the current location of the old is the intersection through the step S41, if the system analyzes to obtain that the old is walking at present through the step S42, the system analyzes to obtain that the physiological indexes of the blood pressure, the heart rate and the like of the old are all in the daily average range through the step S43, the system comprehensively judges that the behavior of the old crossing the road at the time is normal, at the time, if the system analyzes to obtain that the current location of the old has the intersection through the step S41, the system analyzes to obtain that the old is still walking at present through the step S42, and the physiological indexes of the blood pressure, the heart rate and the like of the old are all in the daily average range through the step S43, the system comprehensively judges that the old has passed the road alone and has normal behavior.
And S5, responding to different situations of the old people according to different results in the step S4.
Specifically, the response operation according to the different analysis results in step S4 includes the following cases:
in case A, if the current going-out behavior of the old man is normal through the comprehensive analysis in the step S4, the portable information collection device (such as an intelligent bracelet) worn by the old man can send the information of the place, the action, the physical condition and the like of the old man to the remote contact person in the form of short messages or videos, so that the remote contact person of the old man can know the going-out behavior of the old man in time conveniently, and meanwhile, the system can continue to monitor the going-out behavior of the old man.
And B, if the current outgoing behavior of the old people is abnormal through the comprehensive analysis in the step S4, the portable information collecting device (such as an intelligent bracelet) worn by the old people receives the abnormal signal, sends an alarm to attract the attention of passersby, dials a medical rescue telephone and informs a remote contact person, and the portable information collecting device (such as the intelligent bracelet and the portable camera device) worn by the old people before medical care personnel arrive records the information of the places, the actions, the physical conditions and the like of the old people in real time, so that data reference is provided for the medical care personnel to arrive and then rescue work of the old people.
S6, a learning operation is performed on the result data obtained by executing the step S4.
Specifically, in step S6, a self-learning machine learning algorithm is used for the result data about the location, the action and the physical condition of the old person obtained by executing step S4, so that the daily travel habits of the old person can be analyzed and obtained, and when the travel of the old person is not in line with the habits, the old person can be reported to a remote contact person in time.
It should be noted that, the above-mentioned content describes an adaptive information processing method based on the behavior characteristics of the aged user, by collecting the behavior and action information, the physical and physiological index information and the environment information of the old, and the results of processing the three collected data are combined together, and the comprehensive analysis is carried out to obtain the judgment whether the overall state of the old is normal when the old carries out specific actions under certain environment, the judgment result is closely related to the environment, the action and the current physiological index of the old people at the same time, the occurrence of misjudgment is reduced, and the method has the advantage of high accuracy, can realize the function of looking after the old people in various application scenes, can not only monitor the home behavior of the old people and process the emergency situation, and the behavior of the old can be monitored and the emergency situation can be processed under the application scenes of going out alone, taking a bus, traveling, sports and the like.
The invention also provides a self-adaptive information processing system based on the behavior characteristics of the aged user, which comprises the following modules:
the first module is used for collecting data information of the old, and comprises the following units:
the first unit is used for collecting environmental information of the old, action information of the old and physiological index information of the old;
a second unit for transmitting the data information about the elderly person collected by the first unit to a second module, and simultaneously being capable of receiving result information transmitted from a fourth module;
the second module is used for classifying the data information obtained by the first module, and comprises the following units:
the third unit is used for receiving the data information transmitted from the first module and dividing the data information according to the environmental information class, the motion information class and the physiological information class;
a fourth unit, configured to store a result of class division performed on the data information by the third unit;
a fifth unit, configured to transmit a result of the classification of the data information by the third unit to a third module;
the third module is used for carrying out feature extraction on data information of the old people in a classified mode and comprises the following units:
a sixth unit, configured to receive data information of different categories transmitted from the second module, and extract feature data from the data information of different categories;
a seventh unit configured to store the feature data obtained by the sixth unit;
an eighth unit, configured to transmit the feature data obtained by the sixth unit to a fourth module;
the fourth module is used for processing the characteristic data of the data information of different categories and obtaining results, and comprises the following units:
a ninth unit, configured to receive feature data of different types of data information transmitted from the third module, process the feature data to obtain result data about places, actions, and physical health states of the elderly, and store the result data;
a tenth unit for comprehensively analyzing the result data about the place where the old person is, the action performed, and the health state obtained by the ninth unit, judging whether the state of the old person is normal when the old person performs the action in a specific scene, and storing the result data;
and the eleventh unit transmits a judgment result of whether the state of the tenth unit when the old people perform actions in a specific scene is normal to the first unit so as to respond to different situations encountered by the old people when the old people go out.
And the fifth module is used for performing learning operation on the result obtained by processing the feature data of the data information of different types by the fourth module.
The system executable instructions are stored by a storage medium, and the instructions are used for realizing an adaptive information processing method based on the behavior characteristics of the aged user when a processor included in the system executes the instructions.
It is noted that, herein, relational terms such as first and second, and the like may be 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. Also, 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.
It will be appreciated by those skilled in the art that the foregoing method embodiments of the invention may be implemented as a computer program product. Thus, for example, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. A self-adaptive information processing method based on the behavior characteristics of old users is characterized by comprising the following steps:
s1, the old man wears a portable data collection device and acquires corresponding data information through the data collection device;
s2, identifying and classifying the data information obtained by the data collecting equipment;
s3, respectively extracting characteristic data according to different results of identifying and classifying the data obtained by the data collection equipment;
s4, processing the different characteristic data in the step S3 and obtaining different results;
s5, responding to different situations encountered by the old people when going out according to different results in the step S4;
and S6, learning the result data of the old people on the places, the actions and the health conditions when the old people go out, which are obtained by executing the step S4.
2. The adaptive information processing method based on behavior characteristics of an elderly user according to claim 1, wherein the data information acquired by the data collection device in step S1 includes environmental information of the elderly, exercise information of the elderly, and physiological information of the elderly.
3. The adaptive information processing method based on the behavioral characteristics of the elderly user as claimed in claim 1, wherein the categories of data information in step S2 include environmental information category, motion information category, and physiological information category.
4. The adaptive information processing method based on the behavior characteristics of the aged user according to claim 1, wherein the processing of the different characteristic data in step S3 comprises the following steps:
s41, processing the environmental information characteristic data to obtain the information of the current location of the old;
s42, processing the motion information characteristic data to obtain the information that the old people are moving;
s43, processing the physiological information characteristic data to obtain the current health condition information of the old;
and S44, comprehensively analyzing the results obtained in the steps S41, S42 and S43 respectively to obtain the judgment about whether the state of the old people is normal when the old people performs the action in the specific scene.
5. The adaptive information processing method based on the behavior characteristics of the elderly user according to claim 1, wherein responding according to the different determination results in step S4 includes the following cases:
in case A, if the current outgoing state of the old people is normal through the comprehensive analysis of the step S4, the information of the places, the actions, the physical conditions and the like of the old people is sent to a remote contact person, and meanwhile, the outgoing behaviors of the old people are continuously monitored;
and B, if the current outgoing state of the old people is abnormal through the comprehensive analysis of the step S4, initiating an alarm to attract the attention of passers-by, dialing a medical rescue telephone and informing a remote contact person, and recording the information of the places, the actions, the physical conditions and the like of the old people in real time before the medical care personnel arrive.
6. An adaptive information processing system based on the behavior characteristics of an old user is characterized by comprising the following modules:
the first module is used for collecting data information of the old, and comprises the following units:
the first unit is used for collecting environmental information of the old, action information of the old and physiological index information of the old;
a second unit for transmitting the data information about the elderly person collected by the first unit to a second module, and simultaneously being capable of receiving result information transmitted from a fourth module;
the second module is used for identifying and classifying the data information obtained by the first module, and comprises the following units:
the third unit is used for receiving and identifying the data information transmitted from the first module, and dividing the data information according to the environmental information class, the motion information class and the physiological information class;
a fourth unit, configured to store a result of class division performed on the data information by the third unit;
a fifth unit, configured to transmit a result of the classification of the data information by the third unit to a third module;
the third module is used for carrying out feature extraction on data information of the old people in a classified mode and comprises the following units:
a sixth unit, configured to receive data information of different categories transmitted from the second module, and extract feature data from the data information of different categories;
a seventh unit configured to store the feature data obtained by the sixth unit;
an eighth unit, configured to transmit the feature data obtained by the sixth unit to a fourth module;
the fourth module is used for processing the characteristic data of the data information of different categories and obtaining results, and comprises the following units:
a ninth unit, configured to receive feature data of different types of data information transmitted from the third module, process the feature data to obtain result data about places, actions, and physical health states of the elderly, and store the result data;
a tenth unit for comprehensively analyzing the result data about the place where the old person is, the action performed, and the health state obtained by the ninth unit, judging whether the state of the old person is normal when the old person performs the action in a specific scene, and storing the result data;
the eleventh unit transmits a judgment result of whether the state of the tenth unit when the old people perform actions in a specific scene is normal to the first unit so as to respond to different situations encountered by the old people when the old people go out;
and the fifth module is used for performing learning operation on result data obtained by processing the feature data of the data information of different types by the fourth module.
7. A storage medium having stored therein instructions executable by the system of claim 6, wherein the instructions are adapted to implement an adaptive information processing method based on behavior characteristics of an elderly user according to any one of claims 1-5 when executed by a processor comprised by the system of claim 6.
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Citations (34)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1361503A (en) * | 2000-12-29 | 2002-07-31 | 南开大学 | Color multi-objective fusion identifying technology and system based on neural net |
| JP2005278765A (en) * | 2004-03-29 | 2005-10-13 | Sekio Ninomiya | Monitoring device for posture, breathing, heart rate and action, and physical abnormality judging method |
| CN201853320U (en) * | 2010-11-23 | 2011-06-01 | 南通大学 | Monitoring and alarm system for the old |
| US20130095459A1 (en) * | 2006-05-12 | 2013-04-18 | Bao Tran | Health monitoring system |
| US20150099946A1 (en) * | 2013-10-09 | 2015-04-09 | Nedim T. SAHIN | Systems, environment and methods for evaluation and management of autism spectrum disorder using a wearable data collection device |
| CN104618464A (en) * | 2015-01-16 | 2015-05-13 | 中国科学院上海微系统与信息技术研究所 | Internet-of-things-based smart home care service system |
| CN104835003A (en) * | 2015-05-04 | 2015-08-12 | 南京邮电大学 | System for mobile old-age services and implement method thereof |
| CN105278506A (en) * | 2015-11-05 | 2016-01-27 | 沈阳大学 | Smart home system with old-age care function |
| WO2016110804A1 (en) * | 2015-01-06 | 2016-07-14 | David Burton | Mobile wearable monitoring systems |
| CN205427566U (en) * | 2016-03-24 | 2016-08-03 | 沈阳理工大学 | Long -range basic station monitor system based on internet |
| US20160364549A1 (en) * | 2015-06-15 | 2016-12-15 | Baoguo Wei | System and method for patient behavior and health monitoring |
| WO2017071988A1 (en) * | 2015-10-28 | 2017-05-04 | Koninklijke Philips N.V. | Monitoring activities of daily living of a person |
| CN106683338A (en) * | 2016-12-30 | 2017-05-17 | 肇庆高新区黑阳科技有限公司 | Electronic monitor with sensor and control method of electronic monitor |
| CN106788546A (en) * | 2017-01-23 | 2017-05-31 | 安徽大尺度网络传媒有限公司 | One kind endowment service system |
| CN107092806A (en) * | 2017-05-26 | 2017-08-25 | 北京居家云舍科技服务有限公司 | It is a kind of towards the intelligentized information fusion of old man's household and method for early warning |
| CN107145878A (en) * | 2017-06-01 | 2017-09-08 | 重庆邮电大学 | Old man's anomaly detection method based on deep learning |
| CN108013868A (en) * | 2017-10-24 | 2018-05-11 | 武汉米风通信技术有限公司 | A kind of community monitor system based on the enhancing of Big Dipper ground and Internet of Things fusion |
| WO2018121792A1 (en) * | 2016-12-30 | 2018-07-05 | 梁泰然 | Electronic monitor and control method thereof |
| CN108378833A (en) * | 2018-03-16 | 2018-08-10 | 周国富 | A kind of old man's monitor system based on smart home |
| US20180285496A1 (en) * | 2017-03-30 | 2018-10-04 | The Connected Grip, Inc. | Activities data modeling in human internet of things platforms |
| CN109119172A (en) * | 2018-07-12 | 2019-01-01 | 中原工学院 | A kind of human body behavioral value method based on ant colony algorithm |
| CN109326081A (en) * | 2018-11-09 | 2019-02-12 | 复旦大学 | Old man family nurse early warning system and health Evaluation method based on Internet of Things |
| CN109407504A (en) * | 2018-11-30 | 2019-03-01 | 华南理工大学 | A kind of personal safety detection system and method based on smartwatch |
| CN109686447A (en) * | 2019-01-28 | 2019-04-26 | 远光软件股份有限公司 | A kind of employee status's monitoring system based on artificial intelligence |
| WO2019143318A1 (en) * | 2018-01-16 | 2019-07-25 | Hall David R | Health monitoring system including privacy-ensuring obfuscated camera images |
| CN110415825A (en) * | 2019-08-19 | 2019-11-05 | 杭州思锐信息技术股份有限公司 | A kind of old man's safe condition intelligent evaluation method and system based on machine learning |
| CN110555974A (en) * | 2019-08-28 | 2019-12-10 | 重庆铜鹰商贸有限公司 | Intelligent monitoring system and method for old people |
| WO2020041715A1 (en) * | 2018-08-24 | 2020-02-27 | Vitaltech Properties, Llc | System, method, and smartwatch for fall detection, prediction, and risk assessment |
| US20200100682A1 (en) * | 2018-09-27 | 2020-04-02 | Brain Tunnelgenix Technologies | System and Method for Determining a Sleep Onset, Sleep Arousal, and Sleep Awakening |
| KR102160463B1 (en) * | 2020-05-12 | 2020-09-28 | (주)피스오브퍼즐 | Artificial intelligence-based emergency response system for the elderly |
| CN112183243A (en) * | 2020-09-11 | 2021-01-05 | 杭州市地下管道开发有限公司 | Intelligent community old man monitoring method, system, device and storage medium |
| CN112216065A (en) * | 2020-10-15 | 2021-01-12 | 吕行 | Intelligent nursing system for behavior of old people and identification method |
| CN112464738A (en) * | 2020-11-05 | 2021-03-09 | 江苏集萃未来城市应用技术研究所有限公司 | Improved naive Bayes algorithm user behavior identification method based on mobile phone sensor |
| CN112750278A (en) * | 2021-01-18 | 2021-05-04 | 上海燊义环保科技有限公司 | Full-intelligent network nursing system |
-
2021
- 2021-05-28 CN CN202110588598.0A patent/CN113509156B/en active Active
Patent Citations (34)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1361503A (en) * | 2000-12-29 | 2002-07-31 | 南开大学 | Color multi-objective fusion identifying technology and system based on neural net |
| JP2005278765A (en) * | 2004-03-29 | 2005-10-13 | Sekio Ninomiya | Monitoring device for posture, breathing, heart rate and action, and physical abnormality judging method |
| US20130095459A1 (en) * | 2006-05-12 | 2013-04-18 | Bao Tran | Health monitoring system |
| CN201853320U (en) * | 2010-11-23 | 2011-06-01 | 南通大学 | Monitoring and alarm system for the old |
| US20150099946A1 (en) * | 2013-10-09 | 2015-04-09 | Nedim T. SAHIN | Systems, environment and methods for evaluation and management of autism spectrum disorder using a wearable data collection device |
| WO2016110804A1 (en) * | 2015-01-06 | 2016-07-14 | David Burton | Mobile wearable monitoring systems |
| CN104618464A (en) * | 2015-01-16 | 2015-05-13 | 中国科学院上海微系统与信息技术研究所 | Internet-of-things-based smart home care service system |
| CN104835003A (en) * | 2015-05-04 | 2015-08-12 | 南京邮电大学 | System for mobile old-age services and implement method thereof |
| US20160364549A1 (en) * | 2015-06-15 | 2016-12-15 | Baoguo Wei | System and method for patient behavior and health monitoring |
| WO2017071988A1 (en) * | 2015-10-28 | 2017-05-04 | Koninklijke Philips N.V. | Monitoring activities of daily living of a person |
| CN105278506A (en) * | 2015-11-05 | 2016-01-27 | 沈阳大学 | Smart home system with old-age care function |
| CN205427566U (en) * | 2016-03-24 | 2016-08-03 | 沈阳理工大学 | Long -range basic station monitor system based on internet |
| CN106683338A (en) * | 2016-12-30 | 2017-05-17 | 肇庆高新区黑阳科技有限公司 | Electronic monitor with sensor and control method of electronic monitor |
| WO2018121792A1 (en) * | 2016-12-30 | 2018-07-05 | 梁泰然 | Electronic monitor and control method thereof |
| CN106788546A (en) * | 2017-01-23 | 2017-05-31 | 安徽大尺度网络传媒有限公司 | One kind endowment service system |
| US20180285496A1 (en) * | 2017-03-30 | 2018-10-04 | The Connected Grip, Inc. | Activities data modeling in human internet of things platforms |
| CN107092806A (en) * | 2017-05-26 | 2017-08-25 | 北京居家云舍科技服务有限公司 | It is a kind of towards the intelligentized information fusion of old man's household and method for early warning |
| CN107145878A (en) * | 2017-06-01 | 2017-09-08 | 重庆邮电大学 | Old man's anomaly detection method based on deep learning |
| CN108013868A (en) * | 2017-10-24 | 2018-05-11 | 武汉米风通信技术有限公司 | A kind of community monitor system based on the enhancing of Big Dipper ground and Internet of Things fusion |
| WO2019143318A1 (en) * | 2018-01-16 | 2019-07-25 | Hall David R | Health monitoring system including privacy-ensuring obfuscated camera images |
| CN108378833A (en) * | 2018-03-16 | 2018-08-10 | 周国富 | A kind of old man's monitor system based on smart home |
| CN109119172A (en) * | 2018-07-12 | 2019-01-01 | 中原工学院 | A kind of human body behavioral value method based on ant colony algorithm |
| WO2020041715A1 (en) * | 2018-08-24 | 2020-02-27 | Vitaltech Properties, Llc | System, method, and smartwatch for fall detection, prediction, and risk assessment |
| US20200100682A1 (en) * | 2018-09-27 | 2020-04-02 | Brain Tunnelgenix Technologies | System and Method for Determining a Sleep Onset, Sleep Arousal, and Sleep Awakening |
| CN109326081A (en) * | 2018-11-09 | 2019-02-12 | 复旦大学 | Old man family nurse early warning system and health Evaluation method based on Internet of Things |
| CN109407504A (en) * | 2018-11-30 | 2019-03-01 | 华南理工大学 | A kind of personal safety detection system and method based on smartwatch |
| CN109686447A (en) * | 2019-01-28 | 2019-04-26 | 远光软件股份有限公司 | A kind of employee status's monitoring system based on artificial intelligence |
| CN110415825A (en) * | 2019-08-19 | 2019-11-05 | 杭州思锐信息技术股份有限公司 | A kind of old man's safe condition intelligent evaluation method and system based on machine learning |
| CN110555974A (en) * | 2019-08-28 | 2019-12-10 | 重庆铜鹰商贸有限公司 | Intelligent monitoring system and method for old people |
| KR102160463B1 (en) * | 2020-05-12 | 2020-09-28 | (주)피스오브퍼즐 | Artificial intelligence-based emergency response system for the elderly |
| CN112183243A (en) * | 2020-09-11 | 2021-01-05 | 杭州市地下管道开发有限公司 | Intelligent community old man monitoring method, system, device and storage medium |
| CN112216065A (en) * | 2020-10-15 | 2021-01-12 | 吕行 | Intelligent nursing system for behavior of old people and identification method |
| CN112464738A (en) * | 2020-11-05 | 2021-03-09 | 江苏集萃未来城市应用技术研究所有限公司 | Improved naive Bayes algorithm user behavior identification method based on mobile phone sensor |
| CN112750278A (en) * | 2021-01-18 | 2021-05-04 | 上海燊义环保科技有限公司 | Full-intelligent network nursing system |
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