CN112927474A - Early warning system for old people falling down based on biomechanical monitoring - Google Patents
Early warning system for old people falling down based on biomechanical monitoring Download PDFInfo
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- G—PHYSICS
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- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
- G08B21/0407—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis
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- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
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- G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
- G08B25/10—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using wireless transmission systems
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- G08B7/06—Signalling systems according to more than one of groups G08B3/00 - G08B6/00; Personal calling systems according to more than one of groups G08B3/00 - G08B6/00 using electric transmission, e.g. involving audible and visible signalling through the use of sound and light sources
Abstract
The invention discloses a tumble early warning system for old people based on biomechanical monitoring, which comprises a tumble early warning model building system and a real-time early warning system, wherein the tumble early warning model building system builds a tumble early warning model by acquiring data; the early warning system is wearable wireless monitoring equipment connected with an upper computer, the wireless monitoring equipment at least comprises one or more of surface myoelectricity acquisition equipment, plantar pressure monitoring equipment and video acquisition equipment, the wireless monitoring equipment guides information acquired in real time into the built falling early warning model, and then early warning information of the falling early warning model is transmitted back to the wireless monitoring equipment through signal transmission; according to the early warning system for the old people falling based on biomechanical monitoring, a falling early warning analysis model is established by capturing relevant biomechanical data, and meanwhile, the early warning system is matched with wearable equipment to implement data acquisition and input into the falling early warning analysis model so as to form the early warning system for the old people falling.
Description
Technical Field
The invention relates to the technical field of tumble early warning, in particular to a tumble early warning system for old people based on biomechanical monitoring.
Background
With the rapid development of economy in China, people can obtain due social medical care guarantees, meanwhile, large-area generation of solitary old people also occurs, children cannot take care of the old people in real time, and particularly, the old people cannot find the old people in time when the old people fall down in action under the condition of accidents, so that more serious consequences are caused; with the increase of age, the joint muscles of the human body degenerate, the muscle content is reduced, the joint stability becomes poor, and osteoporosis of bones causes old people to fall down easily when walking and going up and down stairs, the old people are partially bruised and green if the old people are light, and the old people are fractured and even cause paralysis to threaten life; based on the biomechanical analysis of the mechanical change of the lower limbs of a human body when a person stands, walks and exercises, in order to keep the stability of the ankle joint, muscles and ligaments around the ankle joint must form a stable force system, and the stability of the ankle joint is difficult to maintain sometimes to cause a fall due to the decline of the physical function of the old.
Particularly, for people who suffer from osteoarthritis and other diseases of the old, a portable terminal for monitoring the health of the old in real time is developed, intelligent monitoring is urgently connected to the industry of old care, the portable terminal can effectively monitor various body indexes of the old according to specific monitoring requirements, track the movement and the body condition of the old in real time, particularly carry out timely warning notification under emergency conditions, and avoid more serious consequences, and the portable terminal is one of important development contents of the intelligent industry of China.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a tumble early warning system for the old people based on biomechanical monitoring.
In order to achieve the technical purpose and achieve the technical effect, the invention is realized by the following technical scheme:
a tumble early warning system for monitoring old people based on biomechanics comprises a tumble early warning model building system and a real-time early warning system, wherein the tumble early warning model building system builds a tumble early warning model by collecting data, and the data is collected in real time by the wearable real-time early warning system and fed back to the tumble early warning model so as to realize tumble early warning;
the fall early warning model building system comprises a test chamber, a force measuring table is arranged on the ground in the center of the test chamber, an infrared motion capture module is arranged around the three-dimensional space of the force measuring table and used for capturing marker point parameter information carried by a detected target in the space of the force measuring table, the infrared motion capture module is also matched with a synchronous camera and used for capturing video images, a wireless surface myoelectricity acquisition module is worn and detected target in an integrated structure, the force measuring table further comprises a plantar pressure test module, and the modules transmit data to an upper computer in a wireless or wired mode to build a human motion model;
the real-time early warning system is wearable wireless monitoring equipment connected with an upper computer, the wireless monitoring equipment at least comprises one or more of surface myoelectricity acquisition equipment, plantar pressure monitoring equipment and video acquisition equipment, the wireless monitoring equipment guides information acquired in real time into a built falling early warning model, and then early warning information of the falling early warning model is transmitted back to the wireless monitoring equipment through signal transmission.
Furthermore, a power module, a positioning module, a voice module, a storage module and a warning module are integrated on the surface myoelectricity acquisition device, the plantar pressure monitoring device and the video acquisition device;
the surface electromyogram acquisition equipment is characterized in that data acquired by a surface electromyogram signal acquisition circuit are processed by an STM32F411CCU6 and then are uploaded to an upper computer by an NRF24L01 module, a built fall early warning model is led in, and early warning information of the fall early warning model is transmitted back by signal transmission;
the sole pressure monitoring equipment transmits biomechanical information acquired by insoles or socks with pressure sensors to an upper computer, guides the biomechanical information into a built falling early warning model, transmits early warning information of the falling early warning model back through signal transmission, and transmits back early warning after the biomechanical information is analyzed by the upper computer;
the video acquisition equipment acquires video information through the mobile PC and the fixed camera and uploads the video information to the tumble early warning model which is led in and constructed by the upper computer, and then early warning information of the tumble early warning model is returned through signal transmission.
Furthermore, the fall early warning model building system builds an early warning model with surface electromyographic signals, plantar pressure signals and video signals as variables, and uploads one or more surface electromyographic signals, plantar pressure signals and video signals to the built fall early warning model through a real-time updating real-time early warning system, so that early warning is realized.
Further, the upper computer is a computer or a cloud database.
Another objective of the present invention is to provide a tumble warning method for monitoring a tumble warning system for old people based on biomechanics;
the fall early warning method comprises the following steps:
building a fall early warning model: the early warning method comprises the following steps of collecting various information parameters of early warning of falling through a force measuring table, an infrared motion capture module, a synchronous camera, a surface myoelectricity collection module and a plantar pressure test module, constructing an early warning model with surface myoelectricity signals, plantar pressure signals and video signals as variables, uploading one or more surface myoelectricity signals, plantar pressure signals and video signals to the constructed early warning model through a real-time early warning system through real-time updating, and further realizing early warning;
data acquisition of real-time early warning: transmitting the collected surface electromyographic signals, plantar pressure signals and video signals to an early warning model of an upper computer for analysis in real time through wireless monitoring equipment of the real-time early warning system;
and (3) tumble early warning prompt: the upper computer transmits the analyzed early warning data back to the positioning module, the voice module and the warning module integrated on the wireless monitoring equipment in real time to carry out early warning, the warning module reminds the upper computer, and the upper computer sends real-time position and information to the upper computer through the voice module and the positioning module to call for help.
The invention has the beneficial effects that:
the old people falling early warning system based on biomechanical monitoring comprises a falling early warning model building system and a real-time early warning system, wherein the falling early warning model building system builds a falling early warning model by collecting data, and the data is collected in real time by the wearable real-time early warning system and fed back to the falling early warning model so as to realize falling early warning;
the early warning system for monitoring the old people falling down based on biomechanics is a wearable wireless monitoring device connected with an upper computer, the wireless monitoring device at least comprises one or more of a surface myoelectricity acquisition device, a plantar pressure monitoring device and a video acquisition device, the wireless monitoring device guides information acquired in real time into a built falling down early warning model, and then the early warning information of the falling down early warning model is transmitted back to the wireless monitoring device through signal transmission;
according to the early warning system for the old people falling based on biomechanical monitoring, a falling early warning analysis model is established by capturing relevant biomechanical data, and meanwhile, the early warning system is matched with wearable equipment to implement data acquisition and input into the falling early warning analysis model so as to form the early warning system for the old people falling.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a structural block diagram of a fall early warning system for monitoring elderly people based on biomechanics according to an embodiment of 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.
As shown in fig. 1
The utility model provides an old person early warning system that tumbles based on biomechanics monitoring, includes tumble early warning model construction system and real-time early warning system, tumble early warning model construction system constructs tumble early warning model through data acquisition to gather data feedback to tumble early warning model and then realize the early warning of tumbleing through wearable real-time early warning system in real time.
The technical scheme of the invention is explained by combining the specific embodiment as follows:
example 1
The invention relates to a tumble early warning system for old people based on biomechanical monitoring, which comprises the following steps:
the fall early warning model building system comprises a test chamber, a force measuring table is arranged on the ground in the center of the test chamber, an infrared motion capture module is arranged around a three-dimensional space of the force measuring table and used for capturing marker point parameter information carried by a detected target in the space of the force measuring table, the infrared motion capture module is also matched with a synchronous camera and used for capturing video images, a wireless surface myoelectricity acquisition module is worn and detected target in an integrated structure, the force measuring table further comprises a plantar pressure test module, and the modules transmit data to an upper computer in a wireless or wired mode to build a human body motion model;
the process is completed in a hospital, and for patients with falling risks, files are built in real time through an upper computer, and corresponding models are acquired and built through the data;
the force measuring platform is a three-dimensional force measuring platform, can quickly record data such as force values, impulse, moments and the like when a human body instantaneously interacts with the ground in motion, comprises a force measuring platform and a subsequent circuit, can measure three components (Fx, Fy and Fz) of the action force of the human body on the table top, the moment (Mz) around a coordinate axis and the positions (ax and ay) of force application points when the human body stands on the platform, and further measures the change of the values;
the principle of force measurement is that data parameters obtained by a force measurement platform are subjected to digital-to-analog conversion through a preamplifier, input into a computer, processed through a signal processing program, and then output and displayed after digital-to-analog conversion and filtering;
the upper end of the three-dimensional force measuring platform is provided with a footpath integrated with a plantar pressure testing module, the plantar pressure testing module is a sensor arranged in a matrix, and the density of the sensor is 4-6/cm2The sole pressure testing module dynamically collects the sole stress and transmits data to an upper computer to be combined with the data collected by the infrared motion capture module;
the infrared motion capture module comprises a plurality of infrared cameras, a synchronous camera and a plurality of marker points which can be pasted on a detected object, the infrared cameras capture three-dimensional coordinates of the marker points on the space in real time, and data are transmitted to the upper computer to calculate space-time parameters and kinematic dynamics parameters in the human motion process through model construction and scaling, so that a human motion track is obtained and a human motion model is constructed;
the pasting positions of the marker points are bilateral shoulder peaks and C of the object to be detected7Spinous process, bilateral anterior superior iliac spines, mid-point of connecting line of posterior superior iliac spines, bilateral greater trochanter, lateral femoral condyle, capitula fibula, lateral malleolus, heel, lateral border of fifth metatarsal, mid-point of lateral greater trochanter and femoral condyle, and capitula fibulaAt the midpoint of the line connecting the lateral malleolus.
The main observation indexes of the infrared motion capture module are as follows:
space-time parameters: comprises pace speed, step length, step width, step frequency, walking cycle time composition ratio and time of each time phase;
kinematic kinetic parameters: the motion angle of the lower limb joint, the moment of each joint muscle group and the vertical floor reaction force;
the invention also comprises data acquisition of the wireless surface electromyography acquisition module;
the upper computer collects the static and dynamic parameter information of the tested target obtained by the force measuring platform, the infrared motion capture module, the synchronous camera, the wireless surface myoelectricity acquisition module and the plantar pressure testing module, and establishes a three-dimensional human motion mathematical analysis model by utilizing an artificial neural network, and the process is realized by adopting a BTS three-dimensional motion capture system.
Example 2
The invention relates to a tumble early warning system for old people based on biomechanical monitoring, which comprises the following steps:
the real-time early warning system is wearable wireless monitoring equipment connected with an upper computer, the wireless monitoring equipment at least comprises one or more of surface myoelectricity acquisition equipment, plantar pressure monitoring equipment and video acquisition equipment, the wireless monitoring equipment guides information acquired in real time into a built falling early warning model, and then early warning information of the falling early warning model is transmitted back to the wireless monitoring equipment through signal transmission.
The surface myoelectricity acquisition equipment, the plantar pressure monitoring equipment and the video acquisition equipment are all integrated with a power module, a positioning module, a voice module, a storage module and a warning module;
the surface electromyogram acquisition equipment is characterized in that data acquired by a surface electromyogram signal acquisition circuit are processed by an STM32F411CCU6 and then are uploaded to an upper computer by an NRF24L01 module, a built fall early warning model is led in, and early warning information of the fall early warning model is transmitted back by signal transmission;
the sole pressure monitoring equipment transmits biomechanical information acquired by insoles or socks with pressure sensors to an upper computer, guides the biomechanical information into a built falling early warning model, transmits early warning information of the falling early warning model back through signal transmission, and transmits back early warning after the biomechanical information is analyzed by the upper computer;
the video acquisition equipment acquires video information through the mobile PC and the fixed camera and uploads the video information to the tumble early warning model which is led in and constructed by the upper computer, and then early warning information of the tumble early warning model is returned through signal transmission.
The falling early warning model building system builds an early warning model with surface electromyographic signals, plantar pressure signals and video signals as variables, and uploads one or more surface electromyographic signals, plantar pressure signals and video signals to the built falling early warning model through a real-time early warning system through real-time updating, so that early warning is realized.
The upper computer is a computer or a cloud database.
Example 3
A tumble early warning method for monitoring a tumble early warning system for old people based on biomechanics;
the fall early warning method comprises the following steps:
building a fall early warning model: the early warning method comprises the following steps of collecting various information parameters of early warning of falling through a force measuring table, an infrared motion capture module, a synchronous camera, a surface myoelectricity collection module and a plantar pressure test module, constructing an early warning model with surface myoelectricity signals, plantar pressure signals and video signals as variables, uploading one or more surface myoelectricity signals, plantar pressure signals and video signals to the constructed early warning model through a real-time early warning system through real-time updating, and further realizing early warning;
data acquisition of real-time early warning: transmitting the collected surface electromyographic signals, plantar pressure signals and video signals to an early warning model of an upper computer for analysis in real time through wireless monitoring equipment of the real-time early warning system;
and (3) tumble early warning prompt: the upper computer transmits the analyzed early warning data back to the positioning module, the voice module and the warning module integrated on the wireless monitoring equipment in real time to carry out early warning, the warning module reminds the upper computer, and the upper computer sends real-time position and information to the upper computer through the voice module and the positioning module to call for help.
The specific operation is as follows: when the old people walk or do some activities, the early warning system is started when the system judges that the old people have the risk of falling in advance through the feedback biomechanical information, and the old people are informed of information such as voice/red light flickering/vibration to adjust the posture so as to prevent falling;
the early warning processing plan is started simultaneously, if amazing sole core muscle crowd etc. and maintaining the joint stable, positioning module calls for help with real-time position and information transmission value host computer to cell-phones such as family members are sent to family members with the mode of early warning with old person's particular case simultaneously, play the warning effect, and the old person wears wireless guardianship equipment and family members and can install the APP, can specifically know old person's real-time conditions.
Example 4
The invention relates to a fall early warning method of a fall early warning system for monitoring old people based on biomechanics, which comprises the following specific implementation modes:
building a fall early warning model: the early warning method comprises the steps of constructing an early warning model with surface electromyographic signals, plantar pressure signals and video signals as variables by collecting information parameters of early warning of falling, and uploading one or more surface electromyographic signals, plantar pressure signals and video signals to the constructed early warning model by a real-time early warning system through real-time updating so as to realize early warning;
the fall early warning model is constructed by adopting a BP neural network model, a BP algorithm not only comprises an input layer node and an output layer node, but also comprises one or more hidden layer nodes, for input signals (such as surface electromyogram signals, plantar pressure signals and video signals), the input signals are firstly transmitted to the hidden layer nodes, then output signals of the hidden nodes are transmitted to the output nodes after action functions are carried out, finally output results are given, and the excitation functions of the actions of the nodes are usually S-shaped functions:
by back propagation error function:
continuously adjusting the network weight and the threshold value to make the error function extremely small;
design of model network structure:
n-layer network, input layer: n nodes, inputting n characteristic values (including space-time parameters, kinematic parameters, kinetic parameters, electromyographic parameters and the like); an output layer: 1 node, the output is the posture stability; hidden layer (1 layer):
the hidden layer nodes are referenced as follows:
wherein n is the number of neurons in an input layer, m is the number of neurons in an output layer, and a is a constant between 1 and 10;
the operation process selects a neural network tool box in MATLAB to train the network, and uses a newff function, the transfer functions of the hidden layer and the output layer are 'tandig', 'purelin', the network training function is 'trainrp', and the weight learning function is 'leanndm'.
The established early warning model runs in a cloud database, and an early warning numerical range is obtained according to the output parameters of the output layer and the early warning of the respective independent parameters;
and the collected surface electromyographic signals, plantar pressure signals and video signals are transmitted to an early warning model of an upper computer for analysis through wireless monitoring equipment, a parameter combination mode is established by combining independent parameters with the early warning model, and early warning is carried out when the output value is close to the early warning value range.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (5)
1. The utility model provides an old person early warning system that tumbles based on biomechanics monitoring which characterized in that: the early warning system comprises a falling early warning model building system and a real-time early warning system, wherein the falling early warning model building system builds a falling early warning model by collecting data, and feeds the data back to the falling early warning model by the real-time data collected by the wearable real-time early warning system so as to realize falling early warning;
the fall early warning model building system comprises a test chamber, a force measuring table is arranged on the ground in the center of the test chamber, an infrared motion capture module is arranged around the three-dimensional space of the force measuring table and used for capturing marker point parameter information carried by a detected target in the space of the force measuring table, the infrared motion capture module is also matched with a synchronous camera and used for capturing video images, a wireless surface myoelectricity acquisition module is worn and detected target in an integrated structure, the force measuring table further comprises a plantar pressure test module, and the modules transmit data to an upper computer in a wireless or wired mode to build a human motion model;
the real-time early warning system is wearable wireless monitoring equipment connected with an upper computer, the wireless monitoring equipment at least comprises one or more of surface myoelectricity acquisition equipment, plantar pressure monitoring equipment and video acquisition equipment, the wireless monitoring equipment guides information acquired in real time into a built falling early warning model, and then early warning information of the falling early warning model is transmitted back to the wireless monitoring equipment through signal transmission.
2. The biomechanically based monitoring elderly fall early warning system of claim 1, wherein: the surface myoelectricity acquisition equipment, the plantar pressure monitoring equipment and the video acquisition equipment are all integrated with a power module, a positioning module, a voice module, a storage module and a warning module;
the surface electromyogram acquisition equipment is characterized in that data acquired by a surface electromyogram signal acquisition circuit are processed by an STM32F411CCU6 and then are uploaded to an upper computer by an NRF24L01 module, a built fall early warning model is led in, and early warning information of the fall early warning model is transmitted back by signal transmission;
the sole pressure monitoring equipment transmits biomechanical information acquired by insoles or socks with pressure sensors to an upper computer, guides the biomechanical information into a built falling early warning model, transmits early warning information of the falling early warning model back through signal transmission, and transmits back early warning after the biomechanical information is analyzed by the upper computer;
the video acquisition equipment acquires video information through the mobile PC and the fixed camera and uploads the video information to the tumble early warning model which is led in and constructed by the upper computer, and then early warning information of the tumble early warning model is returned through signal transmission.
3. The biomechanically based monitoring elderly fall early warning system of claim 1, wherein: the falling early warning model building system builds an early warning model with surface electromyographic signals, plantar pressure signals and video signals as variables, and uploads one or more surface electromyographic signals, plantar pressure signals and video signals to the built falling early warning model through a real-time early warning system through real-time updating, so that early warning is realized.
4. The biomechanically based monitoring elderly fall early warning system of claim 1, wherein: the upper computer is a computer or a cloud database.
5. A fall early warning method based on a biomechanical monitoring elderly fall early warning system as claimed in claim 1, wherein:
the fall early warning method comprises the following steps:
building a fall early warning model: the early warning method comprises the following steps of collecting various information parameters of early warning of falling through a force measuring table, an infrared motion capture module, a synchronous camera, a surface myoelectricity collection module and a plantar pressure test module, constructing an early warning model with surface myoelectricity signals, plantar pressure signals and video signals as variables, uploading one or more surface myoelectricity signals, plantar pressure signals and video signals to the constructed early warning model through a real-time early warning system through real-time updating, and further realizing early warning;
data acquisition of real-time early warning: transmitting the collected surface electromyographic signals, plantar pressure signals and video signals to an early warning model of an upper computer for analysis in real time through wireless monitoring equipment of the real-time early warning system;
and (3) tumble early warning prompt: the upper computer transmits the analyzed early warning data back to the positioning module, the voice module and the warning module integrated on the wireless monitoring equipment in real time to carry out early warning, the warning module reminds the upper computer, and the upper computer sends real-time position and information to the upper computer through the voice module and the positioning module to call for help.
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| IT202100019415A1 (en) * | 2021-07-27 | 2023-01-27 | Daniele Cafolla | Wearable assistive multi-sensory modular system |
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