CN111887866B - Cushion type real-time hyperactivity monitoring system and method - Google Patents
Cushion type real-time hyperactivity monitoring system and method Download PDFInfo
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- CN111887866B CN111887866B CN202010530071.8A CN202010530071A CN111887866B CN 111887866 B CN111887866 B CN 111887866B CN 202010530071 A CN202010530071 A CN 202010530071A CN 111887866 B CN111887866 B CN 111887866B
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/168—Evaluating attention deficit, hyperactivity
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
- A61B5/6892—Mats
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L1/00—Measuring force or stress, in general
Abstract
The invention discloses a cushion type real-time hyperactivity monitoring system and a cushion type real-time hyperactivity monitoring method, wherein the cushion type real-time hyperactivity monitoring system comprises a cushion, front-end monitoring equipment, a server and a display terminal; the front-end monitoring equipment is arranged on the cushion and used for detecting a pressure value, and the front-end monitoring equipment is connected with the server; the server analyzes data according to the acquired pressure value, and is connected with the display terminal; the display terminal is provided with a graphical display interface, can select the monitoring data of any monitoring device to display in real time or display in a playback mode, is provided with a linked list corresponding to the color and the pressure value, and visually displays the pressure value in different colors; the invention has simple structure and convenient use, provides AFF as an index for monitoring the hyperactivity, describes the hyperactivity of the testee in different periods, can detect the limb actions of the user in real time and evaluate the periodic actions of the limb of the user through the limb action signal under the condition of not influencing the daily life of the user, and realizes the function of monitoring the hyperactivity of the testee in real time.
Description
Technical Field
The invention relates to the field of child behavior monitoring, in particular to a cushion type real-time hyperactivity monitoring system and method.
Background
In a campus teaching situation, the multi-movement situation of children and teenagers often affects the learning efficiency and the classroom teaching quality of the children and teenagers. Hyperactivity is not only a behavioral habit of children and adolescents, but may also be a major symptom of some neuropsychiatric diseases in children and adolescents. In clinical practice, many children and adolescents with Attention Deficit Hyperactivity Disorder (ADHD) often exhibit hyperactivity and impulsivity that are not commensurate with age and developmental level, with learning difficulties, conduct impairment, and maladaptation. Currently, observation methods are used for monitoring and evaluating hyperactivity symptoms. I.e., by live or video observation, the hyperactivity of the subject is assessed empirically by a clinician or professional. Obviously, the method is subjective and not strong in applicability, and can not carry out quantifiable accurate measurement on the hyperactivity. In addition, scientific research finds that ADHD children's distraction and hyperactivity may be periodic, so that monitoring for hyperactivity should consider the use of periodic analysis methods at the data analysis terminal.
For example, a chinese patent document discloses a "behavior monitoring and reminding correction system for a child hyperkinetic syndrome patient", which is disclosed in the publication No.: CN106419926A, filing date thereof: 2016, 08/22, comprising one or more front-end portable monitoring devices, a monitoring end, a doctor end and a cloud database; the front-end portable monitoring equipment is arranged on the limbs or trunk of the child patient, is used for monitoring the behavior state of the child patient in real time, recording and storing the behavior state, and is used for reminding the child patient when the behavior amplitude of the child patient is monitored to be larger than a certain preset threshold value; the monitoring end is connected with the front-end portable monitoring equipment in a wireless or wired communication mode and is used for downloading behavior state data of the children patient stored in the front-end portable monitoring equipment, setting working periods of working states and silent states of the front-end portable monitoring equipment and setting a reminding threshold of the front-end portable monitoring equipment; the monitoring end is connected with the cloud database through the Internet and uploads behavior state data of the infant patient downloaded from the front-end portable monitoring equipment in real time; the doctor end is connected with the cloud database through the Internet and is used for looking up behavior state data of the infant patient at different stages at any time and carrying out comprehensive analysis and diagnosis; the doctor end is still connected with guardianship end through Internet, tracks the response of infant to the treatment. The portable monitoring facilities of front end that this applied for sets up can't judge on infant's four limbs or truck whether children's motion is hyperactivity, can not carry out quantifiable accurate measurement to hyperactivity, can't realize children hyperactivity patient's behavior monitoring.
Disclosure of Invention
The invention mainly solves the problems of low applicability and low accuracy of monitoring the attention deficit hyperactivity disorder in the prior art; the cushion type real-time hyperactivity monitoring system and the cushion type real-time hyperactivity monitoring method are simple in structure and convenient to use, and can monitor hyperactivity conditions of users in different periods under the condition that daily life of the users is not influenced.
The technical problem of the invention is mainly solved by the following technical scheme: a cushion type real-time hyperactivity monitoring system comprises a cushion, front-end monitoring equipment, a server and a display terminal; the front-end monitoring equipment is arranged on the cushion and used for detecting a pressure value, and is connected with the server; the server analyzes data according to the acquired pressure value, and is connected with the display terminal; the display terminal is provided with a graphical display interface, can select the monitoring data of any monitoring equipment to display in real time or display in a playback mode, is provided with a linked list corresponding to the color and the pressure value, and visually displays the pressure value in different colors. When the monitoring device is arranged as a front-end monitoring device, the front-end monitoring device is directly connected with the server, when a plurality of front-end monitoring devices are arranged, one more gateway device can be arranged, the plurality of front-end monitoring devices transmit data to the gateway device, the gateway device transmits the data to the server after concentrating the data, the front-end monitoring devices convert sitting posture analog signals of a testee into digital signals and transmit the digital signals to the server, the server analyzes the data, the multi-activity conditions of the testee are periodically analyzed and visually displayed through the display terminal, a clinician can easily judge whether the testee has the hyperactivity or not by observing the visual graph displayed by the display terminal through the periodic pressure change of the testee at different positions on the cushion, the overall structure is simple, the front-end monitoring devices are convenient to install and do not influence the daily life of the user, when detecting children, can install the cushion that is provided with the front end equipment on children's chair of using on class or children eat the chair of usefulness, it is very convenient to children's hyperkinetic syndrome test, and can not cause any influence to children, does not need the enclosed environment or install equipment on children's health, guarantees to carry out the hyperkinetic syndrome test to children in the condition of natural life for the test result is more accurate, and the credibility is higher.
Preferably, the front-end monitoring device comprises a pressure sensing unit, a matrix switch array, a central processing unit, a storage unit, a data transmission unit, a man-machine interaction unit, a power supply battery and a battery management unit; the pressure sensing unit is arranged on the cushion and used for detecting a pressure value, and the pressure sensing unit is connected with the matrix switch array; the matrix switch array is composed of a plurality of paths of analog switches, the first ends of the analog switches are connected with the pressure sensing unit, and the second ends and the control ends of the analog switches are connected with the central processing unit; the central processing unit consists of one or more microprocessors and is used for controlling the on-off of the matrix switch array and reading the pressure value detected by the pressure sensing unit, and the central processing unit is connected with the server through the data transmission unit; the storage unit is connected with the central processing unit; the data transmission unit is connected with the central processing unit; the human-computer interaction unit is connected with the central processing unit; the power supply battery supplies power to the central processing unit and the pressure sensing unit, and is connected with the battery management unit; the battery management unit is connected with the central processing unit.
Preferably, the pressure sensing unit comprises 1024 thin film pressure sensors, and the 1024 thin film pressure sensors form a 32 × 32 sensor matrix. 1024 film pressure sensors are arranged to form a 32 x 32 sensor matrix, so that the sensors can cover each corner of the cushion, when a testee sits on the cushion, the pressure of the film pressure sensors at different positions can be changed due to the change of the sitting posture, and the test result is more accurate.
Preferably, the data transmission unit includes a wired transmission unit and a wireless transmission unit, the wired transmission unit includes USB transmission, and the wireless transmission unit includes WIFI transmission or bluetooth transmission. The pressure digital signals are transmitted to the server through wireless transmission, so that the sensor is more convenient to install, a wire is not required to be connected, and meanwhile, the cost is effectively reduced.
Preferably, the human-computer interaction unit comprises a plurality of keys and a plurality of indicator lights, and the indicator lights comprise an equipment power supply indicator light, an equipment running state indicator light, a network connection state indicator light and a data receiving and transmitting state indicator light. Set up the warning light, the stability that suggestion equipment began to get into operating condition and equipment operating condition, when equipment breaks down, can be better find the position that equipment broke down, make things convenient for equipment maintenance.
A cushion type real-time hyperactivity monitoring method comprises the following steps:
step S1: a 32 x 32 sensor matrix is arranged in a 400 x 400mm size and is installed in the cushion, and the sensitivity of each sensor is set;
step S2: the server receives the pressure value detected by each sensor and performs data analysis, and the analysis result is graphically and visually displayed through the display terminal;
step S3: and judging the hyperactivity according to the graph displayed by the display terminal.
Preferably, in step S2, the method for analyzing server data includes:
step S21: 1024 pressure values acquired by a sensor matrix are acquired, and each pressure value is formed into a time domain signal;
step S22: calculating a standard deviation of each pressure point time domain signal;
step S23: sorting the standard deviations of all the pressure points in the order from small to large, and taking the pressure points with the top 30% of the ranking as target pressure points;
step S24: performing Fourier transform on the time domain signal of each target pressure point to obtain the pressure amplitude distribution of a frequency domain;
step S25: the frequency bands are divided at intervals of 2 hz. Calculating the average power spectral density of all frequency points of each target pressure point on each frequency band as a frequency amplitude index (AFF);
step S26: and displaying the AFF value of each frequency band of each target pressure point in a graphical mode.
Preferably, the method for calculating the standard deviation in step S22 includes:
where Xi represents the collected pressure values, N represents the number of sensors, and μ is the arithmetic mean.
Preferably, in step S25, the frequency amplitude index is calculated by:
where ω denotes frequency, T denotes time, F T And n represents the number of frequency points of the current frequency band as a Fourier transform function.
The invention has the beneficial effects that: (1) the invention provides a hyperactivity monitoring system, which is equipment for detecting the actions of human limbs, has simple structure and convenient use, can acquire limb action signals of a user on the premise that the user keeps sitting by a matrix film pressure sensing component arranged in the equipment, and processes the limb action signals by the limb action signals acquired by the matrix film pressure sensing component; (2) the invention provides the AFF as the index for monitoring the hyperactivity, describes the hyperactivity conditions of the testee in different periods, and can provide more comprehensive and detailed information for monitoring the hyperactivity, thereby realizing the function of monitoring the hyperactivity condition of the testee in real time by detecting the limb actions of the user in real time and evaluating the periodic actions of the limb of the user through the limb action signals under the condition of not influencing the daily life of the user.
Drawings
Fig. 1 is a block diagram of a hyperactivity disorder monitoring system according to a first embodiment.
Fig. 2 is a block diagram of a front-end monitoring apparatus according to a first embodiment.
Fig. 3 is a schematic flow chart of a data analysis method according to the first embodiment.
In the figure, 1 is a front-end monitoring device, 2 is a server, 3 is a display terminal, 4 is a pressure sensing unit, 5 is a matrix switch array, 6 is a central processing unit, 7 is a storage unit, 8 is a data transmission unit, 9 is a human-computer interaction unit, and 10 is a battery management unit.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
The first embodiment is as follows: a cushion type real-time hyperactivity monitoring system is shown in figure 1 and comprises a cushion, a front-end monitoring device 1, a server 2 and a display terminal 3; the front-end monitoring device 1 is arranged on the cushion and used for detecting a pressure value, and the front-end monitoring device 1 is connected with the server 2; the server 2 performs data analysis according to the acquired pressure value, and the server 2 is connected with the display terminal 3; the display terminal 3 is provided with a graphical display interface, can select monitoring data of any monitoring equipment to display in real time or display in a playback mode, is provided with a linked list with colors corresponding to the pressure values, and visually displays the pressure values in different colors.
As shown in fig. 2, the front-end monitoring device 1 includes a pressure sensing unit 4, a matrix switch array 5, a central processing unit 6, a storage unit 7, a data transmission unit 8, a human-computer interaction unit 9, a power supply battery and a battery management unit 10; the pressure sensing unit 4 is arranged on the cushion, the pressure sensing unit 4 is used for detecting a pressure value, and the pressure sensing unit 4 is connected with the matrix switch array 5; the matrix switch array 5 consists of a plurality of paths of analog switches, the first ends of the analog switches are connected with the pressure sensing unit 4, and the second ends and the control ends of the analog switches are connected with the central processing unit 6; the central processing unit 6 consists of one or more microprocessors and is used for controlling the on-off of the matrix switch array 5 and reading the pressure value detected by the pressure sensing unit 4, and the central processing unit 6 is connected with the server 2 through the data transmission unit 8; the storage unit 7 is connected with the central processing unit 6; the data transmission unit 8 is connected with the central processing unit 6; the human-computer interaction unit 9 is connected with the central processing unit 6; the power supply battery supplies power to the central processing unit 6 and the pressure sensing unit 4, and is connected with the battery management unit 10; the battery management unit 10 is connected with the central processing unit 6, the pressure sensing unit 4 comprises 1024 thin film pressure sensors, the 1024 thin film pressure sensors form a 32 x 32 sensor matrix, the data transmission unit 8 comprises a wired transmission unit and a wireless transmission unit, the wired transmission unit comprises USB transmission, and the wireless transmission unit comprises WIFI transmission or Bluetooth transmission.
The human-computer interaction unit 9 comprises a plurality of keys and a plurality of indicator lamps, and the indicator lamps comprise an equipment power supply indicator lamp, an equipment running state indicator lamp, a network connection state indicator lamp and a data receiving and sending state indicator lamp.
A cushion type real-time hyperactivity monitoring method comprises the following steps:
step S1: a 32 x 32 matrix of sensors is arranged in a 400 x 400mm size and mounted within the seat cushion, setting the sensitivity of each sensor.
Step S2: the server 2 receives the pressure value detected by each sensor and performs data analysis, and the analysis result is graphically and visually displayed through the display terminal 3; as shown in fig. 3, the method of data analysis comprises the steps of:
step S21: 1024 pressure values acquired by the sensor matrix are acquired, and each pressure value is formed into a time domain signal.
Step S22: calculating a standard deviation of each pressure point time domain signal; the standard deviation is calculated as follows:
where Xi represents the collected pressure values, N represents the number of sensors, and μ is the arithmetic mean.
Step S23: and sequencing the standard deviations of all the pressure points in the order from small to large, and taking the pressure points ranked in the first 30% as target pressure points.
Step S24: and performing Fourier transform on the time domain signal of each target pressure point to acquire the pressure amplitude distribution of a frequency domain.
Step S25: dividing frequency bands at intervals of 2 Hz, and calculating the average power spectral density of all frequency points of each target pressure point on each frequency band as a frequency amplitude index (AFF); the calculation method of the frequency amplitude index comprises the following steps:
where ω denotes frequency, T denotes time, F T And n represents the number of frequency points of the current frequency band as a Fourier change function.
Step S26: and displaying the AFF value of each frequency band of each target pressure point in a graphical mode.
Step S3: the hyperactivity is determined based on the figure displayed on the display terminal 3.
The second embodiment, a real-time hyperactivity disorder monitoring system of cushion formula, this embodiment compares in embodiment one, the difference lies in, this embodiment sets up a plurality of front end monitoring devices 1, a gateway equipment is increased simultaneously, a plurality of front end monitoring devices 1 are connected with gateway equipment, gateway equipment is connected with server 2, set up a plurality of display areas in display terminal 3's graphical display interface, carry out visual display to the data acquisition of different front end monitoring devices 1, number every front end monitoring device 1, when a plurality of front end monitoring devices 1 are with data transfer to gateway equipment simultaneously, transmit the data to server 2 after corresponding with the serial number of equipment, server 2 will correspond the data of serial number after carrying out analysis processes with data and show through the display interface.
In the specific application, when a child is monitored for hyperkinetic syndrome, the pressure sensing unit 4 is arranged on a cushion of the child in class or at home, the cushion has certain softness, the child can deform when sitting on the cushion, the pressure value detected by the pressure sensing unit 4 is converted into a digital signal and is directly connected with the server 2 through the data transmission unit 8, the pressure value is visually displayed through a data analysis method, the child sitting on the cushion twists within a certain period of time in the real-time monitoring process, the pressure value changes along with the twisting, the visually displayed image on the graphical display interface of the display terminal 3 changes, according to the scientific research result, the attention dispersion and hyperkinetic of the child ADHD are periodic, and a doctor can accurately judge whether the child has hyperkinetic syndrome or not by observing the image change periodicity of the display terminal 3, the severity of the hyperactivity can be further judged according to the size of the period T; when carrying out hyperkinetic syndrome monitoring to a plurality of children, need set up a plurality of front end monitoring facilities 1, install respectively, number every monitoring facility, concentrate the data that monitoring facility detected through the gateway and then give server 2 with concentrated data transfer for data processing's time increases the timeliness of data processing and monitoring.
The invention provides a hyperactivity monitoring system, which is equipment for detecting the actions of human limbs, has simple structure and convenient use, can acquire limb action signals of a user on the premise that the user keeps sitting by a matrix film pressure sensing component arranged in the equipment, and processes the limb action signals by the limb action signals acquired by the matrix film pressure sensing component; the invention provides the AFF as the index for monitoring the hyperactivity, describes the hyperactivity conditions of the testee in different periods, and can provide more comprehensive and detailed information for monitoring the hyperactivity, thereby realizing the function of monitoring the hyperactivity condition of the testee in real time by detecting the limb actions of the user in real time and evaluating the periodic actions of the limb of the user through the limb action signals under the condition of not influencing the daily life of the user.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the spirit of the invention as set forth in the claims.
Claims (5)
1. The cushion type real-time hyperkinetic syndrome monitoring system comprises a cushion and is characterized by also comprising
The system comprises front-end monitoring equipment, a server and a display terminal;
the front-end monitoring equipment is arranged on the cushion and used for detecting a pressure value, and is connected with the server;
the server analyzes data according to the acquired pressure value, and is connected with the display terminal;
the display terminal is provided with a graphical display interface, can select monitoring data of any monitoring device to display in real time or display in a playback mode, is provided with a linked list corresponding to the color and the pressure value, and visually displays the pressure value in different colors;
the server data analysis method comprises the following steps:
step S21: 1024 pressure values acquired by a sensor matrix are acquired, and each pressure value is formed into a time domain signal;
step S22: calculating a standard deviation of each pressure point time domain signal;
step S23: sequencing the standard deviations of all the pressure points in a sequence from small to large, and taking the pressure points which are positioned in the first 30% as target pressure points;
step S24: performing Fourier transform on the time domain signal of each target pressure point to obtain the pressure amplitude distribution of a frequency domain;
step S25: dividing frequency bands at intervals of 2 Hz, and calculating the average power spectral density of all frequency points of each target pressure point on each frequency band as a frequency amplitude index (AFF);
step S26: displaying the AFF value of each frequency band of each target pressure point in a graphical mode;
the method for calculating the standard deviation in step S22 includes:
wherein Xi represents the collected pressure value, N represents the number of sensors, and mu is an arithmetic mean value;
in step S25, the frequency amplitude index is calculated by:
where ω denotes frequency, T denotes time, F T N represents the number of frequency points of the current frequency band as a Fourier change function;
step S3: and judging the hyperactivity according to the graph displayed by the display terminal.
2. The cushion-type real-time hyperactivity disorder monitoring system of claim 1,
the front-end monitoring equipment comprises a pressure sensing unit, a matrix switch array, a central processing unit, a storage unit, a data transmission unit, a man-machine interaction unit, a power supply battery and a battery management unit;
the pressure sensing unit is arranged on the cushion and used for detecting a pressure value, and the pressure sensing unit is connected with the matrix switch array;
the matrix switch array is composed of a plurality of paths of analog switches, the first ends of the analog switches are connected with the pressure sensing unit, and the second ends and the control ends of the analog switches are connected with the central processing unit;
the central processing unit consists of one or more microprocessors and is used for controlling the on-off of the matrix switch array and reading the pressure value detected by the pressure sensing unit, and the central processing unit is connected with the server through the data transmission unit;
the storage unit is connected with the central processing unit;
the data transmission unit is connected with the central processing unit;
the human-computer interaction unit is connected with the central processing unit;
the power supply battery supplies power to the central processing unit and the pressure sensing unit, and is connected with the battery management unit;
the battery management unit is connected with the central processing unit.
3. The cushion-type real-time hyperactivity disorder monitoring system of claim 2,
the pressure sensing unit includes 1024 thin film pressure sensors, and 1024 thin film pressure sensors form a 32 × 32 sensor matrix.
4. The cushion-type real-time hyperactivity disorder monitoring system according to claim 2 or 3,
the data transmission unit comprises a wired transmission unit and a wireless transmission unit, the wired transmission unit comprises USB transmission, and the wireless transmission unit comprises WIFI transmission or Bluetooth transmission.
5. The cushion-type real-time hyperactivity disorder monitoring system according to claim 2 or 3,
the man-machine interaction unit comprises a plurality of keys and a plurality of indicator lamps, and the indicator lamps comprise an equipment power supply indicator lamp, an equipment running state indicator lamp, a network connection state indicator lamp and a data receiving and sending state indicator lamp.
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CN113133583B (en) * | 2021-04-28 | 2022-06-28 | 重庆电子工程职业学院 | Multifunctional workbench for computer software developer |
CN113359541A (en) * | 2021-05-19 | 2021-09-07 | 杭州师范大学 | Multi-sensory-mode continuous attention monitoring system and method |
CN113712558A (en) * | 2021-07-15 | 2021-11-30 | 杭州师范大学 | Ground mat type attention deficit hyperactivity disorder cognitive intervention training system and method |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107945467A (en) * | 2017-12-20 | 2018-04-20 | 中国科学院合肥物质科学研究院 | A kind of Portable sitting monitoring and system for prompting based on buttocks Pressure Distribution |
CN108903466A (en) * | 2018-08-08 | 2018-11-30 | 浙江科技学院 | Students in middle and primary schools classroom sitting posture based on chute type air bag monitors cushion |
Family Cites Families (15)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2003079891A2 (en) * | 2002-03-18 | 2003-10-02 | Sonomedica, Llc | Method and system for generating a likelihood of cardiovascular disease from analyzing cardiovascular sound signals. |
JP2004029674A (en) * | 2002-06-28 | 2004-01-29 | Matsushita Electric Ind Co Ltd | Noise signal encoding device and noise signal decoding device |
US7913569B2 (en) * | 2007-12-11 | 2011-03-29 | Israel Aerospace Industries Ltd. | Magnetostrictive type strain sensing means and methods |
CN100573573C (en) * | 2008-01-22 | 2009-12-23 | 西北工业大学 | A kind of mode generating method based on Hopf oscillator |
ES2397001T3 (en) * | 2008-02-14 | 2013-03-01 | Kingsdown, Inc. | Devices and methods to evaluate a person in a sleeping system |
RU2487325C2 (en) * | 2010-05-26 | 2013-07-10 | Государственное образовательное учреждение высшего профессионального образования "Иркутский государственный университет путей сообщения" (ИрГУПС (ИрИИТ)) | Method to measure stretching forces acting at rail and device for its realisation |
KR102262451B1 (en) * | 2013-03-20 | 2021-06-07 | 감브로 룬디아 아베 | Monitoring of cardiac arrest in a patient connected to an extracorporeal blood processing apparatus |
CN104337518B (en) * | 2014-10-29 | 2017-03-22 | 杭州师范大学 | Preoperative brain functional network positioning method based on resting-state functional magnetic resonance |
US20160302713A1 (en) * | 2015-04-15 | 2016-10-20 | Sync-Think, Inc. | System and Method for Concussion Detection and Quantification |
CN106170139B (en) * | 2016-09-21 | 2019-10-01 | 北京邮电大学 | A kind of frequency spectrum detecting method and system |
CN110198664B (en) * | 2016-11-10 | 2023-04-18 | 奥本大学 | Method and system for evaluating blood vessels |
CN106820720A (en) * | 2017-01-10 | 2017-06-13 | 上海海事大学 | A kind of multisensor sitting posture detects seat and its detection method |
CA3051401A1 (en) * | 2017-01-24 | 2018-08-02 | The Regents Of The University Of California | Accessing spinal network to enable respiratory function |
CN107174262B (en) * | 2017-05-27 | 2021-02-02 | 西南交通大学 | Attention evaluation method and system |
CN110082028A (en) * | 2019-05-31 | 2019-08-02 | 深圳市卓航自动化设备有限公司 | Pressure detection method and pressure-detecting device with ranging protection |
-
2020
- 2020-06-11 CN CN202010530071.8A patent/CN111887866B/en active Active
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107945467A (en) * | 2017-12-20 | 2018-04-20 | 中国科学院合肥物质科学研究院 | A kind of Portable sitting monitoring and system for prompting based on buttocks Pressure Distribution |
CN108903466A (en) * | 2018-08-08 | 2018-11-30 | 浙江科技学院 | Students in middle and primary schools classroom sitting posture based on chute type air bag monitors cushion |
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