CN105078449A - Senile dementia monitoring system based on healthy service robot - Google Patents
Senile dementia monitoring system based on healthy service robot Download PDFInfo
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- CN105078449A CN105078449A CN201510523110.0A CN201510523110A CN105078449A CN 105078449 A CN105078449 A CN 105078449A CN 201510523110 A CN201510523110 A CN 201510523110A CN 105078449 A CN105078449 A CN 105078449A
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Abstract
The invention discloses a senile dementia monitoring system based on a healthy service robot. The senile dementia monitoring system based on the healthy service robot comprises the healthy service robot, an intelligent terminal and a cloud server. The healthy service robot comprises a robot body, a main control unit, a human-computer interaction unit and a medical detection unit; the human-computer interaction unit is connected with the main control unit and comprises a tablet computer, and the tablet computer is arranged in front of the chest of the robot body; the medical detection unit is connected with the main control unit and comprises a brain electrical detection device which is independent of the robot body, and the brain electrical detection device is connected with the intelligent terminal and the tablet computer through Bluetooth signals; the intelligent terminal and the tablet computer are connected with the cloud server through the mobile Internet, and data interaction between the intelligent terminal and the tablet computer is achieved through wireless signals. By means of the senile dementia monitoring system based on the healthy service robot, automatic auxiliary diagnosis and treatment of senile dementia can be achieved, the diagnostic accuracy is improved, prevention and early detection of the senile dementia are facilitated, the pathogenetic condition is mitigated from exacerbating, and the purpose of healing is achieved.
Description
Technical field
The present invention relates to a kind of health services system, especially a kind of senile dementia monitor system based on health service robot, belongs to disease surveillance, auxiliary treatment, nursing field.
Background technology
Senile dementia (also known as " Alzheimer ") is the neurodegenerative diseases that a kind of meeting causes many-sided brain cognitive dysfunction such as memory, implementation capacity, visual space, communication, abstract thinking, study and calculating.Senile dementia often occurs in Senectitude and presenium, and risk is multiplied with age, is generally 4%-8%, is increased to 10% after 65 years old over 60 years old prevalence, can more than 30% after 80 years old.In recent years, the quantity of Aged in China Dementia patients is remarkable ascendant trend, and the current patient populations of China about has 6,000,000 according to statistics, occupies first place in the world, and in global patient, about 1/4 in China.According to " anti-senile dementia market intelligence " prediction, along with China's aging population becomes increasingly conspicuous, predict the year two thousand twenty Aged in China Dementia patients and will reach 1,020 ten thousand people, the situation is tense for senile dementia prevention and cure, very urgent.Meanwhile, due to senile dementia incidence of occult, easily obscure mutually with physiological aging, make senile dementia be difficult to early discovery, easily ignored by patient and family members, thus lose best therapic opportunity.
At present, senile dementia is mainly assessed by cognitive-relevant scale of spirit after there is dementia symptom, and make clinical diagnosis in conjunction with inspections such as iconographys, but the patients of senile dementia of diagnosis is substantially all in middle and advanced stage when clinical symptoms is obvious, and senile dementia has irreversibility on pathology, in existing situation, on the one hand, both at home and abroad to senile dementia all without effective Therapeutic Method, existing treatment means mainly adopts Drug therapy, but these medicines can only the moment of PD limited alleviate or the stable state of an illness, the effect of healing can not be reached, and utilize music feedback therapy and training of cognitive function treatment patients of senile dementia to be more beneficial to patient's physical and mental health, utilize biofeedback therapy, by musical therapy to improving patients with Alzheimer disease quality of life, these improvement show as sleep improvement, memory is improved, be emotionally stable, ability to express strengthens, and play noinvasive and have no side effect.In addition, Drug therapy is a link in senile dementia prophylactico-therapeutic measures, also can only be after dementia symptom appears in patient, just implemented, thus this Therapeutic Method is difficult to obtain satisfied curative effect, on the other hand, also not for clinic information system and the assistant diagnosis system of patients of senile dementia in hospital, auxiliary treatment system.Because of domestic neurosurgeon's quantity relative deficiency cause the early diagnosis of patient and daily nursing, auxiliary treatment is greatly affected; objectively result in the difficulty that senile dementia is made a definite diagnosis and the delay of making a definite diagnosis the time; and daily nursing, auxiliary treatment disappearance, namely lack promptness and real-time.Therefore the senile dementia monitor system that the present invention is based on health service robot will overcome the above problems.
The palliative treatment of existing senile dementia nursing need and effective care and nursing combine, in a lot of country, especially China, family attendants it has been generally acknowledged that this is not only a kind of responsibility, and be express a kind of mode of liking and being loyal to emotion, therefore to a great extent, patients of senile dementia all relies on the treatment of home system.Patients of senile dementia caretaker subjects many-sided pressure, they not only will learn disease knowledge, knowledge on drug abuse, also GPRS care tips and mental regulation, but, the survey result display of domestic patients of senile dementia caretaker demands for health education, caretaker understands very few to the health knowledge of disease association, particularly be short of communication skill, lack safety nursing knowledge, lack the method for tempering patient and taking care of oneself, do not understand the knowledge of the aspect such as method of administration and untoward reaction.Very most caretaker not yet recognizes the importance to the daily monitoring of patient and auxiliary treatment, too highly can estimate physiology and the mental function of patient, and ignore patients of senile dementia and be beyond recognition direction due to Cerebral pathology factor, do not understand and simply explain or can not read simple explanation, cause patient's even caretaker self generation depressive emotion.
Summary of the invention
The object of the invention is the defect in order to solve above-mentioned prior art, provide a kind of senile dementia monitor system based on health service robot, this system is easy to use, feature richness, in conjunction with service robot and development of Mobile Internet technology, the automatically auxiliary Diagnosis and Treat of senile dementia can be realized, improve the accuracy of diagnosis, be conducive to prevention and the earlier detection of senile dementia, mitigate the disease increases the weight of, and reaches the object of healing; Difficulty that senile dementia makes a definite diagnosis can be solved in time and in real time and make a definite diagnosis the delay of time, and daily nursing, auxiliary treatment disappearance; More scientific rational guidance can also be made for the nursing of patients of senile dementia, thus alleviate physical pain and the psychological burden of patients of senile dementia, the quality of life of patient is provided.
Object of the present invention can reach by taking following technical scheme:
Based on the senile dementia monitor system of health service robot, comprise health service robot, intelligent terminal and Cloud Server, described health service robot comprises robot body, main control unit, man-machine interaction unit and medical detecting unit; Described man-machine interaction unit is connected with main control unit, and it comprises panel computer, before this panel computer is placed in the breast of robot body; Described medical detecting unit is connected with main control unit, and it comprises the EEG checking device of machine-independent human body, and described EEG checking device is connected with intelligent terminal, panel computer by Bluetooth signal; Described intelligent terminal crosses mobile Internet with dull and stereotyped computer expert and is connected with Cloud Server, realizes data interaction between described intelligent terminal and panel computer by wireless signal; Wherein:
Described EEG checking device, for the brain electric information of Real-time Obtaining patients of senile dementia or Healthy People, and is sent to panel computer and intelligent terminal by brain electric information;
Described panel computer, for receiving the brain electric information of patients of senile dementia or Healthy People, collecting children's voice messaging of patients of senile dementia, and complete patients of senile dementia or Healthy People cognition-Psychological Evaluation, sleep quality assessment and training of cognitive function, and by brain electric information, children's voice messaging, cognition-Psychological Evaluation, sleep quality assessment and training of cognitive function information upload to Cloud Server;
Described intelligent terminal, for receiving the brain electric information of patients of senile dementia or Healthy People and inputting the clinical information of patients of senile dementia or Healthy People, and uploads to Cloud Server by brain electric information and clinical information;
Described Cloud Server, for receiving the information that panel computer and intelligent terminal upload, and carries out date processing, thus completes auxiliary diagnosis, and generate the suggestion of corresponding introduction on discharge, and auxiliary diagnosis result and introduction on discharge suggestion are fed back to intelligent terminal.
Preferably, described health service robot also comprises motion control unit, binocular vision capture unit, environment sensing sensor unit and Power supply unit, described motion control unit, binocular vision capture unit are connected with main control unit respectively with environment sensing sensor unit, and described Power supply unit is used for powering for main control unit, motion control unit, binocular vision capture unit, man-machine interaction unit, environment sensing sensor unit and medical detecting unit.
Preferably, described EEG checking device comprises medicated cap, brain electric transducer, integrated simulation front end, mixed signal microcontroller, bluetooth module, input module, indicating lamp module and power module; Described brain electric transducer is placed in inside medicated cap, contacts with the forehead of patients of senile dementia or Healthy People, and is connected with integrated simulation front end; Described integrated simulation front end is connected with mixed signal microcontroller by SPI; Described bluetooth module is connected with mixed signal microcontroller by UART, and this bluetooth module is used for being connected with external equipment; Described power module is used for for brain electric transducer, integrated simulation front end, mixed signal microcontroller, bluetooth module and indicating lamp module are powered; Described input module and indicating lamp module are connected with mixed signal microcontroller respectively, described input module is the switch of EEG checking device, described indicating lamp module is for showing the connection status of EEG checking device and main control unit, and the brain electro-detection functional status of EEG checking device.
Preferably, described bluetooth module comprises main control module, RF core module, universal peripheral interface module and sensor interface module; The signal that described main control module transmits for receiving, storing mixed signal microcontroller, and when signal demand outwards transmits, signal is imported into RF core module, this main control module comprises master controller, JTAG, ROM, flash memory and the SRAM that wire is connected; Described RF core module is used for when signal demand outwards transmits, receive the signal that main control module imports into, and signal is outwards transmitted by antenna, this RF core module comprises the connected association's controller of wire, digital phase-locked loop, DSP modem, SRAM, ROM and amplifier, and described amplifier connects with antenna; Described universal peripheral interface module comprises the I that wire is connected
2c, UART and SPI; Described sensor interface module comprises sensor controller, ADC and the comparator that wire is connected; Described main control module is connected with sensor interface module with RF core module, universal peripheral interface module respectively by wire.
Preferably, described main control unit comprises central processing unit, universal peripheral interface module, memory module, communication interface modules; Described central processing unit receives the data message from motion control unit, binocular vision capture unit, man-machine interaction unit, environment sensing sensor unit and medical detecting unit by universal peripheral interface module or communication interface modules, data message is stored in memory module after treatment, and described central processing unit is by the working method of communication interface modules controlled motion control unit, binocular vision capture unit, man-machine interaction unit, environment sensing sensor unit and medical detecting unit.
Preferably, described motion control unit comprises motor drive module, light-coupled isolation module, group of motors and speed measuring coder; Isolated by light-coupled isolation module between described motor drive module and main control unit, and drive motors group is rotated; Described speed measuring coder is connected with group of motors, for positional information and the rotary speed information of Real-time Feedback group of motors, realizes the closed loop control of group of motors position and rotating speed; Described group of motors is used for the head rotation of control machine human body, waist rotates, mechanical arm action and bobbin movement.
Preferably, described binocular vision capture unit selects the Kinect somatosensory sensor of Microsoft, for realizing the navigation and localization function of robot, and the planning of optimal path.
Preferably, described environment sensing sensor unit comprises photoswitch, gyro sensor, touch sensor, infrared sensor and ultrasonic sensor; Described photoswitch, touch sensor, infrared sensor, ultrasonic sensor collaborative work, carry out the identification of barrier and hide; Described gyro sensor carries out attitude deciphering to robot body; Described environment sensing sensor unit adopts multi-sensor information fusion technology to process the data that perception is returned, and carries out feedback control by main control unit.
Preferably, described Power supply unit comprises charging base, charge in batteries interface, accumulator, voltage transformation module; Described charge in batteries interface, accumulator and voltage transformation module integration are in robot body, and described charging base is fixed on indoor; At machine man-hour, the voltage transitions that accumulator provides by described voltage transformation module becomes main control unit, motion control unit, binocular vision capture unit, man-machine interaction unit and the voltage required for environment sensing sensor unit, when accumulator electric-quantity is lower than the threshold value set, robot automatically returns to charging base place by environment sensing sensor unit and charges.
Preferably, described panel computer, by collecting children's voice messaging of patients of senile dementia, realizes the simulation affective interaction of patients of senile dementia and children, is specially:
A, panel computer are conversed according to the call routine of patients of senile dementia and children, collect the voice messaging of children;
The voice messaging of children is uploaded to Cloud Server and carries out Storage and Processing by b, panel computer, sets up children's voice messaging data base;
C, panel computer, according to children's voice messaging data base, utilize speech recognition technology to simulate the tone color of speaking of patients of senile dementia children, carry out voice interface with patients of senile dementia.
Preferably, described panel computer completes the sleep quality assessment of patients of senile dementia or Healthy People, is specially:
The method adopting energy feature and least square method supporting vector machine to combine completes sleep mode automatically by stages, then the sleep mode automatically received according to sleep quality assessment software is assessed sleep disorder by stages, also utilizes the sleep quality of multimedization to assess scale simultaneously and assesses sleep quality.
Preferably, described panel computer completes the training of cognitive function of patients of senile dementia or Healthy People, is specially:
Set up the individual training of cognitive function archives of patients of senile dementia or Healthy People and formulate training plan, carry out unit mode management, training content has: memory training and intelligent training, cognitive with figure, that digital arithmetic is cognitive form is trained, be divided into two stages each course for the treatment of, i.e. training and intensive training.
Preferably, described Cloud Server is handled as follows the brain electric information received:
Adopt ICA method to remove irregular eye and move the artefact caused;
Brain electric information after the process of ICA method is excavated: use Correlation Dimension method to carry out nonlinear electroencephalogramsignal signal analysis, portray nervous system complexity; Use the complexity of Lempel-Ziv product complexity theory Different brain region when brain is in difference in functionality state; Brain electricity coherent analysis method is used to carry out the synchronicity analysis of brain electricity.
Preferably, described Cloud Server is handled as follows the children's voice messaging received:
A. the composite character parametric technique adopting linear prediction residue error, Mei Er Frequency Cepstral Coefficients and their behavioral characteristics to constitute jointly extracts phonetic feature;
B. adopt the multiple features mixing innovatory algorithm modeling of feature based and gauss hybrid models, and by combination of multiple features mode, temporal signatures and frequency domain character are combined, short-term stationarity and locally Changing Pattern combine.
Preferably, described Cloud Server carries out learning and data mining based on the degree of depth of large data message to the brain electric information received, children's voice messaging, clinical information, cognition-Psychological Evaluation information, sleep quality appreciation information, training of cognitive function information, as follows:
A, adopt data to divide and rule to carry out basic handling with parallel processing strategy to large data message;
B, employing resolution of tensor carry out the feature selection of large data message: utilize Tucker decomposition method to carry out data decomposition, and utilize FSOM algorithm to carry out feature extraction;
C, semi-supervised learning algorithm is adopted to classify to large data message;
D, employing FCM clustering algorithm carry out cluster to large data message, and use MapReduce model to carry out the MPP of data;
E, employing Apriori algorithm carry out association analysis to large data message
Preferably, described panel computer also has music feedback therapy function, and this music feedback therapy function is used for auxiliary treatment patients of senile dementia, is specially:
According to the different state of an illness and the different psychological characteristics of personality of patient, first automatically different treatment music is selected, secondly the individual feedback process adapting to each patient's different characteristics is set up, the different biofeedback indexs of brain electricity, breathing, Pi Wen are selected, by dynamically observing the change of physiological parameter in patient's training process to judge curative effect according to the different state of an illness.
The present invention has following beneficial effect relative to prior art:
1, senile dementia monitor system of the present invention, the brain electric information of patients of senile dementia or Healthy People can be received by the panel computer on intelligent terminal and health service robot, patients of senile dementia or Healthy People clinical information can be entered as by intelligent terminal, children's voice messaging of patients of senile dementia can be collected by panel computer, and complete the cognition-Psychological Evaluation of patients of senile dementia or Healthy People, sleep quality assessment and training of cognitive function, the information uploaded of cloud server intelligent terminal and panel computer subsequently, utilize the degree of depth study based on large data message and data mining technology, the automatic auxiliary diagnosis of senile dementia can be realized, avoid too relying in existing diagnostic method the diagnostic result that doctor's individual's level and experience cause unilateral and inconsistent, improve the accuracy of diagnosis, the object of auxiliary treatment is realized in conjunction with training of cognitive function, mitigate the disease, improve the quality of life of patients of senile dementia.In addition, based on the senile dementia monitor system of health service robot, there is good man-machine interaction, be convenient to mobile, easy to use, that service time is long advantage.
2, senile dementia monitor system of the present invention, EEG checking device is dressed by making patients of senile dementia or Healthy People, the EEG signals that can realize patients of senile dementia or Healthy People detects in real time, for patients of senile dementia, can the state of an illness present situation of Real-Time Monitoring senile dementia, and predict its development trend; For Healthy People, effectively can prevent senile dementia, find senile dementia as early as possible thus take positive intervention means to slow down as early as possible actual bodily harm that senile dementia brings and mental burden.
3, senile dementia monitor system of the present invention, the panel computer in health service robot completes speech exchange by simulation children voice, effectively can alleviate the feeling of lonely of patients of senile dementia; By assessing the sleep quality of patients of senile dementia or Healthy People, it can be helped to understand the sleep health status of oneself at any time, the change of monitoring human state, take suitable therapeutic scheme in time, reduce the risk that high-risk disease occurs patient, reach the object improving mankind's sleep quality and human health.
4, senile dementia monitor system of the present invention, panel computer in health service robot can carry out music feedback therapy according to the psychological characteristics of personality of patients of senile dementia, and in conjunction with training of cognitive function, auxiliary treatment is carried out to patient, be conducive to patients of senile dementia physical and mental health, mitigate the disease, improve the quality of life of patients of senile dementia, this improvement shows as sleep improvement, and memory is improved, and is emotionally stable, ability to express strengthens, and has the advantages that noinvasive has no side effect.
5, senile dementia monitor system of the present invention, automatically introduction on discharge suggestion can be generated at Cloud Server, together with tele-medicine auxiliary diagnosis result, feed back to intelligent terminal, and be synchronized to the panel computer in health service robot by intelligent terminal, provide scientific and reasonable introduction on discharge suggestion to caretaker, thus control the state of an illness of patients of senile dementia better, also there is the mental regulation for caretaker to advise simultaneously, produce depressed love knot to avoid caretaker.
Accompanying drawing explanation
Fig. 1 is the overall construction drawing of senile dementia monitor system of the present invention.
Fig. 2 is the composition structured flowchart of health service robot of the present invention.
Fig. 3 is the schematic diagram of the function of motion control unit of the present invention.
Fig. 4 is the composition structured flowchart of EEG checking device of the present invention.
Fig. 5 is the composition structured flowchart of bluetooth module in EEG checking device of the present invention.
Fig. 6 is the Cloud Server workflow diagram of senile dementia monitor system of the present invention.
Detailed description of the invention
Embodiment 1:
Below in conjunction with embodiment and accompanying drawing, the present invention is described in further detail, but embodiments of the present invention are not limited thereto.
As shown in Figure 1, the senile dementia monitor system of the present embodiment is applied in certain family, comprises health service robot, intelligent terminal and Cloud Server; Wherein:
As shown in Figure 2, described health service robot comprises robot body, main control unit, motion control unit, binocular vision capture unit, man-machine interaction unit, environment sensing sensor unit, medical detecting unit and Power supply unit; Described main control unit, motion control unit, binocular vision capture unit, man-machine interaction unit and environment sensing sensor unit are arranged on robot body; Described main control unit is connected with medical detecting unit with motion control unit, binocular vision capture unit, man-machine interaction unit, environment sensing sensor unit with serial communication protocol respectively by bus communication protocol; Wherein, motion control unit, binocular vision capture unit, man-machine interaction unit, environment sensing sensor unit and medical detecting unit are the functional unit of top layer.
The underlying operating system (software processes platform) of described robot body adopts the robot operating system (RobotOperatingSystem that increases income, ROS), it comprise that hardware abstraction describes, bsp driver management, the execution of common functions, Message Transmission between program, program distribution package management, distributed process framework and support code storehouse system combined; Under robot operating system of increasing income is mounted in ubuntu (Wu Bantu) system of linux kernel, communicated with main control unit by serial ports, and then the working method of controlled motion control unit, binocular vision capture unit, man-machine interaction unit, environment sensing sensor unit and medical detecting unit.
Described main control unit comprises central processing unit (CPU), universal peripheral interface module, memory module, communication interface modules, described central processing unit is received from motion control unit by universal peripheral interface module or communication interface modules, binocular vision capture unit, man-machine interaction unit, the data message of environment sensing sensor unit and medical detecting unit, integration process is carried out to data, described Data Integration process comprises filtering algorithm, neural network algorithm, FUZZY ALGORITHMS FOR CONTROL, then carry out judgement decision-making and store data in memory module, described central processing unit carries out the transmitting-receiving of instruction by communication interface modules, and then controlled motion control unit, binocular vision capture unit, man-machine interaction unit, the working method of environment sensing sensor unit and medical detecting unit, described communication interface modules comprises I
2c (Inter-IntegratedCircuit), CAN (ControllerAreaNetwork, controller local area network) bus and UART (UniversalAsynchronousReceiver/Transmitter, universal asynchronous receiving-transmitting transmitter) and SPI (SerialPeripheralInterface, Serial Peripheral Interface (SPI)) serial communication modular, to meet the communication interface requirement between different function units, described central processing unit can communicate with operating system, completes the functional requirement of motor control, navigation and localization, man-machine interaction, data communication.
As shown in Figure 3, described motion control unit comprises motor drive module, light-coupled isolation module, group of motors and speed measuring coder; PWM (the PulseWidthModulation that described motor drive module sends for receiving main control unit, pulse width modulation) control signal, drive motors group is rotated, and isolated by light-coupled isolation module between described motor drive module and main control unit, protection main control unit is not by the impact of electric moter voltage fluctuation; Described speed measuring coder is connected with group of motors, for positional information and the rotary speed information of Real-time Feedback group of motors, realizes the closed loop control of group of motors position and rotating speed; Described group of motors can be made up of servomotor, direct current generator, motor, high-torque steering wheel, rotates, mechanical arm action and bobbin movement for the head rotation of control machine human body, waist.
Described binocular vision capture unit selects the Kinect somatosensory sensor of Microsoft, this Kinect somatosensory sensor is used for setting up 3D steric environment by three photographic head, and pass through image identifying and processing, realize the navigation and localization function of robot, and the planning of optimal path, thus improve the decision-making judgement of main control unit.
Described environment sensing sensor unit comprises photoswitch, gyro sensor, touch sensor, infrared sensor and ultrasonic sensor; Described photoswitch, touch sensor, infrared sensor, ultrasonic sensor collaborative work, carry out the identification of barrier and hide; Described gyro sensor carries out attitude deciphering to robot body; Described environment sensing sensor unit adopts multi-sensor information fusion technology to process the data that perception is returned, and carries out feedback control by main control unit.
Described Power supply unit is used for powering for main control unit, motion control unit, binocular vision capture unit, man-machine interaction unit and environment sensing sensor unit, and it comprises charging base, charge in batteries interface, accumulator, voltage transformation module; Described charge in batteries interface, accumulator and voltage transformation module integration are in robot body, and described charging base is fixed on indoor; At machine man-hour, the voltage transitions that accumulator provides by described voltage transformation module becomes main control unit, motion control unit, binocular vision capture unit, man-machine interaction unit and the voltage required for environment sensing sensor unit, when accumulator electric-quantity is lower than the threshold value set, namely, when accumulator electric-quantity is too low, robot automatically returns to charging base place by environment sensing sensor unit and charges.
Described man-machine interaction unit comprises voice interaction module and panel computer, and described voice interaction module comprises voice recognition unit, phonetic synthesis unit, voice alerting unit, and described voice recognition unit is for identifying the phonetic order from user; The speech data that described phonetic synthesis unit is used for identifying processes, and synthesis machine code (can by each functional unit identification of top layer), sends to main control unit to carry out decision-making; Described voice alerting unit can adopt voice prompting device, for receiving the control instruction that main control unit sends over, carrying out voice message to user, realizing the interactive function between user and robot; Described panel computer is placed in the front of robot body, can carry out touch-control display.
Described medical detecting unit comprises the EEG checking device of machine-independent human body, the brain electric information of family dementia of middle-and old aged man patient or Healthy People utilizes EEG checking device to carry out Real-time Obtaining, and described EEG checking device is connected with intelligent terminal, panel computer by Bluetooth signal; Described panel computer is connected with Cloud Server by mobile Internet with intelligent terminal, realizes data interaction between described intelligent terminal and panel computer by wireless signal (data channel can select cellular network/WLAN/bluetooth).
As shown in Figure 4, described EEG checking device is low-power consumption, high accuracy, adopt the wearable device singly leading and detect, it comprises medicated cap (not shown), brain electric transducer, integrated simulation front end, mixed signal microcontroller, bluetooth module, input module (i.e. button), indicating lamp module and power module; Described brain electric transducer is placed in inside medicated cap, contacts with the forehead of patients of senile dementia or Healthy People, and is connected with integrated simulation front end; Described integrated simulation front end is connected with mixed signal microcontroller by SPI; Described bluetooth module is connected with mixed signal microcontroller by UART, and this bluetooth module is used for being connected with external equipment; Described power module is used for for brain electric transducer, integrated simulation front end, mixed signal microcontroller, bluetooth module and indicating lamp module are powered; Described input module and indicating lamp module are connected with mixed signal microcontroller respectively, described input module is the switch of EEG checking device, described indicating lamp module is for showing the connection status of EEG checking device and main control unit, and the brain electro-detection functional status of EEG checking device.
As shown in Figure 5, described bluetooth module adopts low-power consumption bluetooth standard V4.0 equipment, and can ensure high-speed transfer, can solve again the excessive problem of power consumption, it comprises main control module, RF core module, universal peripheral interface module and sensor interface module; The signal that described main control module transmits for receiving, storing mixed signal microcontroller, and when signal demand outwards transmits, signal is imported into RF core module, this main control module comprises master controller, the JTAG (JointTestActionGroup that wire is connected, joint test working group), ROM (Read-OnlyMemory, read only memory), flash memory and SRAM (StaticRandomAccessMemory, static RAM); Described RF core module is used for when signal demand outwards transmits, receive the signal that main control module imports into, and signal is outwards transmitted by antenna, this RF core module comprises the connected association's controller of wire, digital phase-locked loop, DSP modem, SRAM, ROM and amplifier, and described amplifier connects with antenna; Described universal peripheral interface module comprises wire connected I2C, UART and low-power consumption SPI; Described sensor interface module comprises sensor controller, ADC (AnalogtoDigitalConverter, analog-digital converter) and the low power consumption comparator that wire is connected; Described main control module is connected with sensor interface module with RF core module, universal peripheral interface module respectively by wire.
Described panel computer can receive the brain electric information of patients of senile dementia or Healthy People, collect children's voice messaging of patients of senile dementia, and complete on a user interface patients of senile dementia or Healthy People cognition-Psychological Evaluation, sleep quality assessment and training of cognitive function, and by brain electric information, children's voice messaging, cognition-Psychological Evaluation, sleep quality assessment and training of cognitive function information upload to Cloud Server.
Described panel computer, by collecting children's voice messaging of patients of senile dementia, realizes the simulation affective interaction of patients of senile dementia and children, is specially:
1) panel computer is conversed according to the call routine of patients of senile dementia and children, collects the voice messaging of children;
2) voice messaging of children is uploaded to Cloud Server and carries out Storage and Processing by panel computer, sets up children's voice messaging data base;
3) panel computer is according to children's voice messaging data base, speech recognition technology is utilized to simulate the tone color of speaking of patients of senile dementia children, when children fail to carry out affection exchange with patient, voice interface is carried out with patients of senile dementia, the children's tone color such as simulating patients of senile dementia reads aloud story or joke, alleviates the feeling of lonely of patients of senile dementia.
Described panel computer completes the sleep quality assessment of patients of senile dementia or Healthy People, is specially:
The method adopting energy feature and least square method supporting vector machine (LS-SVM) to combine completes sleep mode automatically by stages, then the sleep mode automatically received according to sleep quality assessment software is assessed sleep disorder by stages, also utilizes the sleep quality of multimedization to assess scale simultaneously and assesses sleep quality.
Described panel computer completes the training of cognitive function of patients of senile dementia or Healthy People, is specially:
Set up the individual training of cognitive function archives of patients of senile dementia or Healthy People and formulate training plan, carry out unit mode management, training content has: memory training (in the recent period, at a specified future date) and intelligent training (ability to accept, respond, adaptibility to response), train with forms such as figure are cognitive, digital arithmetic is cognitive, be divided into two stages each course for the treatment of, i.e. training and intensive training.
Described panel computer also has music feedback therapy function, and this music feedback therapy function is used for auxiliary treatment patients of senile dementia, is specially:
According to the different state of an illness and the different psychological characteristics of personality of patient, first automatically different treatment music is selected, secondly the individual feedback process adapting to each patient's different characteristics is set up, the different biofeedback indexs such as brain electricity, breathing, Pi Wen are selected, by dynamically observing the change of physiological parameter in patient's training process to judge curative effect according to the different state of an illness.
Described intelligent terminal can receive the brain electric information of patients of senile dementia or Healthy People, and inputs the clinical information of patients of senile dementia or Healthy People on a user interface, and brain electric information and clinical information are uploaded to Cloud Server; Described clinical information comprises the height of patients of senile dementia or Healthy People, body weight, sex, age, medical history, family history essential information and body temperature, blood oxygen saturation, blood pressure physio-parameter detection and psychological characteristics of personality data; In addition, owing to being data interaction between intelligent terminal and panel computer, the data that panel computer is collected and processed can therefore also be obtained.
Described Cloud Server workflow as shown in Figure 6, the information that cloud server panel computer and intelligent terminal upload, i.e. brain electric information, children's voice messaging, clinical information, cognition-Psychological Evaluation information, sleep quality appreciation information, training of cognitive function information, and process accordingly, described Processig of EEG information, brain electric information comprises sleep cerebral electricity and non-sleep brain electricity, non-sleep brain electricity utilizes ICA algorithm to carry out process in conjunction with electroencephalogram information, set up diagnostic cast, diagnose out patients of senile dementia disease severity, and the method that sleep cerebral electricity adopts energy feature and least square method supporting vector machine (LS-SVM) to combine completes sleep mode automatically by stages, and sleep quality is assessed in conjunction with sleep quality assessment scale by panel computer, described children's speech signal analysis, adopt linear prediction residue error, the composite character parametric technique that Mei Er Frequency Cepstral Coefficients and their behavioral characteristics constitute jointly extracts phonetic feature, and adopt the multiple features mixing innovatory algorithm modeling of feature based and gauss hybrid models, and the combination of multiple features mode by innovating, temporal signatures and frequency domain character are combined, short-term stationarity and local Changing Pattern combine, improve Detection accuracy, effectively process the middle low signal-to-noise ratio situation of panel computer application common noise, then tone color process is carried out, for simulation and children's affective interaction of panel computer, described clinical information process, clinical information comprises physiological and pathological data psychology characteristics of personality, utilizes data mining and degree of depth study, genius morbi is extracted, and set up electronic health record, and set up individual feedback process according to psychological characteristics of personality, carry out music feedback therapy to panel computer.
Described Cloud Server is handled as follows the brain electric information received (non-sleep brain electricity):
Adopt ICA (IndependentComponentAnalysi, independent component analysis) method to remove irregular eye and move the artefact caused;
By deeper analytical method, the brain electric information after the process of ICA method is excavated, to obtain more information, specifically comprises:
1) Correlation Dimension method is used to carry out nonlinear electroencephalogramsignal signal analysis, portray nervous system complexity, the function and the structure that there is its brain of Cognitive Dysfunction Patients all there occurs change, between neuron, connection reduces, cerebral cortex activity reduces, and these performances are reflected by Correlation Dimension;
2) Lempel-Ziv complexity (Lempel-Zivcomplexity is used, referred to as LZC) complexity of algorithm Different brain region when being in difference in functionality state to brain, brain development or function better, its complexity is higher, and the patients that there is cognitive disorder goes out complexity low compared with normal person;
3) brain electricity coherent analysis method is used to carry out the synchronicity analysis of brain electricity, the consistent degree of two signals wave in a certain frequency range can be reflected, indirectly can reflect the contact degree between the cerebral cortex of corresponding site, coherence factor between two of different loci lead is larger, represent that the cortex contact in the site at place of leading is stronger, patients of senile dementia compares coherence between hemisphere with normal aging people have more obvious decline.
Because the data bulk received is huge, described Cloud Server carries out learning and data mining based on the degree of depth of large data message to the brain electric information received, children's voice messaging, clinical information, cognition-Psychological Evaluation information, sleep quality appreciation information, training of cognitive function information, comprises the following steps:
1) adopt data to divide and rule, with parallel processing strategy, basic handling is carried out to large data message;
2) resolution of tensor is adopted to carry out the feature selection of large data message: to utilize MET (Memory-EfficientTuckerDecomposition) this internal memory to use more efficient Tucker decomposition method to carry out data decomposition, and utilize FSOM (FastSelf-organizingMap, quick Self-organizing Maps) algorithm to carry out feature extraction;
3) semi-supervised learning algorithm is adopted to classify to large data message;
4) adopt FCM (Fuzzyc-means, fuzzy c-means) clustering algorithm to carry out cluster to large data message, and use MapReduce model to carry out the MPP of data;
5) Apriori algorithm is adopted to carry out association analysis to large data message.
After described Cloud Server passes through to carry out above-mentioned process to the brain electric information received, children's voice messaging, clinical information, cognition-Psychological Evaluation information, sleep quality appreciation information, training of cognitive function information, automatically auxiliary diagnosis is completed, and generate the suggestion of corresponding introduction on discharge, and in conjunction with tele-medicine auxiliary diagnosis result, then auxiliary diagnosis result and introduction on discharge suggestion are fed back to intelligent terminal, simultaneously owing to being data interaction between intelligent terminal and panel computer, auxiliary diagnosis result and introduction on discharge suggestion can be synchronized to panel computer by intelligent terminal.
Intelligent terminal in above-described embodiment can be smart mobile phone, PDA handheld terminal etc.
In sum, senile dementia monitor system of the present invention can realize the automatically auxiliary Diagnosis and Treat of senile dementia, and improve the accuracy of diagnosis, be conducive to prevention and the earlier detection of senile dementia, mitigate the disease increases the weight of, and reaches the object of healing; Difficulty that senile dementia makes a definite diagnosis can be solved in time and in real time and make a definite diagnosis the delay of time, and daily nursing, auxiliary treatment disappearance; More scientific rational guidance can also be made for the nursing of patients of senile dementia, thus alleviate physical pain and the psychological burden of patients of senile dementia, the quality of life of patient is provided.
The above; be only patent preferred embodiment of the present invention; but the protection domain of patent of the present invention is not limited thereto; anyly be familiar with those skilled in the art in the scope disclosed in patent of the present invention; be equal to according to the technical scheme of patent of the present invention and inventive concept thereof and replace or change, all belonged to the protection domain of patent of the present invention.
Claims (10)
1. based on the senile dementia monitor system of health service robot, it is characterized in that: comprise health service robot, intelligent terminal and Cloud Server, described health service robot comprises robot body, main control unit, man-machine interaction unit and medical detecting unit; Described man-machine interaction unit is connected with main control unit, and it comprises panel computer, before this panel computer is placed in the breast of robot body; Described medical detecting unit is connected with main control unit, and it comprises the EEG checking device of machine-independent human body, and described EEG checking device is connected with intelligent terminal, panel computer by Bluetooth signal; Described intelligent terminal crosses mobile Internet with dull and stereotyped computer expert and is connected with Cloud Server, realizes data interaction between described intelligent terminal and panel computer by wireless signal; Wherein:
Described EEG checking device, for the brain electric information of Real-time Obtaining patients of senile dementia or Healthy People, and is sent to panel computer and intelligent terminal by brain electric information;
Described panel computer, for receiving the brain electric information of patients of senile dementia or Healthy People, collecting children's voice messaging of patients of senile dementia, and complete patients of senile dementia or Healthy People cognition-Psychological Evaluation, sleep quality assessment and training of cognitive function, and by brain electric information, children's voice messaging, cognition-Psychological Evaluation, sleep quality assessment and training of cognitive function information upload to Cloud Server;
Described intelligent terminal, for receiving the brain electric information of patients of senile dementia or Healthy People and inputting the clinical information of patients of senile dementia or Healthy People, and uploads to Cloud Server by brain electric information and clinical information;
Described Cloud Server, for receiving the information that panel computer and intelligent terminal upload, and carries out date processing, thus completes auxiliary diagnosis, and generate the suggestion of corresponding introduction on discharge, and auxiliary diagnosis result and introduction on discharge suggestion are fed back to intelligent terminal.
2. the senile dementia monitor system based on health service robot according to claim 1, it is characterized in that: described health service robot also comprises motion control unit, binocular vision capture unit, environment sensing sensor unit and Power supply unit, described motion control unit, binocular vision capture unit is connected with main control unit respectively with environment sensing sensor unit, described Power supply unit is used for for main control unit, motion control unit, binocular vision capture unit, man-machine interaction unit, environment sensing sensor unit and medical detecting unit are powered.
3. the senile dementia monitor system based on health service robot according to claim 1, is characterized in that: described EEG checking device comprises medicated cap, brain electric transducer, integrated simulation front end, mixed signal microcontroller, bluetooth module, input module, indicating lamp module and power module; Described brain electric transducer is placed in inside medicated cap, contacts with the forehead of patients of senile dementia or Healthy People, and is connected with integrated simulation front end; Described integrated simulation front end is connected with mixed signal microcontroller by SPI; Described bluetooth module is connected with mixed signal microcontroller by UART, and this bluetooth module is used for being connected with external equipment; Described power module is used for for brain electric transducer, integrated simulation front end, mixed signal microcontroller, bluetooth module and indicating lamp module are powered; Described input module and indicating lamp module are connected with mixed signal microcontroller respectively, described input module is the switch of EEG checking device, described indicating lamp module is for showing the connection status of EEG checking device and main control unit, and the brain electro-detection functional status of EEG checking device.
4. the senile dementia monitor system based on health service robot according to claim 1, it is characterized in that: described panel computer is by collecting children's voice messaging of patients of senile dementia, realize the simulation affective interaction of patients of senile dementia and children, be specially:
A, panel computer are conversed according to the call routine of patients of senile dementia and children, collect the voice messaging of children;
The voice messaging of children is uploaded to Cloud Server and carries out Storage and Processing by b, panel computer, sets up children's voice messaging data base;
C, panel computer, according to children's voice messaging data base, utilize speech recognition technology to simulate the tone color of speaking of patients of senile dementia children, carry out voice interface with patients of senile dementia.
5. the senile dementia monitor system based on health service robot according to claim 1, is characterized in that: described panel computer completes the sleep quality assessment of patients of senile dementia or Healthy People, is specially:
The method adopting energy feature and least square method supporting vector machine to combine completes sleep mode automatically by stages, then the sleep mode automatically received according to sleep quality assessment software is assessed sleep disorder by stages, also utilizes the sleep quality of multimedization to assess scale simultaneously and assesses sleep quality.
6. the senile dementia monitor system based on health service robot according to claim 1, is characterized in that: described panel computer completes the training of cognitive function of patients of senile dementia or Healthy People, is specially:
Set up the individual training of cognitive function archives of patients of senile dementia or Healthy People and formulate training plan, carry out unit mode management, training content has: memory training and intelligent training, cognitive with figure, that digital arithmetic is cognitive form is trained, be divided into two stages each course for the treatment of, i.e. training and intensive training.
7. the senile dementia monitor system based on health service robot according to claim 1, is characterized in that: described Cloud Server is handled as follows the brain electric information received:
Adopt ICA method to remove irregular eye and move the artefact caused;
Brain electric information after the process of ICA method is excavated: use Correlation Dimension method to carry out nonlinear electroencephalogramsignal signal analysis, portray nervous system complexity; Use the complexity of Lempel-ziv product complexity theory Different brain region when brain is in difference in functionality state; Brain electricity coherent analysis method is used to carry out the synchronicity analysis of brain electricity.
8. the senile dementia monitor system based on health service robot according to claim 4, is characterized in that: described Cloud Server is handled as follows the children's voice messaging received:
A. the composite character parametric technique adopting linear prediction residue error, Mei Er Frequency Cepstral Coefficients and their behavioral characteristics to constitute jointly extracts phonetic feature;
B. adopt the multiple features mixing innovatory algorithm modeling of feature based and gauss hybrid models, and by combination of multiple features mode, temporal signatures and frequency domain character are combined, short-term stationarity and locally Changing Pattern combine.
9. the senile dementia monitor system based on health service robot according to claim 1, it is characterized in that: described Cloud Server carries out learning and data mining based on the degree of depth of large data message to the brain electric information received, children's voice messaging, clinical information, cognition-Psychological Evaluation information, sleep quality appreciation information, training of cognitive function information, as follows:
A, adopt data to divide and rule to carry out basic handling with parallel processing strategy to large data message;
B, employing resolution of tensor carry out the feature selection of large data message: utilize Tucker decomposition method to carry out data decomposition, and utilize FSOM algorithm to carry out feature extraction;
C, semi-supervised learning algorithm is adopted to classify to large data message;
D, employing FCM clustering algorithm carry out cluster to large data message, and use MapReduce model to carry out the MPP of data;
E, employing Apriori algorithm carry out association analysis to large data message.
10. the senile dementia monitor system based on health service robot according to claim 1, it is characterized in that: described panel computer also has music feedback therapy function, this music feedback therapy function is used for auxiliary treatment patients of senile dementia, is specially:
According to the different state of an illness and the different psychological characteristics of personality of patient, first automatically different treatment music is selected, secondly the individual feedback process adapting to each patient's different characteristics is set up, the different biofeedback indexs of brain electricity, breathing, Pi Wen are selected, by dynamically observing the change of physiological parameter in patient's training process to judge curative effect according to the different state of an illness.
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