Remote rehabilitation is a multidisciplinary cross-disciplinary project combining modern information and communication technology with rehabilitation medicine. It can be defined as: based on the comprehensive use of communication, remote sensing, remote control, computer, information processing and other technologies. Remote rehabilitation medical services.
Foreign countries have different starting points in this aspect. In summary, the remote rehabilitation system is mainly used as a means of communication to eliminate the space barrier between the assistant evaluation experts and the remote disabled people, and how to remotely recover the system itself. Although it has been mentioned as an auxiliary device evaluation and diagnosis system and promotion of the development of rehabilitation medicine, it has not been substantively studied. Domestic products in this area are only reported by the Shenzhen Disabled Persons' Federation's first remote rehabilitation system for the disabled. The system focuses on communication and communication between experts and patients, enabling disabled people to conduct rehabilitation consultations on experts online. Get advice on rehabilitation.
Judging from the current development situation at home and abroad, the research of all parties has great limitations and are in the initial stage. Therefore, research on remote rehabilitation systems is of great significance.
In the remote rehabilitation system, the information acquisition system is its main component. How to effectively control the information collection system at a long distance, the effect of its realization, the speed of the realization, plays a key role in the performance of the whole system. Since the remote rehabilitation information acquisition system is a multivariable, nonlinear time-varying system, it is difficult to establish an accurate mathematical model of the entire synchronous control system. Therefore, it is necessary to use an effective control method - fuzzy control.
2 The composition of remote rehabilitation information collection and control systemThe schematic diagram of the remote rehabilitation information collection control system is shown in Figure 1. The system is an auxiliary camera robot that can accept instructions to observe a patient with a spatial curve as a path. This control system is mainly realized by two functional modules. One is the PC of the site, which receives the control commands of the remote site through the Internet. After the fuzzy control algorithm is processed, it is transmitted to the MCU processing system through the RS 232 serial port to control. The movement of the car, the gimbal, and the camera. In addition, the PC at the site can also process the image information collected from the camera according to the requirements, and then present it to the remote site through the Internet in an appropriate manner for the diagnosis and design of the remote rehabilitation experts and auxiliary design manufacturers. The second is the single-chip computer control system, which is mainly used to control the movement of the trolley, the pan/tilt and the camera so that it can reach the appropriate orientation, so that the remote rehabilitation experts can observe the physical condition of the patient in real time without being limited by time and space, and carry out remote diagnosis and evaluation. The MCU control system can also process the signals of the sensors that detect the position of the motor, and feed back the situation of the control unit of the fuzzy control system to the remote site. To put it simply, the fuzzy control system mainly realizes the automatic control of the movement of the trolley carrying the information acquisition device, the pan/tilt and the camera that drives the camera, and collects real-time video or image information according to requirements for diagnosis and auxiliary product design.
The input variables of the system are: the steering angle of the trolley to the target, the distance from the trolley to the target, the height of the gimbal from the target, the direction angle and distance of the camera and the target, and a total of 6 input variables. The output variables are: the running speed and direction of the car rudder motor, the running speed and direction of the car driving motor, and the running speed, direction and pan head of the motor that drives the pan/tilt up and down, and a total of 10 output variables. Therefore, the preliminary control object of the information acquisition system has six input variables and ten output variables, and belongs to a fuzzy controller of multiple input and multiple output structures.
The multi-input and multi-output fuzzy control structure is transformed into a single-variable fuzzy controller by fuzzy demodulation. The following is an example to control the speed of the car drive motor to illustrate the establishment of fuzzy control rules.
The trolley drive motor uses a stepper motor whose speed is controlled by changing the pulse frequency of the drive signal. Therefore, the control of the speed of the driving motor of the trolley adopts a univariate two-dimensional fuzzy controller. The input amount is the error e of the trolley to the target distance and the rate of change ec of the trolley to the target distance error, and the output variable is the frequency f of the control pulse. In the specific implementation method of fuzzy control, the fuzzy look-up table method is adopted, and the principle is shown in Figure 2.
The error e and the error change rate ec of each sampling are range-converted, that is, multiplied by the scaling factors k1 and k2, and then quantized, and the input physical signal value is converted into a point on the input domain, which can be controlled by query. The action table gets the output control amount. It is the point on the output domain, and multiplied by the scale factor k3 for range conversion to obtain the required control pulse frequency value f. The control action table is the correspondence between the point-to-output domain on the input domain. It has been a process of fuzzification, fuzzy reasoning and defuzzification, which can be calculated offline. The table look-up method is simple in structure, convenient to implement, low in resource overhead, and fast in online operation.
The basic fuzzy subset of error e, error variation ec, and control amount f is {NB (negative large deviation), NS (negative small deviation), 0 (zero), PS (positive small deviation), PB (positive deviation) . In the system, the domain of the car to the target distance error e is E, the domain of the car to the target distance error rate ec is EC, and the field of the output control quantity f is F. According to the actual situation of the system, the size is quantized into 5 levels, which are {-3, -1, 0, +1, +3 respectively. The membership function curve shown in Figure 3 is selected, and the controller can complete the input. The fuzzification of variables.
The fuzzy input variables are then reasoned and determined by the fuzzy control rules, and the fuzzy output linguistic variables {NB (negative large), NS (negative small), 0 (zero), PS (positive small), PB (positive large)} are obtained. By the same token, the output result inferred by the fuzzy controller must also be transformed into the actual correction amount, and the pulse frequency that controls the speed of the driving motor of the trolley is adjusted to complete the control of the speed of the trolley.
In order to simplify programming and facilitate real-time control, the system tabulates the control rules. The fuzzy controller is controlled according to the control status table shown in Table 1.
Error E, the choice of the quantization factors k1 and k2 of the error rate of change EC has a great influence on the dynamic performance of the control system. K1 determines the response speed of the system. The larger the k1, the faster the response of the system, but the larger the overshoot, the longer the transition time. K2 affects the overshoot of the system. The larger the k2 is selected, the smaller the overshoot of the system will be, but the longer the response time of the system will be. K3 is the total gain of the fuzzy controller. If the selection is too small, the dynamic response process of the system will become longer, and the selection will cause the system to oscillate.
The control rules of other control quantities are similar to those of the above-described trolley drive motor speed.
3.2 Software Design of Information Acquisition Control SystemAt present, there are three techniques for fuzzy controller construction: using traditional single-chip microcomputer or micro-computer as the physical basis, compiling corresponding software to realize fuzzy reasoning and control; constructing fuzzy controller with single-chip microcomputer or integrated circuit chip, using configuration data to determine fuzzy controller The structural form; the fuzzy controller is constructed by using a programmable gate array. Since the remote rehabilitation system site requires a microcomputer to receive remote control commands and process image information from the camera and transmit information through the Internet, in order to fully utilize and save resources, we use the microcomputer as the physical basis and compile the corresponding software implementation. Fuzzy reasoning and control.
The design of the upper computer software of fuzzy control is mainly the design and implementation of the fuzzy control algorithm. It also includes the serial communication part of the microcomputer and the single chip microcomputer and the design and implementation of the interface part with the Internet. The program flow is shown in Figure 4.
This part mainly realizes the fuzzy control function of the information collection system. Before the system runs, the host computer program must first be initialized, set the serial port, and prepare for the correct operation of the system. When the remote control command is transmitted to the PC of the site through the Internet, it is processed by the fuzzy control algorithm, and then the command is sent to the microcontroller control system via the serial port for execution. This control process does not require the personnel of the site to operate, completely remote control, so that the remote expert can easily control the operation of the information collection system according to needs, and also facilitate the local physician or patient family, reducing the remote expert Operational errors caused by communication barriers with local physicians or family members.
4 ConclusionThe system uses the fuzzy control technology to solve the remote intelligent control of the remote rehabilitation information collection system, so that the remote rehabilitation experts and assistant designers can remotely remotely control the local information collection system through the Internet in an appropriate way and in an accurate and real-time manner. Collect data information for diagnostic and auxiliary product design. The test proves that the control system meets our design requirements and can collect 3D visual information in real time and in real time.
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