1 Introduction
Remote rehabilitation is a multidisciplinary topic combining modern information and communication technology with rehabilitation medicine. It can be defined as remotely delivered rehabilitation services enabled by communication, remote sensing, remote control, computing, and information processing technologies.
Research abroad has taken different starting points. In general, remote rehabilitation systems have mainly been treated as a communication tool to remove spatial barriers between assistive-device evaluators and distant patients. Although some work has mentioned using remote rehabilitation systems themselves as an assistive-device evaluation and diagnostic tool to advance rehabilitation medicine, substantial research on that topic is still limited. In China, the only reported product is a remote rehabilitation system developed by a Shenzhen disability federation, which focused on communication between experts and patients to allow online rehabilitation consultations and advice.
Given the current international situation, research in this area remains at an early stage and has significant limitations. Therefore, studying remote rehabilitation systems remains important.
In a remote rehabilitation system, the information collection subsystem is a major component. Effective remote control of that subsystem, and its responsiveness, directly affect the overall system performance. Because the remote rehabilitation information collection system is a multivariable, nonlinear, time-varying system, it is difficult to derive an exact mathematical model for the entire synchronous control system. This motivates the use of an effective control method: fuzzy control.
2 Architecture of the Remote Rehabilitation Information Collection Control System
The remote rehabilitation information collection control system is essentially an assistive-camera robot that can follow a spatial curve to observe a patient. The control system consists of two main functional modules. The first is an on-site PC that receives remote control commands over the Internet. After processing by a fuzzy control algorithm, the PC sends commands through an RS-232 serial port to a microcontroller-based processing system to control the mobile platform, pan-tilt unit, and camera. The on-site PC can also process images captured by the camera and present them to remote experts and assistive-device designers via the Internet for diagnosis and design.
The second module is the microcontroller control system, which drives the mobile platform, pan-tilt unit, and camera so they can assume suitable orientations. This allows remote rehabilitation experts to observe a patient in real time for diagnosis and assessment without being constrained by distance or time. The microcontroller system can also process sensor signals that detect motor position and other status information, and feedback execution status of the fuzzy control actuators to the remote site. In short, the fuzzy control system automates the mobile platform and the devices that carry and orient the camera, collecting real-time video or images for diagnosis and assistive-product design.
3 Fuzzy Control Design for the Information Collection System
3.1 Fuzzy Control Strategy for the Information Collection System
The system has six input variables: the steering angle of the mobile platform relative to the target, the distance from the mobile platform to the target, the pan-tilt vertical distance to the target, and the camera-to-target direction angle and distance. The outputs are ten control variables: steering motor speed and direction for the mobile platform, drive motor speed and direction for the mobile platform, motor speed and direction for pan-tilt vertical motion, and four pan-tilt direction controls. Thus, the information collection system is initially a multi-input multi-output fuzzy controller.
Using fuzzy decoupling, this multivariable fuzzy structure is transformed into single-variable fuzzy controllers for design. The following example details rule construction for controlling the drive motor speed of the mobile platform.
The mobile platform drive motor is a stepper motor whose speed is controlled by changing the pulse frequency of the drive signals. Therefore, speed control uses a single-variable two-dimensional fuzzy controller with inputs being the distance error e between the platform and the target and the rate of change of that distance error ec. The output is the control pulse frequency f. The implementation adopts a fuzzy lookup-table approach, whose principle is shown in the next figure.
For each sampling instance, the error e and its rate ec are scaled by factors k1 and k2, then quantized into points on the input domain. Consulting the control lookup table yields the output control value as a point on the output domain, which is then scaled by k3 to obtain the required pulse frequency f. The control lookup table maps input-domain points to output-domain points and represents the fuzzyfied, inferred, and defuzzified mapping. The lookup-table method is simple, easy to implement, resource-efficient, and fast at run time.
The basic fuzzy subsets for error e, error rate ec, and control f are {NB (negative big), NS (negative small), 0 (zero), PS (positive small), PB (positive big)}. In the system, the domain of e is E, the domain of ec is EC, and the domain of f is F. Each is quantized into five levels {-3, -1, 0, +1, +3} and uses the membership functions shown in the next figure to perform fuzzification of the input variables.
The fuzzified inputs are processed by fuzzy control rules to infer fuzzy output linguistic variables {NB, NS, 0, PS, PB}. The inferred outputs are then converted to actual corrective values to adjust the drive motor pulse frequency, thereby controlling the platform speed.
To simplify programming and facilitate real-time control, the control rules are tabulated. The fuzzy controller operates according to the control-state table shown in the figure below.
The selection of quantization factors k1 and k2 for error E and error rate EC strongly affects dynamic performance. k1 determines response speed: increasing k1 speeds response but increases overshoot and transition time. k2 primarily affects overshoot: larger k2 reduces overshoot but lengthens response time. k3 is the overall gain of the fuzzy controller: too small a k3 slows dynamics, while too large a k3 can induce oscillation.
Control rules for other control variables are similar to the drive-motor-speed example above.
3.2 Software Design for the Information Collection Control System
There are three techniques to construct fuzzy controllers: implementing fuzzy inference and control in software on a microcontroller or microcomputer; building fuzzy controllers as single-chip or IC-based hardware configured by data; or implementing fuzzy controllers in programmable gate arrays. Because the on-site station requires a PC to receive remote commands, process camera images, and transmit information over the Internet, this project uses the PC as the physical base and implements fuzzy inference and control in software to conserve resources.
The host PC software design focuses on implementing the fuzzy control algorithm and also includes serial communication between the PC and the microcontroller and the Internet interface. The program flow is shown in the following figure.
Before system operation, the host PC program initializes and configures the serial port to prepare for correct operation. When remote control commands arrive at the on-site PC via the Internet, the fuzzy control algorithm processes them and sends commands over the serial port to the microcontroller for execution. This control process does not require on-site personnel intervention and operates entirely under remote control, enabling remote experts to conveniently operate the information collection system and reducing operational errors due to communication barriers between remote experts and local staff or caregivers.
4 Conclusion
This system applies fuzzy control to enable intelligent remote control of a rehabilitation information collection system. Remote rehabilitation experts and assistive-product designers can remotely control the on-site information collection equipment via the Internet to accurately and in real time collect data and images at appropriate angles for diagnosis and product design. Tests show that the control system meets the design requirements and can perform remote real-time 3D visual data collection.
