Conclusiones del proyecto

Nowadays a significant increase in the number of older persons can be seen in all countries of the world. This part of society as well as patients with quadriplegia, permanently paralyzed, and/or amputated arms need to use special control systems to control the rehabilitation robots and wheelchairs instead of using traditional electrical wheelchairs, which are controlled by a joystick. The system design and development must take into consideration the available control signal for this kind of patients. The most important control signal can be received from the region of shoulder, neck, and head of the user. Some of the useful signals in this area are the voice, head orientation, Electroencephalogram (EEG), Electromyogram (EMG) and Electrooculogram (EOG).

In this dissertation, a multi-input hybrid control system for rehabilitation application is proposed. The multi-input makes the system more flexible to adapt to the available body signals, which are used as control signals for the rehabilitation and medical applications. The first input is a voice controller unit. It includes voice controllers with two modes of operation to maximize the voice recognition accuracy and reduce the voice recognition errors. The voice controller has a novel implementation using two different voice recognition modules which add an intelligent feature to the system that allows it to identify wrong command recognition and also increases the voice recognition probability. The second input is the head orientation control unit. It uses the user head movements around the x, y, and z-axes to control the wheelchair movement and speed in all directions. This control unit includes two 9 DOF (degree of freedom) orientation modules. Each module has three MEMS sensors (micro-electro-mechanical systems), which accelerometer, gyroscope, and magnetometer are combined in one module to give proper orientation data in the format of Euler angles. An auto-calibrated algorithm has been embedded into the head tilt controller to calibrate the control of movement and speed when the wheelchair passes through a hill or non-straight roads. Other functions such as command confirmation function, wrong orientation handling function, and orientation sensor error handling functions have been added to the head tilt controller to enhance the performance of the controller and to add more safety to the system users. The low-cost design is taken into consideration as it allows more patients to use this system.

Comparison of Selected Solutions with Previous Work

Based on the available state-of-the-art solutions to control electrical wheelchairs without using a traditional joystick controller, two types of controller are selected in this study for use by quadriplegic patients, which are the voice controller and the head tilt controller. Most previous work has used one or more computer to process and classify the acquired signals. This adds high costs and complexity to the system. In the present work, a powerful ARM microcontroller has been selected to use instead of computers to reduce the system's cost. Most body signals such as EEG, EMG, and EOG are profoundly affected by electrical interference from the user's body and power sources. Special electrodes in contact with the body need to be used to pick up these signals and this makes this approach uncomfortable for the patient. The small electrical values of these signals make their processing and use more complicated as control signals for wheelchairs and rehabilitation systems. The user's voice and head motions can instead be used effectively for this control by quadriplegics and paralyzed patients.

Voice Recognition

Compared to previous research, this work is innovative in the sense that it uses two voice recognition modules built based on two different voice recognition algorithms, dynamic time warping (DTW) and a hidden Markov model (HMM), as a hybrid primary voice controller. This controller can work with speaker dependent (SD) and speaker-independent (SI) modes. The use of these two VR modules in one control unit enhances allow the system to detect the wrong command recognition and improve the VR accuracy by selecting the most accurate recognition results and reducing the VR errors. A microcontroller program has been written to include a false positive (FP) error cancellation function. This works stand-alone, without the need to use a computer to perform data processing and there is no need to fit complex, sensitive electrodes onto the user’s body. The proposed system can operate in any language in speaker-dependent mode, and works with only eight global languages in speaker- independent mode.

Orientation Detection

In this work, an auto-calibrated head tilt controller and speed compensation algorithms for wheelchair and rehabilitation robotics are proposed. The system uses orientation data of the Euler angles of head tilt movements as a controller for the intelligent application. The movement of the user's head around the x- and y- axes is translated into motion in the forward, backward, left and right directions.

The system can adjust its speed depending on the slope of the road. The system uses Euler angles for pitch, roll, and yaw to detect the head's orientation. The Euler angles are picked up using three MEMS sensors which are an accelerometer, gyroscope, and magnetometer. The three sensors are combined to build a highly accurate orientation sensor. The system uses two orientation sensors, the first being fixed to the wheelchair chassis to supply a reference orientation. The second sensor is fixed in a wearable headset on the user’s head to detect head movements and give orientation data to the microcontroller.

Compared to previous work, the proposed head tilt controller uses two control algorithms to enhance its performance. The first is an auto-calibrated algorithm which is used to avoid the effect of road slope in case of the wheelchair climbing a hill or traveling along non-straight roads. It prevents any incorrect command when the head position enters the control threshold area because of the change in head position due to the slope or curvature of the road. It has the flexibility to perform the head tilt control when the system is used in outdoor environments with non-straight roads. The algorithm gives the flexibility to the user to change the speed of the wheelchair in all directions from the minimum to the maximum speed depending on the head tilt angle. The use of Euler angles makes the system more accurate and enhances the sensitivity of the controller.

Outlook

There are several ideas and suggestions to improve the system performance. One suggestion is to add magnetic or optical encoders to the wheelchair motors. This will allow the system to measure the real speed of the wheelchair motors and compare it to the speed given by the control commands. The speed feedback will allow the system to compensate the difference between the intended and measured/real speed. Further, encoders will allow to add a closed loop PID controller to the wheelchair and apply a speed compensation algorithm to compensate the speed changes in case of ascending or descending ramps due to gravity forces. The implementation of speed compensation algorithm with the wheelchair will improve the performance of the head tilt controller via auto adjustment of the required speed for each individual motor in case of ascending and descending ramps and hills.

The communication between the head orientation sensor and the main microcontroller can be changed to wireless by using Bluetooth modules. This will increase the ease and comfortability of wearing and take off the headset.

Adding a robotic arm can be another important improvement for the proposed system especially if is designed for peoples with useless arms. The robotic arm can be fixed on the wheelchair chassis and can be controlled using voice commands. The robotic arm can be used by the user for implementing simple daily tasks such as pick up an object, pressing buttons (electrical switches for light or elevator buttons) and so on.

The performance of the voice controller can be further improved by adding a new VR module. The new module will increase the probability of voice command recognition and decrease the chance of FP errors to ≈ zero.

Through the technical test of the system, we find that the user weight is affecting the system speed inversely so that when the weight increased the speed decreased. We suggest adding a weight sensor array to measure the user weight and then calibrate the speed ratio of the system motors to compensate the speed depending on the measured user weight.

Another suggestion is to add an obstacle detection unit. It is a necessary unit for systems to be used by paralyzed patients. This unit will improve the safety of the system and protect the user in cases of any problems or errors which affect the system. There are several types of obstacle detection and avoidance approaches that can be adopted in the system. The present study does not focus on this unit as it is already available. The selection of the type of obstacle detection unit is related to the processor unit used in the system. The use of cameras and vision sensors for obstacle detection means that a high-speed processor is required and can be implemented using computers or tablets. Ultrasound sensors can be effectively used with the microcontroller unit and it is necessary to adopt more than one sensor in the system to cover all directions due to the sensor coverage area. The ultrasound sensor can be used to detect an obstacle from 2 to 400 cm distance and it can check if there are pitfalls in the wheelchair's path at the same range [213], [214]. The use of eight ultrasound sensors fixed on the front, back and corners of the wheelchair can cover all the directions that the wheelchair may take. Each sensor can be adjusted by the host processor to detect a specific distance based on the design requirements.