Powered Wheelchair Controller Using Hybrid Bio-Signals

MSc. Dissertation Report

Masterarbeit , 2010 , 59 Seiten , Note: 70

Ingenieurwissenschaften - Robotik

The idea of using a powered wheelchair, for people with mobility limitation and the elderly has been around for quite a while. Most of these wheelchairs require the use of upper limbs to control them. On the contrary, this project aims to help quadriplegic individuals to use their wheelchair with minimum human assistance. It involves the use of Bio-signals mainly EMG EOG and EEG to control the intelligent wheelchair using Artificial Neural Network and Sensor Fusion technology. The setup can also be use for below the neck paralyzed or elderly people with less upper arm strength.

It’s a new approach towards wheelchair control which is non-invasive, discrete and functional. This document gives details of the human-machine interface, the technical equipment, functionality, evaluation and implementation of the system.

Table of Contents

1 Introduction
1.1 History
1.2 Types of wheelchairs
Manual wheelchairs
Electric-powered wheelchairs
Limitation of the electric chairs
Smart or Intelligent wheelchair
1.3 Current research

2 Goals and objective
2.1 Project description
2.2 Hardware
Data acquisition box
Wheelchair
2.3 Software
Software description
Data collection
Pre-processing and data segmentation
Feature extraction
Classification
Algorithm
Training and Testing
Normalizing
Decision process
User interface

3 Evaluation
Component testing
Sub-system testing
System testing

4 Future development
4.1 Challenges faced and recommendations for future work

5 Project plan
Work breakdown structure
Gantt chart
5.1 Conclusion

6 References

Table of Figures

Figure 1: Child’s bed on rollers. From a hydria, lonian made 530B.C [9]

Figure 2: An Intelligent wheelchair [27]

Figure 3: The VAHM2 Prototype [24]

Figure 4: The NavChair [23]

Figure 5: A powered wheelchair with JACO Robotic arm [26]

Figure 6: The Smart Powered Assistance Module (SPAM) for manual Wheelchair [28]

Figure 7: The Home Lift, Position and Rehabilitation (HLPR) chair [29]

Figure 8: Toyota BMI wheelchair [30]

Figure 9: Intelligent Wheelchair control using Computer Vision and Bio-signals [13]

Figure 10: System Block Diagram

Figure 11: The Cyberlink TM headband and data acquisition box

Figure 12: An electrochemical wave called an action potential travels along the axon of a neuron [31]

Figure 13: Picture of the smart wheelchair (RoboChair) for the project [18]

Figure 14: Eleven frequency bands representing EOG, EEG and EMG signals [32]

Figure 15: EEG, EOG and EMG signal displayed using Brainfinger software

Figure 16: Flow chart of software stages to control the wheelchair

Figure 17: Different states in a training sample data

Figure 18: Feature fusion for better movement discrimination

Figure 19: Artificial Neural Network with three input neurons, a hidden layer and four output neurons [20]

Figure 20: A weighted neuron structure with bias [19]

Figure 21: Artificial Neural Network Topology

Figure 22: Mean square error plot of intermediate iterations

Figure 23: Mean square error of the finalized ANN after training

Figure 24: Output of ANN for 20 test samples

Figure 25: User interface dialog box

Figure 26: A simple path for testing the wheelchair [32]

Figure 27: Simulation of wheelchair movement using directional arrows

Figure 28: Work breakdown structure [8]

Nomenclature

HMI: Human Machine Interaction.

HCI: Human Computer Interface.

BCI: Brain Computer Interface.

EEG: Electroencephalography, measure neural activity of the brain.

EMG: Electromyography, measures muscle activity.

EOG: Electrooculography, measures resting potential of the retina.

ANN: Artificial Neural Network.

FANN: Fast Artificial Neural Network.

CMRR: Common Mode Rejection Ratio.

SNR: Signal to noise ratio.

MIPS: Microprocessor without Interlocked Pipeline Stages.

DSP: Digital Signal Processor.

ADC: Analog-to-Digital Convertor.

VoIP: Voice over IP, Internet data service for making telephone calls.

Chapter 1

Introduction

1 Introduction

The current surge and advancement of technology has increased the social demands for the quality of life. This has given rise to the development of consumer conscious gadgets for everyday use, such as the latest mobile phones. These devices have made our life easier, faster, safer and more entertaining with improved user experience. As part of the efforts to improve the quality of life for the disabled and the elderly, robotic researchers have been trying to merge the robotic techniques into systems that can assist them in their daily life. The latest developments in research areas such as computer science, robotics and Artificial Intelligence have broadened the possibility to support disabled or elderly people with new assisting systems [1].

The assistive robotic system has to be safe and reliable to use. For this the user should have certain degree of control over the system to overwrite undesired actions of the machine. This is done by providing users access to system controls and give regular feedback. These types of systems are called the human-in-the-loop control system. These systems need to be tested thoroughly before any commercial production to meet standard requirements [2]. Human Machine interaction (HMI) is fast becoming one of the prominent technologies used for improvising the available resources.

The keyboard and mouse are often used as the Human Computer Interface (HCI) devices. However, it requires more training for the disabled and the elderly to get familiar with a computer. With the advancement of the computer performance, many researchers have tried to use computer vision, voice recognition and similar techniques. However the techniques have some flaws. Voice recognition systems are slow in interpreting the results and are easily affected by noise e.g. during a party. The vision based HCI still has to overcome the detection of the user in real world environments with changing light conditions. Other researchers have proposed to use Bio-signals such as Electromyogram (EMG) [3], Electroencephalogram (EEG) [4], and Electrooculograph (EOG) [5] for HCI. Each of the Bio-signal has its own uniqueness which is used for extracting eminent information. Current researchers are trying to tap these by improving their detection and classification methods with the aid of new technology.

In Bio-signals used, EEG refers to the recording of the brain’s electrical activity measured using multiple electrodes placed on the scalp region. The brain is always active and generates signals of different intensities. Researchers’ are now able to identify different regions of the brain which are activated when we use different sensory organs or think of using them. EEG signal is produced by the triggering of neurons within the brain. They are widely used for diagnostic of epilepsy patients’. For better usability of the signals, we have to take into account the quality of the EEG recorded, user involvement and more accurate ways of signal analysis. EOG signals are mostly obtained by using two electrodes which are placed on the forehead region. When the eyes move towards one of the sides it gets closer to one of electrode and away from the other electrode thus creating a potential difference. The signal is then compared with the resting potential of the retina and the movement of the eye in a particular direction is detected.

EMG refers to the recording of the electrical activity of the skeletal muscles, when they are electrically or neurologically activated. These signals are used to test proper motion of muscle group, controlling bionic parts for the amputees, etc. These signals are strong compared to EEG and easy to notice. EMG signal are often applied to the rehabilitation system, e.g. electric prosthetic hand, because it can be generated by voluntary muscle contraction and it has better properties such as, high amplitude and signal to noise ratio (SNR) than other Bio-signals . EMG signals can be efficiency used as control command with higher accuracy has been concluded in previous research papers [3], [6] and [8].

In this project EMG, EOG and EEG signals generated by eyes and facial muscle movements are recorded using the head band with embedded electrodes. For a paralyzed individual the forehead is the most crucial area to capture useful signals. Also the headband appears less evident when used in public places compared to the electrode cap used in BCI. The signals recorded are further processed to extract unique features. These features are given to the ANN to obtain a decision logic. The logical table is used to maps particular movements with the control commands of the wheelchair. The following sub-parts gives background of wheelchairs used, explain different stages of signal processing, use of ANNs and control logic to manoeuvre the wheelchair in more detail.

1.1 History

The first record of combining wheels to furniture was a Greek vase image of wheeled child’s bed around 530 B.C. A picture of the painting is shown in figure1.The use of wheelchair by people with activity limitation mainly started in the early 1900s. Since then manual wheelchairs have undergone many changes to fit the need of today’s user. Despite disability, wheelchair helps persons to maintain mobility and have a social life. From the manual wheelchair, we have now moved to electric powered wheelchair. The needs of many disabled individuals are satisfied by use of manual or powered wheelchair, but there is a segment of disable individuals who cannot use them for independent mobility. To help these individuals, researchers are using technologies applied in the fields of mobile robots to build intelligent wheelchair with embedded devices and sensors. Nowadays even the traditional wheelchairs are available with improvements such as light weight structure, postural stability, efficiency in propulsion, portability in cars and manoeuvrability [9] [23].

Abbildung in dieser Leseprobe nicht enthalten

Figure 1: Child’s bed on rollers. From a hydria, lonian made 530B.C [9].

1.2 Types of wheelchairs

Manual wheelchairs

Manual wheelchairs are propelled by the occupant by turning the hand-rims or by an attendant using handles. These are mostly used by individuals with upper-body mobility. They are of two main subtypes, rigid and foldable. Rigid chairs are preferred by active users as they have less moving parts and are light in weight. Foldable chairs are easy for storage or placement into a vehicle during travel and are mostly used at airports and hospitals. These wheelchairs can also be fitted with shock absorbers, to cushion bumps on the path [10]. The light weight wheelchairs reduce shoulder and wrist injuries due to strain, decrease the total energy expenditure and are easier for transportation.

Electric-powered wheelchairs

An electric-powered wheelchair uses an electric motor, a joystick or handle bar for navigation and is powered by batteries. Motorized wheelchairs are useful for those who are too weak or otherwise unable to move around themselves in a manual wheelchair. They are also provided to those with cardiovascular conditions. Electric wheelchairs are used to travel a longer distance without physical exhaustion.

Furthermore these wheelchairs are customised to cater individual needs by adding suspension to the front and back wheels, cushioning, light weight frame, Pneumatic tires for softer rolling resistance, etc. There are also sports varieties built for wheelchair athletics, playing tennis, basketball, etc [8].

Limitation of the electric chairs

- Steered in an upright posture, hand strength and upper-body mobility are required.
- Mobility scooters have longer length, which limits their turning radius in smaller lanes.
- It has a low ground clearance that can make it difficult to navigate around poor structured paths.
- They have fewer options for body support, such as head or leg rests.
- They are quite heavy and not portable.
- Need to be charged regularly.

Smart or Intelligent wheelchair

A Smart wheelchair is a motorized chair with an artificial control system designed to assist the user. The artificial control is applied using a computer’s processing power, sensors and applying technology from the fields of robotics. The interface with the system can be made using a joystick, touch sensitive display, a sip-and-puff device, etc. For obstacle detection and avoidance sonar, infrared sensors or Lasers are used. Some wheelchairs are attached with robotic manipulators, usually a robotic arm to grab household things.

Smart wheelchairs are designed for special user requirements. For a user with cerebral palsy the role of the smart wheelchair is to interrupt small muscular signals as high-level commands and execute them. We can also apply different techniques on a smart wheelchair such as face detection, path finder, artificial reasoning or behaviour based control techniques [8].

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Figure 2: An Intelligent wheelchair [27].

Earlier intelligent wheelchairs were developed by adding a seating arrangement to mobile robots. The VAHM is one of the wheelchairs which used modified mobile robot base in its earlier models. It had three control modes, autonomous navigation based on internal maps, wall following and an obstacle avoidance mode. Figure below show the prototype of VAHM2 wheelchair.

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Figure 3: The VAHM2 Prototype [24].

The later models of smart wheelchair were modified commercial powered wheelchair with added functionality. The NavChair provides navigation assistance for powered wheelchair. It uses obstacle avoidance algorithm developed for autonomous robots [25]. Figure 5 shows a powered wheelchair with robotic arm which can grasp a bottle and pickup books or other similar objects.

Abbildung in dieser Leseprobe nicht enthalten

Figure 4: The NavChair [23].

Abbildung in dieser Leseprobe nicht enthalten

Figure 5: A powered wheelchair with JACO Robotic arm [26].

The other variety available is called the “add-on” unit. These units can be easily assembled or detached to any commercial wheelchair. These types of units are valuable for children as they have to change their wheelchair after couple of years as their body grows. The figure below shows the Smart Powered Assistance Module (SPAM) for manual Wheelchair [23].

Abbildung in dieser Leseprobe nicht enthalten

Figure 6: The Smart Powered Assistance Module (SPAM) for manual Wheelchair [28].

[...]

• Hochschule: University of Essex
• Veranstaltung: Embedded Systems - Robotics - Human Machine Interaction
• Note: 70
• Autor: Pankaj Kadam (Autor:in)
• Erscheinungsjahr: 2010
• Seiten: 59
• Katalognummer: V210927
• ISBN (eBook): 9783656387473
• ISBN (Buch): 9783656389545
• Sprache: Englisch
• Schlagworte: powered, wheelchair, controller, using, hybrid, bio-signals, dissertation, report