Two IEEE senior members are working on technologies that could give wheelchair users more independence and a better quality of life. Hung Nguyen has developed a chair that can be steered by brain waves to help people with severe spinal cord injuries. Mahesh Krishnamurthy has designed an environmental-adaptive system to make a robotic wheelchair more aware of its surroundings, which is meant for those who are parapalegic.
Severely disabled individuals currently have limited ways of operating wheelchairs. People who can’t move their bodies send movement signals through a chin stick, and those who can’t move their heads control the wheelchair by blowing air onto a sensor. Realizing that both methods can be exhausting, Nguyen, the dean of engineering and information technology at the University of Technology, Sydney, and his team at the Centre for Health Technologies there came up with what they say is a better hands-free wheelchair, dubbed the Aviator.
The patient thinks about where the Aviator is to go, and then it heads in that direction. The system relies on a brain-computer interface based on electroencephalography (EEG). It translates brain signals into commands to drive the chair.
Two electrodes serve as the thought sensors. They are fastened to the head near the visual cortex and parietal cortex and connected to a sensitive EEG amplifier. The electrodes are worn in a headband, and the amplified signals are sent wirelessly to a laptop, which will be replaced in the next version, Nguyen says, by a microcontroller housed in a box on one side of the chair. A guidance system relying on cameras allows the chair to avoid obstacles and navigate through crowds.
Users steer their wheelchair with four separate thought commands. To move forward, they visualize a Rubik’s cube or a die rolling forward. To turn left, they mentally write a letter. To turn right, they perform a simple arithmetic problem, such as a series of one-digit multiplications. To stop, they close their eyes.
“The chair is easy to control,” Nguyen says. “Users can even control the wheelchair from a distance—for example, signaling the wheelchair to roll to them and then transferring themselves into it.”
Krishnamurthy, a professor of electrical and computer engineering at the Illinois Institute of Technology, in Chicago, was asked to create a low-cost, power-assisted wheelchair that could sense its surroundings and would be simple to operate. A local wheelchair user made the request to the university’s Institute of Design, which came to Krishnamurthy’s research group for technical help.
Krishnamurthy began with a widely used conventional manual chair and added an electric power boost. He then combined a classic motor control method with a novel approach that lets the wheelchair adapt to the driving conditions. It uses motion detection that relies on a pair of infrared sensors to recognize hand and arm movements and a three-axis gyroscope combined with an accelerometer.
Power to each motor depends on commands received from the user combined with feedback collected by the sensors. The drive includes a brushless DC hub motor and a Hall Effect sensor for each wheel, which measures the motor’s speed and acts as a position sensor to drive the motor. In addition, a motion sensor detects uphill and downhill angles as well as left- and right-leaning slopes to increase the left and right motor’s torque. It’s all driven by a 36-volt, 10-ampere-hour lithium-ion battery.
One challenge for a wheelchair user is caused by the moderate changes in a sidewalk’s center-to-street grade. In conventional power-assist chairs, such banking can cause the chair to veer toward the street unless additional force is applied to the street-side wheel. Another problem occurs when a wheelchair going uphill needs to turn left or right. More force must be applied to just one wheel. Such variations require extra muscle effort and could cause the chair to tip over.
Krishnamurthy’s chair combines a motion sensor and gyroscope with the two motor drives. Using an environmental adaptive-control strategy, the system controls the two motors with feedback from the sensor regarding the incline of the pavement. The torque automatically increases as the chair goes uphill. On a banked sidewalk, the system automatically distributes torque between the wheels.
“No matter what the driving circumstances—uphill, downhill, or banking—from the user’s perspective, ideally we want nothing to change,” Krishnamurthy says. “They should be able to push equally on both sides so that one arm does not feel any more tired than the other.”
Even better, he says, there are no buttons to push or levers to adjust. “It’s not meant to be technologically intense,” he explains. “You don’t have to predict too far in advance about when you will have to ramp up or push really hard. The wheelchair senses conditions and, after a small delay, starts applying the right amount of torque to each wheel.”
The other component the team developed relies on a push-and-go strategy. This approach also controls the speed of the two motors with the infrared sensors. These are critical components, responsible for detecting the user’s intended motion and then acting as pulse generators to control the motor’s speed. Much cheaper than torque-and-speed sensors, the system senses arm movements when the user pushes the wheels, triggering the motors. When the arm motion stops, the sensors signal the controller to turn off the motors.
Other input comes from the gyroscope, which reacts to road conditions. The system can distinguish between traveling uphill and hopping over a curb, allowing the user to apply the same amount of “push” to the wheels independent of conditions.
“We are trying to reduce stress on patients’ muscles while at the same time avoiding muscle atrophy,” Krishnamurthy says. “We hope that people who use this chair find it to be a simple yet effective addition to their daily lives, whether it is for a short or long period of time, without having to learn to use it.”
One of the team members tests out the power-assisted wheelchair being built by Krishnamurthy and others at IIT.