Engineering students Jody Phouthavong, left, and Jimmy Rivas developed a thought-controlled robotic vehicle, working with Professor Christopher Martinez, center. Phouthavong is wearing the headset used to transmit the impulses that control the vehicle.
UNH Engineering Students Build Thought-Controlled Robotic Vehicle
West Haven, Conn., May 9, 2011—Two engineering students at the University of New Haven have built a thought-controlled robotic vehicle to demonstrate the feasibility of a wheelchair to serve patients with severe disabilities.
Jody Phouthavong of Stratford, Conn., and Jimmy Rivas of Carolina, Puerto Rico, undertook the challenge as a senior design project under the guidance of Christopher Martinez, an assistant professor of electrical and computer engineering at UNH.
On May 7 at Rochester Institute of Technology, the students won an award for “most innovative project” in the annual senior design contest sponsored by Fairchild Semiconductor Inc., Harris RF Communications, the RIT Department of Electrical and Microelectronic Engineering, and IEEE Region 1.
The project takes advantage of the recent availability of a relatively inexpensive headset called an electroencephalogrammer, which records electrical activity in several regions of the brain. The students designed a small robotic vehicle that mimics the navigational capacities of a motorized wheelchair, able to move forward and in reverse as well as to pivot in order to change direction. They then wrote a software interface linking the two devices.
The “driver” of the robot dons the headset and through a series of directed exercises “trains” the software to recognize his mental commands. To do so, he thinks of commands, such as “turn left,” “back up” and “stop.” For each command, the headset records the electrical impulse created by the driver’s thought; at the same time, the intended command is entered manually into the software interface. Because individuals tend to have electrical impulses that are consistent and specific to each command, the software can associate a repeated impulse with the intended consequence. After all the commands have been “learned” by the interface and the robot is activated, the software transmits signals directing the robot according to the sequential impulses delivered by the driver’s brain as he thinks new commands.
While the interface software requires substantial refinement before it could yield the kind of subtle navigational control that would be needed by a patient in a motorized wheelchair, the device does demonstrate that a thought-controlled wheelchair is an attainable prospect, according to Martinez.
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