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since the user sees the robot mimicking his action and thereby easy to guide the robot to
perform assistive tasks.
The mapping technique proposed enabled realistic and easier way of controlling the
robot the arm to perform the task. Also variation of sampling the Wii remote data and modifying
the control routine for controlling the joint angles of the robot arm that enabled near real-time
interaction and decreased the response time for the robot interaction for the user.
8.1.2. Control of LABO-3 Mobile robot using Neuro Headset
In this part of thesis we are able to extract the user thoughts and expressions with a
neural acquisition system. Facial expression like smiling, blinking, looking left and right,
eyebrow movement, furrow etc. was detected. A mapping scheme to map these expressions to
control the Neptune mobile manipulator was proposed and the results were shown. This type of
mapping scheme which uses the BCI technology helps patients with disabilities to command the
robot and interact with to assist them with daily tasks.
8.1.3. Force feedback interaction
Robot assistive system for holding the iPad for the children with CP was proposed. The
user was able to set the iPad screen for playing rehabilitation games on the iPad, and
interactive screen adjustment was achieved with the help of several force sensors mounted on
the robotic arm system.
8.1.4. Gesture recognition system
Gestures were recognized through Wii remote and Neuro headset. Gesture recognition
system was developed with the help of a neural network and cross correlation. Several gestures
like making a circle and line was recognized also the user can make a custom gesture to be
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