Controlling the motor direction by tilting of head and using KNN classifier to identify the pattern
- Resource Type
- Conference
- Authors
- Akhil, G.; Satyakumar, I.; Sarath, K.; Rahul, M.; Warrier, Prasanth M.
- Source
- 2017 IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM) Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), 2017 IEEE International Conference on. :281-285 Aug, 2017
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Feature extraction
Tracking
Head
Pattern recognition
Conferences
Bayes methods
- Language
This paper presents a methodology for controlling the direction of motor taking a video sample from a camera as input. To control the direction the subject has to move his head in a direction which he would want the motor to rotate. The main challenge would be classifying the test sequence which has the data of the activity performed by the subject The actions are recognized in the frontal view by tracking the centroid of the head over consecutive frames in a video. Input sequence is matched against the sample points and the action is labeled by using the neighborhood. Once the class is labeled control signals can be sent to the motor to rotate it in the desired direction. In the present implementation we tried to classify the bending right and left directions since the motor has only two degrees of freedom. The paper uses a basic K-Nearest Neighbor(KNN) classifier to classify the actions. This methodology can find an application where an individual without arms can control the direction of a wheel chair using a camera interface. It discusses the reason for preferring the KNN classifier which has more error probability than Bayesian frame work for the classification of activity of a person in a given video sample.