EdgeWave 3

Carnegie Mellon University HCI Master’s Studies. July 2008

Machine learning, data collection, data transformation, electronics prototyping, stroke recognition, significance testing, Support Vector Machines

Brief

Connect machine learning and hardware sensors.

What I did

EdgeWave was a series of the my class projects that were about studying the construction, use and application of accelerometer-based microelectronic devices. This project used machine learning techniques to recognize characters written in air by the user using natural handwriting strokes, and studied the effects of data and class value space reduction and algorithm selection on the recognizer’s performance.

Outcome

I found that although support vector machine has extremely high performance in case of consistent character data collected from one subject in a controlled environment, this model trained on clean data collected from one subject does not generalize well to data collected from other subjects in a less controlled setting.