Classifying arm movement with embedded system and machine learning

Abstract

This paper presents a method to classify the arm movements into 8 categories for the sensor data acquired from a micro-electro-mechenial sensor. The method uses the attribute weighted KNN (AWKNN) which is a kind of machine learning algorithm. The measurement system consists of gyroscopes and an accelerometer, attached to a human arm to measure arm movement. The system has been implemented with field-programmable gate array. The sensor data are pre-processed and handed over the AWKNN-based machine learning classifier to classify the sensed arm movements into 8 classes. The training data sets have been collected from the group of men and women who had exercised the predefined arm movements. The developed arm movement recognition system has achieved the accuracy higher than 90% in the experiments.