Smart Device Monitoring System Based on Multi-type Inertial Sensor Machine Learning

Abstract

Construction activity recognition can be improved using data fusion from multiple inertial sensors such as accelerometers and gyroscopes, yet the number of accelerometers and gyroscopes and their optimal placement for combination need empirical determination. We considered the optimal combination of these two types of sensors placed on different parts of a construction worker for identifying construction activities through machine learning. The waist, arm, and wrist were equipped with data acquisition units to simultaneously acquire acceleration and angular velocity data for multiple sensor locations. A system for recognizing complex construction activities was developed on the basis of an accelerometer and gyroscope (A+G) synergy at multiple sensor locations. Results show that the A+G combination dataset at the wrist had the best activity recognition among the sensor configurations when the raw data came from a single sensor location. The results of comparing a single sensor location, two sensor locations, and three sensor locations indicate that combination with three sensor locations produced the best accuracy.