Activity Recognition for construction site process via real time sensor signals

Sammanfattning

Measuring construction tool activity has a potential to improve tool productivity, reduce down-time and give insights to various construction processes. Today, a lot of data is being recordedfrom a construction site. This research aims to explore the technical feasibility of handheld powertool activity recognition with real-time tri-axial accelerometer data. The present study has threefocus areas: 1) Data collection using real-time accelerometer data from two tools: a combihammerand a screwdriver. 2) Hand-craft time and frequency domain features from the collected data. 3)Develop two classification algorithms, namely decision trees and random forest, with hand craftedfeatures to detect tool usage activities. The hand-crafted features provide an understanding of themechanical properties of the tools. For the combihammer, the activities recognized were hammerdrilling, chiseling and motor stop. The activity recognition accuracy was 79% with a decision treeand 80.8% with a random forest. For the screwdriver, the activities recognized were screwing,unscrewing and motor stop. The activity recognition accuracy was 87.7% for a decision tree and94.5% for a random forest algorithm. Variance from time domain and energy from frequencydomain were detected as the high importance features by both the classification algorithms forboth the tools.