Sensor-based activity recognition for hand-held power tools at the construction site

Nowadays, the construction sector still lacks transparency in terms of productivity, work progress and tool utilization on the job site. The ongoing digitalization of the construction industry is considered as a great opportunity to overcome these challenges and requires data collection, processing, and analytics in a large-scale. Cameras, audio sensors and kinematic-based sensors erected on construction sites provide data for activity recognition and activity tracking of construction tools and workers. Especially, kinematic-based sensors such as accelerometers, gyroscopes and magnetometers are particularly suitable for use on the construction site. Activity recognition for hand-held power tools such as rotary hammers, on the other hand, is a vastly unexplored field but relevant of research. Sensors directly attached to the tool can enable the recognition of selected tool activities which will increase transparency about tool utilization, construction site productivity, tool user understanding and will provide information for tool development and tool design, among other. This thesis aims to address this research gap by exploring and identifying the potential of sensor-based activity recognition for hand-held power tools. Models for the recognition of different types of tool utilization will be developed and possible deployment scenarios will be evaluated.