This thesis aims to enhance analysis of Inertial Measurement Unit (IMU) data in context of professional forestry operations. The experiment involved mounting an IMU on a passive exoskeleton, capturing real-time movement data during forestry activity, and then the data was analysed both with and without machine learning.

As a result, different back angle movement patterns has been identified using graphs on Euler angles which came from IMU sensor. And also AI model had over 60% of accuracy for all movements except for one, indicating a need for refinement, through more participants, better sensor attachment and combining other sensors. The findings from this thesis will guide future research, particularly for ergonomic specialist students and also mechanical students to develop an active exoskeleton. In conclusion, using IMU data with exoskeletons can improve worker safety and reduce ergonomic risks in forestry by informing the user of  their posture.