Quality assessment of welding using regression analysis of biomechanical data

Abstract

The widespread integration of wearable devices in various fields has paved the way for numerous novel applications in medicine, rehabilitation, sports, and industry, etc. through the data acquisition, and analysis of hand gestures and gait patterns. This study aims at a comprehensive exploration of a psychomotor skill, i.e., welding using a wearable device to capture hand movements. After the data collection, this study is focused on assessing the welding quality parameters such as symmetry and equal distribution of a welding path. For this, several machine learning and deep learning algorithms were trained in order to obtain the above welding quality parameters using biomechanical data which was captured using four IMU sensors placed on the hand of the worker when performing the welding task. From our results, it is evident that it is possible to use regression analysis to predict the welding quality parameters including equal distribution of a welding path with minimum error.