Machine Learning Based Recognition of Elements in Lower-Limb Movement Sequence for Proactive Control of Exoskeletons to Assist Lifting

Abstract

Exoskeleton robots that provide physical assistance to workers at industrial sites have recently been advanced by utilizing various bio-signal measurement sensors and artificial intelligence. However, their commercialization speed is slow compared to the pace of technological development. One of the reasons for this is the motion mismatch that occurs between the control of the exoskeleton and the worker’s response. To solve this issue, the worker’s intended motion could be predicted, and proactive control of the exoskeleton robot could be implemented. An experiment was conducted with 35 subjects, with the data of 30 subjects used for unsupervised learning and the data of the remaining 5 subjects used for supervised learning. To predict the intended motion of the subjects, the data from IMU sensors were used to segment the motion elements through a k-means clustering algorithm, employing a motion segmentation technique. The lower extremity motion was understood as a sequence composed of motion elements. The potential for predicting motion intention and sequence was demonstrated by comparing the results from unsupervised learning with those of an MLP model that predicted motion sequences from new, unused data. Additionally, it was confirmed that proactive control of the exoskeleton robot using the motion segmentation technique was possible when the duration of the element motion constituting the lower extremity motion sequence exceeded 200ms. Therefore, this study conducted preliminary research to develop a system for predicting a worker’s intended motion and motion sequence for proactive control of an exoskeleton robot.