Use of Human Motion Data to Train Wearable Robots - Jung, Jibum; Park, Yoonyong; Lee, Kyungoh; Lee, Howon; Kim, Dongmin (Turkish journal of computer and mathematics education, 2021)
Development of wearable robots is accelerating. Walking robots mimic human behavior and must operate without accidents. Human motion data are needed to train these robots. We developed a system for extracting human motion data and displaying them graphically.We extracted motion data using a Perception Neuron motion capture system and used the Unity engine for the simulation. Several experiments were performed to demonstrate the accuracy of the extracted motion data.Of the various methods used to collect human motion data, markerless motion capture is highly inaccurate, while optical motion capture is very expensive, requiring several high-resolution cameras and a large number of markers. Motion capture using a magnetic field sensor is subject to environmental interference. Therefore, we used an inertial motion capture system. Each movement sequence involved four and was repeated 10 times. The data were stored and standardized. The motions of three individuals were compared to those of a reference person; the similarity exceeded 90% in all cases. Our rehabilitation robot accurately simulated human movements: individually tailored wearable robots could be designed based on our data. Safe and stable robot operation can be verified in advance via simulation. Walking stability can be increased using walking robots trained via machine learning algorithms.