Human activity recognition (HAR) has been actively researched in fields such as healthcare to understand and analyze human behavior in human-robot interaction. However, most studies have struggled to recognize activities like turning and motion transitions, which are often associated with dynamic balance. Therefore, we propose a novel HAR approach using a single sensor to collect and early fuse motion and position data. The aim is to enhance the accuracy of motion classification for daily activities and those that cause imbalance, which have traditionally been difficult to recognize. We constructed a quarantine room environment for data collection and to evaluate the impact of the suggested features on behavior. Five deep learning models were trained and evaluated to identify the optimal model. The collected data was classified and analyzed by the selected model, which demonstrated an average accuracy of 98.96%.

Parallel robots exhibit superior precision to serial robots. They operate with reduced power consumption due to load distribution among individual motors. However, symmetrical parallel robots employing a 1T2R structure encounter challenges with parasitic movements at the end-effector, leading to control complexities and application limitations. This study aimed to downsize the robot while ensuring its operational range by employing origami techniques. Addressing the inherent weakness of origami’s stiffness, various methods of material stacking and designed joints with diverse materials and thicknesses were proposed to meet specific angle requirements for each component. The developed control model was validated through simulations and experiments, effectively minimizing parasitic movements by verifying the robot"s motion.