Human–robot mechanics model using hip torque identification with a dual-arm nursing-care transfer robot

Abstract

In response to the growing demand for robotic interventions to mitigate the profound physical strain experienced by caregivers during transfers, dual-arm robotic systems have emerged as a focal point of interest in the caregiving community due to their adaptability and versatility. However, the accuracy of existing human–robot mechanics model is insufficient, impacting the execution of transfer tasks. To enhance the model’s precision, an improved model is proposed, integrating hip torque identification. This proposed methodology involves the simplification of the human structure based on the kinematic attributes associated with the embrace of individuals by robotic arms and the subsequent computation of its inertial parameters. Accordingly, the application of D’Alembert’s principle is employed to analyze the impact of embracing motion, human posture, and the position of human–robot contact on the forces exerted on the human. This culminates in the establishment of a model. Considering the static indeterminacy predicament inherent in the model, a biomechanical data set is curated for the dual-arm transfer scenario. Leveraging this data set, a deep neural network utilizing multilayer perceptron is trained to accurately identify hip torque, thereby improving the model. Accordingly, a robotic transfer platform featuring dual arms is developed and trailed on six subjects with varying anatomical profiles. The results show that the constructed model has high accuracy. This study provides critical mechanics insights for dual-arm transfer tasks, offering potential application value in the field of nursing and rehabilitation.