Rehabilitation robotics has become a widely accepted method to deal with the training of people with motor dysfunction. In robotics medium training, shoulder repeated exercise training has been proven beneficial for improving motion ability of human limbs. An important and difficult paradigm for motor function rehabilitation training is the movement rhythm on the shoulder, which is not a single joint but complex and ingenious combination of bones, muscles, ligaments, and tendons. The most robots for rehabilitation were designed previously considering simplified biomechanical models only, which led to misalignment between robots and human shoulder. Current biomechanical models were merely developed for rehabilitation robotics design. This paper proposes a new hybrid spatial model based on joint geometry constraints to describe the movement of the shoulder skeletal system and establish the position analysis equation of the model by a homogeneous coordinate transformation matrix and vector method, which can be used to calculate the kinematics of human-robot integrated system. The shoulder rhythm, the most remarkable particularity in shoulder complex kinematics and important reference for shoulder training strategy using robotics, is described and analyzed via the proposed skeleton model by three independent variables in this paper. This method greatly simplifies the complexity of the shoulder movement description and provides an important reference for the training strategy making of upper limb rehabilitation via robotics.
Training based on muscle-oriented repetitive movements has been shown to be beneficial for the improvement of movement abilities in human limbs in relation to fitness, athletic training, and rehabilitation training. In this paper, a muscle-specific rehabilitation training method based on the optimal load orientation concept (OLOC) was proposed for patients whose motor neurons are injured, but whose muscles and tendons are intact, to implement high-efficiency resistance training for the shoulder muscles, which is one of the most complex joints in the human body. A three-dimensional musculoskeletal model of the human shoulder was used to predict muscle forces experienced during shoulder movements, in which muscles that contributed to shoulder motion were divided into 31 muscle bundles, and the Hill model was used to characterize the force-length properties of the muscle. According to the musculoskeletal model, muscle activation was calculated to represent the muscle force. Thus, training based on OLOC was proposed by maximizing the activation of a specific muscle under each posture of the training process. The analysis indicated that the muscle-specific rehabilitation training method based on the OLOC significantly improved the training efficiency for specific muscles. The method could also be used for trajectory planning, load magnitude planning, and evaluation of training effects.
Abstract. In order to implement the high-efficiency resistance training for a specific muscle of human shoulders using the rehabilitation robots, a muscle-specific rehabilitation training method based on the optimal load orientation concept (OLOC) was proposed. A 3D mathematical musculoskeletal model of the shoulder complex was used to predict the muscle forces. In this model, 31 muscle bundles were used to represent all the muscles contributing to the shoulder function, and the Hill-type model was used to characterize the mechanical property of the muscles. The calculation results show that, for a specific muscle, there is always an optimal load orientation (OLO) of the external load which lead the activation of muscle to its maximum. Moreover, the distribution of the OLO is significantly consistent as the movement under different magnitudes of load. Thus the optimal load orientation cluster for a specific muscle, which can be used to specify a muscle-specific rehabilitation strategy, was determined. Simultaneously, the analysis suggests that the muscle-specific rehabilitation training method based on the OLOC could improve the training efficiency of specific muscles significantly.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.