Abstract
ObjectiveHuman intention recognition technology plays a vital role in the application of robotic exoskeletons and powered exoskeletons. However, the precise estimation of the continuous motion of each joint represents a major challenge. In the current study, we present a method for estimating continuous elbow joint movement.MethodsWe developed a novel approach for estimating the elbow joint angle based on human physiological structure. We used surface electromyography signals to analyze the biomechanical properties of the muscle and combined it with physiological structure to achieve a model for estimating continuous motion. And a genetic algorithm was used to optimize unknown parameters.ResultsWe performed extensive trials to verify the generalizability and effectiveness of this method. The trial types included elbow joint motion with single cycle trials, typical cycle trials, gradually increasing amplitude trials, and random movement trials for handheld loads of 1.25 and 2.5 kg. The results revealed that the average root-mean-square errors ranged from 0.12 to 0.26 rad, reflecting an appropriate level of estimation accuracy.ConclusionEstablishing a reasonable physiological model and applying an efficient optimization algorithm enabled more accurate estimation of the joint angle. The proposed method provides a theoretical foundation for robotic exoskeletons and powered exoskeletons to understand the intentions of human continuous motion.
Highlights
In recent years, robotic exoskeletons have been widely applied in assisting the human body
The current study presents a method for estimating continuous motion of the elbow joint based on the physiological structure of the upper limb, which is a method utilizing surface electromyography (sEMG) to determine human intent
In the current study, we proposed a visualized structural model of the human elbow joint based on examination of its physiological structure
Summary
Robotic exoskeletons have been widely applied in assisting the human body. Human bioelectrical signals are potentials excited by neurons that carry human behavioral information when transmitted to related tissues/organs, directly reflecting human intentions. These signals can be collected from the human body via specialized electrodes, and motion intention can be judged by analyzing the signals. These methods include physiological models, classification and regression techniques, Li et al BioMed Eng OnLine (2019) 18:31 which have been used to estimate motion intention [2]. Previous studies of human intention identified by sEMG have largely focused on the classification of human actions for HRI systems This method has only been used to predict a small number of discrete movements of the human body. The current study presents a method for estimating continuous motion of the elbow joint based on the physiological structure of the upper limb, which is a method utilizing sEMG to determine human intent
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