The Synergistic Assistance Mechanism of Mechanical Exoskeletons and Human Muscles from the Perspective of Biomechanical Characteristics Matching

Abstract

In the field of perception automation, five mainstream technical solutions have been identified and organized. They are position sensors, pressure sensors, electromyography (EMG) sensors, current sensors, and multi-sensor fusion. At the same time, we've made clear their core features, key performance indicators, and applicable scenarios. Our research shows that multi-sensor fusion has become the core perception solution for high-end exoskeletons. It integrates multidimensional data to reduce the error of a single sensor. When it comes to intention recognition, we compared three algorithms: Online Support Vector Machine (Online SVM), EMG signal recognition, and the empirical formula method. The results show that the Online SVM algorithm, with the zero-moment point (ZMP) as the core feature, can achieve an accuracy rate of 95% after fusing inertial measurement unit data. It has significant advantages in adaptability and real-time performance. The current technology has some problems. There are sensors, and the intention recognition algorithms have a narrow coverage in scenarios. Also, they rely highly on manual intervention. In addition, this paper proposes four optimization paths, which are performance upgrade, scenario expansion, algorithm optimization, and practicality improvement. The research results can provide theoretical references for the structural innovation and control strategy optimization of mechanical exoskeletons. They can also help exoskeleton technology be applied in clinical rehabilitation, daily assistance, and other scenarios.