# Shoulder-Elbow Joint Angle Prediction Using COANN with Multi-Source Information Integration
## 1 Introduction  
In recent years, China's rehabilitation medicine field has witnessed continuous deepening development. According to the 2023 Annual Report by the China Disabled Persons' Federation, 12,463 rehabilitation institutions have been established nationwide, with increasing demand for upper-limb rehabilitation robots and motion-assistive devices. The conflict between rapid response and prediction accuracy in motion intent recognition has become increasingly prominent [1, 2, 3, 4, 5], making research on high-precision, fast-response human-robot interaction models for these devices a major focus. Domestic and international researchers have actively explored solutions. In China: [PERSON] et al. implemented the least squares support vector machine (LSSVM) method for upper-limb joint angle prediction, addressing issues of suboptimal accuracy and slow prediction speeds in motion control models [1]. [PERSON] et al. developed a long short-term memory network with attention mechanisms and residual blocks (AR-LSTM) to continuously estimate elbow joint angles [2]. [PERSON] et al. proposed a generalizedregression neural network (GRNN) to predict subjects' upper-limb joint angles, evaluated using the root mean square error (RMSE) [3]. [PERSON] et al. introduced a rapid upper-limb assessment (RULA) method based on adaptive neuro-fuzzy inference systems [4]. [PERSON] designed a VMD-ELMAN angle-fitting algorithm to enhance real-time prediction [5]. [PERSON] et al. proposed a method for lower-limb multi-joint continuous motion prediction using surface electromyography (sEMG) signals, demonstrating higher accuracy with sparrow search algorithm-optimized Elman neural networks [6]. Internationally: [PERSON] et al. applied Gaussian process regression models to lower-limb muscle modeling [7]. [PERSON] et al. combined K-nearest neighbors (KNNs) with artificial neural networks (ANNs) for real-time prediction [8]. [PERSON] et al. developed a machine learning method for sEMG signal binary classification [9]. [PERSON] et al. proposed a multi-scale convolutional neural network (MSCNN) with gradient reversal layers for domain adaptation, significantly improving prediction capabilities [10]. The Cheetah Optimizer Algorithm (COA) has demonstrated significant optimization potential across multiple domains in recent years, though its application in neural network training remains underexplored. In a comprehensive evaluation conducted by [PERSON] et al., the COA exhibited superior convergence accuracy and stability compared with 12 established algorithms, including the Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO), across 14 CEC-2005 benchmark functions. When applied to engineering problems, the COA achieved performance enhancements of 3.57-56.52% over Genetic Algorithms (GAs) [11], with its open-source implementation accessible via the Optim-App platform. [PERSON] et al. proposed a novel methodology that maximizes network coverage through biomimetic simulations of cheetah hunting behavior. However, this research focused exclusively on static optimization scenarios, lacking investigations into dynamic weight adaptation or high-dimensional (d-dimensional) parameter spaces [12]. In this paper, the Cheetah Optimization Algorithm (COA) is introduced into ANN weight optimization for the first time to simulate the dynamics of cheetah hunting in a three-phase strategy--the first phase of the fast-tracking strategy (the first 30% iteration), the second phase of the ambush strategy (the middle 40% iteration), and the third phase of the attack strategy (the second 30% iteration)--in order to avoid falling into the local optimum leading to a long prediction response time [11]; at the same time, it combines the sEMG with the joint angle for multi-information fusion prediction to make up for the limitation of a single signal, improve the robustness of the prediction model (which is able to capture the sEMG signals in a timely manner when the muscle state changes) and to provide a good solution for upper-limb rehabilitation robots.