## 5 Conclusions  
In the present investigation, an innovative methodology was proposed for the prediction of the shoulder and elbow joints angles, leveraging MMG signals in conjunction with the Broad Learning System optimized by the Slime Mold Algorithm (SMA-BLS).  
What's more, the study found that with the increase in time in the future, the training accuracy of the model and the real-time prediction accuracy of the joint angle decreased, but the real-time prediction accuracy of the joint angle of the proposed method can meet the fine angle control requirements of the rehabilitation upper limb exoskeleton. In the subsequent research on the rehabilitation upper limb exoskeleton, the control method considered for application will use the joint angle prediction method proposed in this study. This method uses the MMG signal of an intact arm combined with the contact force signal and the joint motor angle signal to drive the rehabilitation exoskeleton. The purpose is to use the patient's intact arm to drive the rehabilitation exoskeleton to train the paralyzed arm. The prediction results based on MMG and the IMU have also been compared. The comparison results show that MMG is indeed superior to the IMU, and its prediction accuracy for joint angles under prescribed exercises is better than the IMU.  
In addition, the study found that as the hand load increased from 0 to 1 KG, the accuracy of joint angle prediction was significantly improved. This shows that the model proposed in this study can be applied to joint angle prediction in daily behavior, and the model also has broad application prospects in the field of upper limb assisted exoskeleton control. In future research, upper limb assisted exoskeletons based on MMG signals will be developed to freely adjust joint angle prediction model parameters and exoskeleton assistance strategies according to the user's hand load, thereby reducing the sense of frustration during the assistance process and ultimately achieving precise assistance and smooth control.  
In this study, the prediction accuracy of joint angles under loads greater than 1 kg was not tested. This is because excessive loads may cause muscles to quickly enter the fatigue stage, resulting in an abnormal decrease in prediction accuracy. In subsequent studies, joint angle prediction combined with muscle fatigue level identification will also be one of the research focuses, because fatigue is a common condition in human daily activities, and joint angle prediction considering fatigue conditions will have a wider range of application scenarios.  
**Author Contributions:** Methodology, Y.B.; Software, Y.B., S.C., R.Z. and L.H.; Validation, Y.B. and H.L.; Formal analysis, S.C.; Investigation, L.H.; Resources, R.Z.; Data curation, Y.B., S.C., R.Z. and L.H.; Writing--original draft, Y.B.; Writing--review and editing, Y.B.; Visualization, X.G. and H.L.; Supervision, X.G. and H.L.; Project administration, Y.B., X.G. and H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Postgraduate Research Practice Innovation Program of Jiangsu Province (Grant number KYCX23-0512).  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Nanjing Medical University (Project identification code 2021-SR109, date of experiment approval is 1 January 2024).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Acknowledgments:** Thanks to the experiment participants and Postgraduate Research Practice Innovation Program of Jiangsu Province for their support of this study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.