Soft Pneumatic Actuators for Hand Rehabilitation: A Study on Design Enhancement and Neural Network Prediction | ||
| Engineering Research Journal | ||
| Volume 184, Issue 3, June 2025, Pages 1-18 PDF (1.02 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/erj.2025.356274.1191 | ||
| Authors | ||
| Karim Badr* 1; Abdelkader Ibrahim2; Mahmoud Elsamanty3 | ||
| 1Mechanical Engineering Department, Shoubra Faculty of Engineering, Benha University | ||
| 2Mechanical Engineering Department, Shoubra Faculty of Engineering , Benha University. | ||
| 3Mechanical Engineering Department, Shoubra Faculty of Engineering, Benha University. | ||
| Abstract | ||
| Stroke rehabilitation is a critical area of research, with soft actuators playing an increasingly important role in improving function recovery. These devices, particularly soft finger actuators, are vital for enabling precise motion control in rehabilitation training gloves, which are essential for restoring hand functionality. This study presents an in-depth analysis of soft finger actuators using finite element modeling to evaluate their performance across various structural configurations. By conducting detailed numerical simulations, the research explores how structural parameters, specifically actuator height and surrounding thickness, impact the bending angle and overall actuator performance. The results reveal that increasing actuator height significantly enhances bending capability, while thicker surrounding materials hinder bending, highlighting the need for careful design optimization. Additionally, the study employs artificial neural networks to predict bending angles, achieving an outstanding predictive accuracy with a residual variance of just 0.74% and an explained variance of 99.26%. These results underscore the potential of machine learning to refine actuator designs for therapeutic applications. The insights gained from this research contribute to the development of improved design guidelines for soft actuators, advancing rehabilitation technology and enabling more effective treatments for stroke patients. | ||
| Keywords | ||
| Soft Pneumatic Actuator; Finite Element Analysis (FEA); Geometrical Parameter Optimization; Artificial Neural Network (ANN) | ||
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