A Study On Upper Limb Comfort-using Optimization Of Palmprint Recognition Equipment Design Parameters Asan Example

As an important indicator in product ergonomic design, comfort degree affects not only users’ satisfaction with the product, but also users’ health. Therefore, based on a palm print identification device, a human body dimension predication model was constructed in this paper. Then, postures of human upper limbs were predicted to construct a joint torque-based hand comfort degree evaluation model. In addition, the palm print identification device was optimized.At first, human body dimensions were predicted. Since there are differences among human body dimensions, accurate typical human body dimensions constitute the premise for good product design. In this paper, a small group of samples were used to obtain big data, so as to predict human body dimensions. To be exact, stepwise regression analysis was used to select height, body mass index (BMI) and crotch height/height as predictive factors, so as to construct a regression model, through which upper arm length, forearm length and hand length were predicted.Then, postures of human upper limbs were predicted. In software CATIA V5. a 3D model of palm print identification device was constructed. In addition, customized human body model files were imported into this software in accordance with relevant rules. Different parameter configurations of palm print identification device were set.Particular height percentiles were selected for simulation experiment in software CATIA V5, in order to obtain prediction results of 15 groups of human body postures.The 15 groups of simulation results were analyzed to study effects of parameter configurations of palm print identification device on joint angle.In addition, the evaluation model of the upper limb comfort level based on joint torque was constructed based on the calculation of joint torque. Through the construction of human upper limb dynamic model, the joint moments of the upper limbs of the human body were calculated, and the correlation analysis was conducted on the joint torque. The selected joint torque was used as the predictive factor, and the evaluation model of the upper limb discomfort level was obtained by regression analysis. Taking the subway gate as an example, the comfort evaluation was respectively conducted by the quick upper limb evaluation method and the evaluation model of upper limb discomfort level. The variation trends of the two evaluation results were similar, and the rationality of the upper limb discomfort level model was verified.Finally, the man-machine design parameters of the palmprint identification device were optimized. According to the design requirements of the palmprint identification equipment and the characteristics of each design parameter, the design variables were determined, and the calculation results of the upper limb discomfort level were chosen as the optimization target. The value range of the design variables was taken as the constraint condition. The man-machine design parameters of the palmprint identification device were optimized to obtain the optimal man-machine design parameters of palmprint identification equipment.