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Mobility
Published in Karl H.E. Kroemer, Fitting the Human, 2017
As Figure 2.3 shows, the five digits attach to the main body of the hand by their metacarpal bones in the carpal–metacarpal (CM) joints, which have two-axis mobility. The thumb is particularly mobile in its CM joint, but it has only two final segments (phalanges), whereas the four fingers each have three phalanges. The knuckles, metacarpal–phalangeal joints, provide two axes of mobility to the fingers, but their interphalangeal joints (proximal and distal) are simple one-axis hinges. Movers of the hand
Optimum design and analysis of a novel planar eight-bar linkage mechanism
Published in Mechanics Based Design of Structures and Machines, 2023
Recep Halicioglu, Assylbek Jomartov, Moldir Kuatova
In recent years, Stephenson mechanisms have begun to be considered for the press machines. Studies related to this mechanism have generally included synthesis of the mechanism dimensions. Hsieh and Tsai (2012) optimized a Stephenson-I mechanism design for the press system that would be used in a deep drawing process. A generalized Oldham coupling drove this mechanism. They got optimized linkage motion. Plechnik and McCarthy presented kinematic studies of the Stephenson-II and Stephenson-III six-bar linkages by sorting them into pairs of function generate cognates (Plecnik and McCarthy 2016a, 2016b). Hu, Sun, and Cheng (2016) proposed a Stephenson six-bar knuckle-joint linkage for mechanical servo presses consisting of a frame, crank, and slider connected by a knee mechanism. The knuckle-joint mechanism consisted of a rocker and a connecting rod connected to a crank through a three-joint link. Jomartov et al. (2020) studied the design of a Stephenson II six-bar linkage mechanism actuated by a servo motor. They also presented kinematic results. Tuleshov, Merkibayeva, and Akhmetova (2020) developed a kinematic synthesis method of Stephenson lever link mechanisms based on the mean-square minimization of the objective function. Although Stephenson mechanisms have been used for different purposes, it has been tried to focus on either the dwell motion or the slider balance by this mechanism used in press machines. While there has been no solution for both, it has been observed that the obtained dwell movements are not smooth.
Optimal Score Level Fusion for Multi-Modal Biometric System with Optimised Deep Ensemble Technique
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023
M. R. Bharath, K. A. Radhakrishna Rao
An discriminant local coding-related convolutional neural network (LC-CNN) was presented by Li et al. in 2020 for multi-modal finger recognition by combining ‘fingerprint, finger-vein and finger-knuckle-print features’. For each tri-modal finger picture, the authors have initially demonstrated a weighted local gradient coding (WLGC) operator to increase discriminant qualities. Bank of predefined convolution layers was used to reconstruct the image and as a result, a local coding layer (LC-layer) was formed. Authors then created a powerful LC-CNN scheme to get more detailed tri-modal finger information. In the end, they integrated the feature vectors produced by the three modalities and used them against an image analysis Support Vector Machine. Finally, the suggested work had produced reliable results for multi-modal finger findings.
Smart structures with embedded flexible sensors fabricated by fused deposition modeling-based multimaterial 3D printing
Published in International Journal of Smart and Nano Materials, 2022
Huilin Ren, Xiaodan Yang, Zhenhu Wang, Xuguang Xu, Rong Wang, Qi Ge, Yi Xiong
The finger in the gripper system is a typically multifunction structure with both pressure and bending sensing modality. Therefore, the whole finger was fabricated by multimaterial FDM, with a spherical shell pressure sensor and a bending sensor deployed at the tip and knuckle regions, respectively. The pressure sensor on the fingertip worked well to distinguish the force applied on the effective region, as shown in Figure 9(b). This detected touch signal makes it potential to determine if there is contact with actual things and thus identify the weight of gripped objects. The bending sensor is located around the joints because of the concentrated deformation according to the simulation of finger bent, as shown in Figure 9(c). A cable running through the soft finger was employed for bending and stretching. When the cable was pulled, two joints supported the motions of the finger. Figure 9(d) shows a corresponding change in the electrical signal with the bending angle of 30°, 60° and 90°, validating the effectiveness of the bending sensor. The application of the finger with two individual sensors reveals the potential of a smart structure embedded with multiple sensors to handle multitasking at a time. Such smart structures can be utilized in various intelligent systems, including soft robotics, IoT and industrial automation.