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Biometric identification system has become emerging research field because of its wide applications in the fields of security. This study (multimodal system) aims to find more applications than the unimodal system because of their high user acceptance value, better recognition accuracy and low-cost sensors. The biometric identification using the finger knuckle and the palmprint finds more application than other features because of its unique features.

The proposed model performs the user authentication through the extracted features from both the palmprint and the finger knuckle images. The two major processes in the proposed system are feature extraction and classification. The proposed model extracts the features from the palmprint and the finger knuckle with the proposed HE-Co-HOG model after the pre-processing. The proposed HE-Co-HOG model finds the Palmprint HE-Co-HOG vector and the finger knuckle HE-Co-HOG vector. These features from both the palmprint and the finger knuckle are combined with the optimal weight score from the fractional firefly (FFF) algorithm. The layered k-SVM classifier classifies each person's identity from the fused vector.

Two standard data sets with the palmprint and the finger knuckle images were used for the simulation. The simulation results were analyzed in two ways. In the first method, the bin sizes of the HE-Co-HOG vector were varied for the various training of the data set. In the second method, the performance of the proposed model was compared with the existing models for the different training size of the data set. From the simulation results, the proposed model has achieved a maximum accuracy of 0.95 and the lowest false acceptance rate and false rejection rate with a value of 0.1.

In this paper, the multimodal biometric recognition system based on the proposed HE-Co-HOG with the k-SVM and the FFF is developed. The proposed model uses the palmprint and the finger knuckle images as the biometrics. The development of the proposed HE-Co-HOG vector is done by modifying the Co-HOG with the holoentropy weights.

The purpose of this paper is to propose a method for hand measurement based on image and marker watershed algorithm, and combine the data to analyze the shape and characteristics of the hand.

A portable hand image capturing instrument was designed and manufactured, and the hand images and dimensions of 328 young men in Zhejiang area were obtained. The outer contour curve of the hand and the key points of finger root, fingertip, wrist and knuckle position were extracted. Then, the size of each hand part was calculated. The hand data obtained from the two-dimensional image was compared with the manual measurement data. Finally, the hands were classified according to the measurement data, and the relationship between hand control size and hand length, hand width and the relationship between hand length and height were explored.

The data comparison results show that the two measurement methods have high data consistency and are replaceable. In addition, analyzing the data obtained four major characteristic factors that affect the shape of the hand, divided the hands of young men in Zhejiang into five categories, and obtained the regression equations of basic hand size, hand length and hand width, and obtained the regression equation of hand length and height.

The method proposed in this study to obtain hand size based on the image and mark watershed algorithm has lower requirements on the external environment and testers, conforms to the development trend of applying artificial intelligence to anthropometric engineering and provides a useful reference value for data collection of gloves specification design. In addition, the results of data analysis can provide a valuable reference basis for consumer hand shape predictions, which can be used to guide the research and production of hand instruments, the design of specifications series and the purchase of hand products.

The purpose of this study is to provide a novel multi-fingered hand made of hyperelastic material. This kind of hand has the advantage of less mechanical parts, simpler control system. It can greatly cut down the complexity and cost of the hands under conditions of ensuring enough flexibility of grasping.

Based on the principle of virtual work, the equations of pulling force and grasping force are derived. To get the max grasping force, the optimal structural dimensions of the hand are obtained by finite element simulations. Hand’s grasping experiment is conducted.

The factors influencing grasping force and grasping stability are identified, and they are the length between short poles around the knuckles and the height of short poles. Experimental results show that the max strain of knuckles is less than the elastic limit of hyperelastic material, and the presented hand is practicable. The adaptive ability and grasping stability of the presented hand are demonstrated.

A novel multi-fingered hand made of hyperelastic material is presented in this paper. By designing the thickness of every section of a hyperelastic plate, the knuckle sections will bend and other sections of the plate will remain straight, and thus, the multi-fingered hand will grasp.

The purpose of this paper is to study the innovative design of prosthetic hands now in production from a Scottish spin‐off company.

The novel features are described, followed by the details of the mechanical construction and the available grip patterns. The benefits of the modular design are explained, and the function and construction of a skin‐like covering are explored. The clinical network supporting the prosthesis is briefly outlined.

The design allows patients to achieve many functional and natural‐looking hand configurations from simple “open” and “close” signals. Miniature motors and gearboxes allow independent movement of each digit, and an integral microprocessor translates electric signals from the forearm to control the movements of the hand. Stall detection ensures that no finger exerts excessive force or wastes power. Artificial skin can be very realistic, with imitation fingernails, hairs and pores. The lifelike prosthesis helps the patient emotionally, socially and at work.

The paper shows how advances in motors, gearboxes, batteries and electronics have enabled a breakthrough in prosthetic design.

The application of rapid prototyping in fabricating a non‐assembly, multi‐articulated robotic hand with inserts is presented in this paper. The development of robotic systems that have all necessary components inserted, with no assembly required, and ready to function when the manufacturing process is complete is quite attractive. Layered manufacturing, in particular stereolithography, can provide a means to do this. Stereolithography produces a solid plastic prototype via a manufacturing procedure where three‐dimensional solid models are constructed layer upon layer by the fusion of material under computer control. An important aspect of the rapid prototype method used in this research is that multi‐jointed systems can be fabricated in one step, without requiring assembly, while maintaining the desired joint mobility. This document presents the design and techniques for part insertion into a non‐assembly, multi‐articulated, dexterous finger prototype built with stereolithography.

Many people suffer from injuries related to their hand. This research aims to focus on the improvement of the previously developed smart glove by using position and force control algorithms. The new smart glove may be used for both physiotherapy and assistance.

The proposed robot uses shape memory alloy (SMA) actuators coupled to an under-actuated tendon-driven mechanism. The proposed device, which is presented as a wearable glove attached to an actuation module, is capable of exerting extremely high forces to grasp objects in various hand configurations. The device’s performance is studied in physiotherapy and object manipulation tasks. In the physiotherapy mode, hand motion frequency is controlled, whereas the grasping force is controlled in the object manipulation mode. To simulate the proposed system behavior, the kinematic and dynamic equations of the proposed system have been derived.

The achieved results verify that the system is suitable to be used as part of a rehabilitation device in which it can flex and extend fingers with accurate trajectories and grasp objects efficiently. Specifically, it will be shown that using six SMA wires with the diameter of 0.25 mm, the proposed robot can provide 45 N gripping force for the patients.

The proposed robot uses SMA actuators and an under-actuated tendon-driven mechanism. The resulted robotic system, which is presented as a wearable glove attached to an actuation module, is capable of exerting extremely high force levels to grasp objects in various hand configurations. It is shown that the motion and exerted force of the robot may be controlled effectively in practice.

This paper aims to design a magnetostrictive tactile sensor for surface depth detection. Unlike the human finger, although most tactile sensors have high sensitivity to pressure, they cannot detect millimeter-level depth information on the surface of objects precisely. To enhance the ability to detect surface depth information, a piezomagnetic sensor combining inverse magnetostrictive effect and bionic structure is developed in this paper.

A magnetostrictive tactile sensor based on Galfenol [(Fe₈₃Ga₁₇)_99.4B_0.6] is designed and studied for surface depth measurement. The optimal structure of the sensor is determined by experiment and theory. The test platforms for static and dynamic characteristics are set up. The static and the dynamic sensing performance of the sensor are studied experimentally.

The sensor can detect 0–2 mm depth change with a sensitivity of 91.5 mV/mm. A resolution of 50 µm can be achieved in the depth direction. In 50 cycles of loading and unloading tests, the maximum error of the sensor output voltage amplitude is only 2.23%.

The sensor can measure the depth information of object surface precisely with good repeatability through sliding motion and provide reference for object surface topography detection.

The purpose of this paper is to review the Automate Show (robots and vision) and the Promat Show (material handling) that ran jointly in Chicago, with emphasis on the new robot innovations and applications on display.

In‐depth interviews with exhibitors of robots, as well as system integrators who apply robots to specific categories of applications.

Telepresence robots have come of age with multiple vendors offering units. Delta robots have found a niche in quick handling of packing and order assembly tasks.

Customers will have to begin thinking of robots as much more that automated workers and start to envision them as tools for making it possible for people to be in more than one place at the same time. Logistics customers need to examine the abilities of the delta style robot to do more and faster order assembly than ever before.

A fresh look at innovations in robotic technology for users. Robots are not limited to just “heavy” lifting any more.

The area of behavioural phenotype research and related clinical practice is now recognised as one of high relevance to all practitioners who help people with learning disabilities, whatever their age. Knowledge continues to accumulate rapidly regarding aspects pertaining to aetiology, likely developmental, emotional and behavioural challenges, useful multidisciplinary interventions and supports and long‐term prognosis. This paper reviews the concept, its history and recent developments, focusing on those aspects which are of particular importance to clinical and other care and support professionals and their clients. There is a continuing need for widespread dissemination of the large body of relevant information, and its application to practice in order to maximise benefits for people with learning disabilities and their families.