Search Results (54)

Reliable control of robotic hands using residual muscle activity is challenging due to low-amplitude myoelectric signals, susceptibility to noise, and the need for real-time actuation. This paper presents a myoelectric-controlled robotic hand capable of voluntary independent finger motion. Surface myoelectric signals from the forearm are processed via amplification, filtering, and digital analysis to enable accurate detection of muscle activity. The system achieves independent and simultaneous actuation of five fingers using a tendon-driven, servo-actuated mechanism in a lightweight ABS structure. Experimental evaluation demonstrates finger actuation delays ranging from 314 ms to 650 ms, maximum holding strengths between 1.75 N and 4.07 N, and minimum gripping distances between 22 mm and 49 mm across all five fingers, with peak motor currents remaining below 0.7 A. Results validate consistent muscle activity detection, successful execution of individual and combined finger movements, and the robustness of the proposed design. Full article

Background/Objectives: Hand loss or severe impairment significantly reduces quality of life by restricting essential daily activities and professional tasks. Despite advances in prosthetics, challenges remain in affordability, accessibility, and usability. This study aimed to design and develop a low-cost, ergonomic bionic hand prototype that integrates sustainable fabrication, intuitive control, and modular electronics. Methods: A user-centred design process guided by iterative prototyping, anatomical modelling, and functional validation. The prototype was manufactured using 3D printing techniques and assembled with modular electronic components. The design included segmented fingers, independent thumb articulation, and a tendon-like actuation system driven by micro-motors. Control was implemented through an ESP32-based board and a Bluetooth-enabled mobile application. Durability was preliminarily assessed through 500 grasp–release cycles. Results: Experimental validation confirmed the feasibility of both precision and power grips. The pinch grip successfully lifted objects to 120 g, and the power grip up to 85 g, corresponding to effective output forces of approximately 1.2 N and 0.83 N, respectively. The final prototype weighed ~350 g and maintained reliable performance during 500 grasp–release cycles. Conclusions: The developed bionic hand demonstrates that an affordable, ergonomic, and functional prosthetic can be achieved through sustainable 3D printing and accessible electronics. Future work will focus on enhancing actuation strength, long-term durability, and integration of sensory feedback, with the long-term objective of clinical testing and scalable production. Full article

Addressing the challenges of large-scale gesture recognition models, high computational complexity, and inefficient deployment on embedded devices, this study designs and implements a visual dynamic gesture recognition system based on a lightweight CNN-LSTM-DSA model. The system captures user hand images via a camera, extracts 21 keypoint 3D coordinates using MediaPipe, and employs a lightweight hybrid model to perform spatial and temporal feature modeling on keypoint sequences, achieving high-precision recognition of complex dynamic gestures. In static gesture recognition, the system determines the gesture state through joint angle calculation and a sliding window smoothing algorithm, ensuring smooth mapping of the servo motor angles and stability of the robotic hand’s movements. In dynamic gesture recognition, the system models the key point time series based on the CNN-LSTM-DSA hybrid model, enabling accurate classification and reproduction of gesture actions. Experimental results show that the proposed system demonstrates good robustness under various lighting and background conditions, with a static gesture recognition accuracy of up to 96%, dynamic gesture recognition accuracy of 90.19%, and an overall response delay of less than 300 ms. Full article

In recent years, agricultural production activities have been advancing towards mechanization and intelligence to bridge the growing gap between the high labor intensity and time sensitivity of harvesting operations and the limited labor resources. As the component that directly interacts with target crops, the end-effector is a crucial part of agricultural harvesting robots. This paper first reviews their materials, number of fingers, actuation methods, and detachment techniques. Analysis reveals that three-fingered end-effectors, known for their stability and ease of control, are the most prevalent. Soft materials have gained significant attention due to their flexibility and low-damage characteristics, while the emergence of variable stiffness technology holds promise for addressing their issues of poor stability and fragility. The introduction of bionics and composite concepts offers potential for enhancing the performance of end-effectors. Subsequently, starting from an analysis of the biomechanical properties of fruits and vegetables, the relationship between mechanical damage and the intrinsic parameters of produce is elucidated. On the other hand, practical and efficient finite element analysis has been applied to various stages of end-effector research, such as structural design and grasping force estimation. Given the importance of compliance control, this paper explores the current research status of various control methods. It emphasizes that while hybrid force–position control often suffers from frequent controller switching, which directly affects real-time performance, active admittance control and impedance control directly convert external forces or torques into the robot’s reference position and velocity, resulting in more stable and flexible external control. To enable a unified comparison of end-effector performance, this review proposes a progressive comparison framework centered on control philosophy, comprising the ontological characteristic layer, physical interaction layer, feedback optimization layer, and task layer. Additionally, in response to the current lack of scientific rigor and systematization in performance evaluation systems for end-effectors, performance evaluation criteria (harvest success rate, harvest time, and damage rate) are defined to standardize the characterization of end-effector performance. Finally, this paper summarizes the challenges faced in the development of end-effectors and analyzes their causes. It highlights how emerging technologies, such as digital twin technology, can improve the control accuracy and flexibility of end-effectors. Full article

In pharmaceutical and biomedical industries, manual handling of dangerous chemicals is a leading cause of hazardous exposure to chemicals, toxic burning, and chemical contamination. To counteract these risks, we proposed a gesture-controlled bionic hand system to mimic human finger movements for safe and contactless chemical handling. This innovation system uses an ESP32 microcontroller to decode the hand gestures that are detected by the system using computer vision via an integrated camera. A PWM servo driver converts these movements to motor commands such that accurate movements of the fingers can be achieved. Teflon and other corrosion-proof materials are utilized in the 3D printing of the bionic hand in order to withstand corrosive conditions. This new, low-cost, and non-surgical approach replaces the EMG sensors, gives real-time control, and enhances industrial and laboratory process safety. The project is a major milestone in the application of robotics and AI for automation and risk reduction in dangerous environments. Full article

The primary objective of the presented study is to develop a comprehensive system for the acquisition of surface electromyographic (sEMG) data and to perform time–frequency analysis aimed at extracting discriminative features for the classification of hand gestures intended for the control of a simplified bionic hand prosthesis. The proposed system is designed to facilitate precise finger gesture execution in both prosthetic and robotic hand applications. This article outlines the methodology for multi-channel sEMG signal acquisition and processing, as well as the extraction of relevant features for gesture recognition using artificial neural networks (ANNs) and other well-established machine learning (ML) algorithms. Electromyographic signals were acquired using a prototypical LPCXpresso LPC1347 ARM Cortex M3 (NXP, Eindhoven, Holland) development board in conjunction with surface EMG sensors of the Gravity OYMotion SEN0240 type (DFRobot, Shanghai, China). Signal processing and feature extraction were carried out in the MATLAB 2024b environment, utilizing both the Fourier transform and the Hilbert–Huang transform to extract selected time–frequency characteristics of the sEMG signals. An artificial neural network (ANN) was implemented and trained within the same computational framework. The experimental protocol involved 109 healthy volunteers, each performing five predefined gestures of the right hand. The first electrode was positioned on the brachioradialis (BR) muscle, with subsequent channels arranged laterally outward from the perspective of the participant. Comprehensive analyses were conducted in the time domain, frequency domain, and time–frequency domain to evaluate signal properties and identify features relevant to gesture classification. The bionic hand prototype was fabricated using 3D printing technology with a PETG filament (Spectrum, Pęcice, Poland). Actuation of the fingers was achieved using six MG996R servo motors (TowerPro, Shenzhen, China), each with an angular range of ${180}^{\circ }$, controlled via a PCA9685 driver board (Adafruit, New York, NY, USA) connected to the main control unit. Full article

The complex structure of the wrist joint supports the hand to complete a variety of dexterous and accurate operations in daily living, which in turn makes it vulnerable to motor injury due to stroke, sports, occupational, or traffic accidents. As a supplement to traditional medical treatment, timely and effective rehabilitation training can accelerate the recovery process of wrist motor function. The wearable rehabilitation device in this work exhibits excellent application prospects in the field of human rehabilitation training due to its inherent flexibility and safety. Inspired by the motion principle of tendons and muscles, a modular vacuum-actuated artificial muscle (VAM) is proposed, with the advantages of being lightweight and having a high contraction ratio. The VAMs are applied to the development of a wearable wrist rehabilitation device (WWRD) prototype, which can realize wrist rehabilitation training in the motion directions of extension, flexion, radial deviation, and ulnar deviation. The design concept, structural model, and motion analysis of a WWRD are introduced to provide a reference for the design and analysis of the WWRD prototype. To evaluate the performance of the WWRD, we establish the force and motion parameter models of the WWRD and carry out performance experiments. The process of wrist rehabilitation training is tested and evaluated, which indicates that the WWRD with VAMs will enhance flexibility, comfort, and safety in wrist rehabilitation training. This work is expected to promote the development of high-performance wearable wrist rehabilitation devices based on an understanding of the bionic vacuum-actuated artificial muscles. Full article

To achieve obstacle-avoiding puncture in breast interventional surgery, a robotics system based on three-fingered breast target-point manipulation is proposed and designed. Firstly, based on the minimum number of control points required for three-dimensional breast deformation control and the bionic structure of the human hand, the structure and control scheme of the robotics system based on breast target-point manipulation are proposed. Additionally, the workspace of the robotics system is analyzed. Then, an optimal control point selection method based on the minimum resultant force principle is proposed to achieve precise manipulation of the breast target point. Concurrently, a breast soft tissue manipulation framework incorporating a Model Reference Adaptive Control (MRAC) system is developed to enhance operational accuracy. A dynamic model of breast soft tissue is developed by using the manipulative force–displacement data obtained during the process of manipulating breast soft tissue with mechanical fingers to realize the manipulative force control of breast tissue. Finally, through simulation and experiments on breast target-point manipulation tasks, the results show that this robotic system can achieve spatial control of breast positioning at arbitrary points. Meanwhile, the robotic system proposed in this study demonstrates high-precision control with an accuracy of approximately 1.158 mm (standard deviation: 0.119 mm), fulfilling the requirements for clinical interventional surgery in target point manipulation. Full article

This study uses bionics as an enabling methodology to bridge the gap between biology and engineering for generating innovative designs for implementation into novel technology development. A product lifecycle management (PLM) methodology framework is proposed that uses bionics as a technical discipline. The manuscript presents a novel, reverse biomimetics as a shape abstraction methodology to investigate, analyse, and de-feature biological structures through functional morphology as the enabling methodology for studying the relationships between form and function. The novel reverse engineering (RE) format with eleven stages supports technical biology, addressing the abstraction issues which have been identified as the most difficult steps in Fayemi’s eight-step framework. Inverse biomimetics and RE changes functional modelling (FM) from highly abstracted principles to low- or even reality-level abstraction, achieving nature design intents. The goal of the reverse biomimetic approach is to implement functional feature extraction, surface reconstruction, and solid modelling into five stages of a design process. The benefit of virtually mapping this in a pictorial fashion with high-end software fosters a simpler understanding and representation of knowledge transfer from biology to engineering, and can lead to innovative bio-inspired developments. The study aims to present the bionics PLM framework and its comprehensive processes of bionic design and biomimetic modelling, simulation, optimisation, and clinical validation techniques for two large-scale, human skeletal biological systems: a drug-releasing chewing robot and an anthropometric prosthetic hand suitable for introduction to engineering courses. Integration into undergraduate courses would be one route to bolster interest and encourage growth within the subject area in future. Full article

As the end-effector of a humanoid robot, the dexterous hand plays a crucial role in the process of robot execution. However, due to the complicated and delicate structure of the human hand, it is difficult to replicate human hand functionality, balancing structural complexity, and cost. To address the problem, the article introduces the design and development of a multi-finger synergistic adaptive humanoid dexterous hand with underactuation flexible articulated fingers and integrated pressure sensors. The proposed hand achieves force feedback control, minimizes actuator use while enabling diverse grasping postures, and demonstrates the capability to handle everyday objects. It combines advanced bionics with innovative design to optimize flexibility, ease of manufacturing, and cost-effectiveness. Full article

This study is part of a broader project, the Open Source Bionic Hand, which aims to develop and control, in real time, a low-cost 3D-printed bionic hand prototype using signals from the muscles of the forearm. This work is intended to implement a bimodal signal acquisition system, which uses EMG signals and Force Myography (FMG) in order to optimize the recognition of gesture intention and, consequently, the control of the bionic hand. The implementation of this bimodal EMG-FMG system will be described. It uses two different signals from BITalino EMG modules and Flexiforce™ sensors from Tekscan™. The dataset was built from thirty-six features extracted from each acquisition using two of each EMG and FMG sensors in extensor and flexor muscle groups simultaneously. The extraction of features is also depicted, as well as the subsequent use of these features to train and compare Machine Learning models in gesture recognition through MATLAB’s Classification Learner tool (v2.2.5 software). Preliminary results obtained from a dataset of three healthy volunteers show the effectiveness of this bimodal EMG/FMG system in the improvement of the efficacy on gesture recognition as it is shown, for example, for the Quadratic SVM classifier that raises from 75.00% with EMG signals to 87.96% using both signals. Full article

Wrist movements play a crucial role in upper-limb motor tasks. As prosthetic and robotic hand technologies have evolved, increasing attention has been focused on replicating the anatomy and functionality of the wrist. Closely imitating the biomechanics and movement mechanisms of human limbs is expected to enhance the overall performance of bionic robotic hands. This study presents the design of a tendon-driven bionic spherical robot wrist, utilizing two pairs of cables that mimic antagonist muscle pairs. The cables are actuated by pulleys driven by servo motors, allowing for two primary wrist motions: flexion–extension and ulnar–radial deviation. The performance Please confirm if the “1583 Iiyama” is necessary. Same as belowof the proposed robot wrist is validated through manipulation experiments using a prototype, demonstrating its capability to achieve a full range of motion for both ulnar and radial deviation. This wrist mechanism is expected to be integrated into robotic systems, enabling greater flexibility and more human-like movement capabilities. Full article

Humans have a complex way of expressing their intuitive intentions in real gestures. That is why many gesture detection and recognition techniques have been studied and developed. There are many methods of human hand signal reading, such as those using electroencephalography, electrocorticography, and electromyography, as well as methods for gesture recognition. In this paper, we present a method based on real-time surface electroencephalography hand-based gesture recognition using a multilayer neural network. For this purpose, the sEMG signals have been amplified, filtered and sampled; then, the data have been segmented, feature extracted and classified for each gesture. To validate the method, 100 signals for three gestures with 64 samples each signal have been recorded from 2 users with OYMotion sensors and 100 signals for three gestures from 4 users with the MyWare sensors. These signals were used for feature extraction and classification using an artificial neuronal network. The model converges after 10 sessions, achieving 98% accuracy. As a result, an algorithm was developed that aimed to recognize two specific gestures (handling a bottle and pointing with the index finger) in real time with 95% accuracy. Full article

Cotton topping is a crucial aspect of cotton production, inhibiting apical dominance in cotton plants. Existing cotton topping machinery often results in over-topping. To address this challenge, the characteristics of manual topping operations were emulated by incorporating bionic principles to analyze the motions involved. Studying the artificial topping action and the trajectory of hand movements led to the design of a bionic topping manipulator and a trajectory-generating mechanism, serving as the core component of the cotton topping device. A flat-bottomed follower disc cam mechanism was used to facilitate the automatic opening and closing of the manipulator. The cam’s working area was divided, its contour curve selected, and the manipulator’s pulling spring’s action point and length determined. Subsequently, parametric equations for the motion trajectory of the bionic topping manipulator were established. Building on the topping mechanism’s working principle, a mechanical model was developed to analyze the swing of cotton plants. The model demonstrates that the displacement at the free end of the stalk was primarily influenced by its length. A lifter was then designed to reduce plant swing amplitude and orderly distribute its top position. The designed prototype of a single-row cotton bionic topping device was tested and verified through orthogonal tests, using operating speed, rotational speed, and topping depth as test factors. The topping rate and over-topping rate served as the indices for testing. The results indicated an average topping rate of 78.67% and an over-topping rate of 8%. This was achieved at a 0.3 m/s operating speed, a 40 r/min rotational speed, and a 110 mm topping depth. Cotton topping devices demonstrated greater effectiveness in minimizing damage to cotton plants, and future research should focus on enhancing topping rates even further. This study provides a theoretical foundation and test data to support the design of cotton topping machinery, guiding future mechanical improvements and agricultural practices. Full article

The cutter of the hand-held tea picker is the key cutting component in the efficient tea harvesting process. In order to solve the problems of large cutting resistance and uneven incision during tea picking, this study fully applied the bionics principle to combine the excellent cutting performance of Aeolesthes induta Newman’s mandibles with the tea cutter, which extracted and fitted the tooth profile structure curve of the upper edge of the Aeolesthes induta Newman’s mandibles. The trapezoidal teeth on the reciprocating cutter of ordinary hand-held tea-picking harvesters were optimized by the fitted curve, and a new tea cutter with the shape of Aeolesthes induta Newman teeth was obtained, which included four kinds of bionic tea-harvesting cutters. The multi-body system software ADAMS 2020 and finite element analysis software ANSYS 2024R1 were used to compare the kinematics, statics and explicit dynamics of cutting properties of the four bionic cutters and common cutters with ordinary trapezoidal teeth and saw teeth. The simulation results showed that the maximum equivalent elastic strain and the maximum cutting force during the cutting operation were reduced by 36.7% and 42.89%, respectively, for the cutting teeth of the bionic tea-harvesting cutter #4 compared with that of the cutter with ordinary trapezoidal teeth. The bionic tea-harvesting cutter designed in this study has better cutting performance than the cutter with traditional cutting teeth, which can effectively reduce the cutting force and improve the flatness and cutting quality of the cutting surface. Full article