Ultrasound imaging is widely used for early breast tumor screening in clinical practice due to its operational simplicity and absence of radiation exposure. In this paper, a prone position autonomous breast ultrasound scanning system is developed to improve the quality and repeatability of image acquisition. A practical scanning path generation algorithm is proposed for autonomous breast scanning, which is applicable to linear, radial and anti-radial scanning protocol. Furthermore, a novel hybrid admittance control method is proposed, incorporating force-deformation characteristics of breast and a transition module to address force fluctuations and overshoot. Experimental results demonstrate that the proposed method effectively improves the smoothness and accuracy of scanning paths. During the contact collision stage, probe-breast interaction force exhibits a slow initial buildup, rapid mid-phase acceleration, and final asymptotic stabilization, which is more physiologically compatible with human interaction. Force tracking performance further validates the effectiveness of the proposed method, with maximum errors of 0.1265 N (T=0.008 s) and 0.1810 N (T=0.016 s), and minimum errors of -0.1120 N (T=0.008 s) and -0.1210 N (T=0.016 s) respectively. During the anti-radial scanning experiment, the mean confidence of the acquired images remains close to 0.5, while the confidence weighted barycentre remains around 0, indicating that the quality of ultrasound image is both good and stable.

Hybrid transaction/analytical processing (HTAP) is an emerging database paradigm that supports both online transaction processing (OLTP) and online analytical processing (OLAP) workloads. Computing-intensive OLTP operations, involving row-wise data manipulation, are suitable for row-store format. In contrast, memory-intensive OLAP operations, which are column-centric, benefit from column-store format. This data-format dilemma prevents HTAP systems from concurrently achieving three design goals: performance isolation, data freshness, and workload-specific optimization. Another background technology is Processing-in-Memory (PIM), which integrates computing units (PIM units) inside DRAM memory devices to accelerate memory-intensive workloads, including OLAP. Our key insight is to combine the interleaved CPU access and localized PIM unit access to provide two-dimensional access to address the data format contradictions inherent in HTAP. First, we propose a unified data storage format with novel data alignment and placement techniques to optimize the effective bandwidth of CPUs and PIM units and exploit the PIM's parallelism. Second, we implement the multi-version concurrency control (MVCC) essential for single-instance HTAP. Third, we extend the commercial PIM architecture to support the OLAP operations and concurrent access from PIM and CPU. Experiments show that PUSHtap can achieve 3.4X/4.4X OLAP/OLTP throughput improvement compared to multi-instance PIM-based design.

Expanding the input bandwidth of the human-machine interface to capture more control intentions from the human is key to achieving dexterous control of multi-degree-of-freedom prosthetic hands/wrists. This paper presents a wearable intelligent prosthesis system based on multimodal fusion, which integrates voice interaction, myoelectric control, limb movement decoding, and computer vision-based environmental perception. The system supports intention estimation throughout the entire process from grasping to operation, enabling synchronized control of 4 grasp gestures and 2 wrist DOFs. Moreover, all these decisions are made automatically during the user’s natural and continuous body movements. Six able-bodied subjects and two amputee subjects participated in a comparative experiment involving multi-object grasping and operation in a cluttered environment. Compared to myoelectric pattern recognition, our method demonstrated significant advantages in improving grasp and operation efficiency (reducing total time, grasp time, and operation time by 67.62%, 67.89%, and 67.40% for able-bodied subjects, and by 73.93%, 69.82%, and 76.67% for amputees). It also reduced control burden, with gesture and wrist myocontrol times decreasing by 71.77% and 100% in able-bodied subjects, and by 80.59% and 100% in amputees. These advantages are not limited to the grasp object, grasp part, operation type, or the subject involved. Compared to other semi-autonomous control methods, our method achieved a higher reduction in time metrics, with reduction rates ranging from 22.61% to 84.82% higher, resulting in a more significant performance improvement. Furthermore, the questionnaire showed that the system was well-recognized by the subjects in terms of operational robustness, wearing comfort, and user experience.

A framework is proposed in this paper for learning variable impedance in percutaneous puncture surgery, with the aim of simplifying the robotic puncture of soft tissues. The framework involves simulating the dynamic changes that occur when the human arm interacts with human tissues and transferring the resulting adaptive capabilities to the robot through learning movement trends and stiffness changes. To enhance performance during task execution, we integrate the variable impedance control framework with the interactive operation and feedback controllers. To provide flexibility for trajectory modification during operation, derivative Gaussian processes are introduced to identify the target position and obtain a model of motion trends. This control law is combined with virtual dynamics that describe puncture dynamics, enabling the robot to regulate interactions and plan its trajectory. We present experiments involving tissue puncturing tasks performed by the Franka-Emika Panda robot with varying degrees of hardness. The results demonstrate that our framework is capable of learning manipulation skills for physical interaction with humans, thereby reducing application complexity in tasks involving complex force interactions for robots. Compared to using fixed or variable impedance gain controllers, our approach effectively improves the success rate, stability, and efficiency of percutaneous puncture. Note to Practitioners—This paper is motivated by the limitations encountered by robots when handling deformed objects. In traditional robot control processes, the assumption of a fixed and unchanging contact object poses a significant challenge in applying robot control to the medical industry. Consequently, it becomes imperative for robot control systems to develop stable intelligent approaches capable of interacting with deformed objects. In this paper, we propose a framework for robot-assisted puncture that combines robotic impedance control techniques with sensing mechanisms. By integrating these approaches, our framework demonstrates effectiveness in performing tasks involving soft tissues with varying levels of hardness. Our proposed method encompasses three main ideas: 1) Sensing muscle activity during task execution enables the acquisition of task parameters from the human arm. 2) The utilization of a robot control method enhances the stability of the robot’s execution process. 3) The proposed method shows potential for application in processing and treating objects with low stiffness, deformed objects, and thin-walled parts. Experimental results validate the effectiveness of the developed method. In future work, it is important for the robot-assisted puncture system to consider recognizing and localizing more diverse targets to enhance its generalization capabilities.

Traditional breast cancer surgeries require collaboration between ultrasound (US) doctors and surgeons, making the procedure complex and treating physicians prone to fatigue. In leader-follower robotic surgery, a surgeon controls an US robotic arm and an instrument robotic arm with their left and right hands, enabling independent surgical performance. However, the lack of US scanning skills among surgeons, as well as the physical separation in leader-follower operations, can negatively impact both the scanning and surgical outcomes. This paper proposes a robot-assisted scheme based on dynamic arm impedance compensation (IC) that references expert arm stiffness to compensate for novice arm stiffness. The impedance compensator adjusts the compensation strategy according to the scanning area and scanning stage. The impedance force generator estimates the scanning direction via Kalman filtering and applies stiffness and damping forces in the vertical direction to suppress tremors and other involuntary movements. The experimental results revealed that during the coarse and fine scanning phases, the probe position variance decreased by 57.9% and 73.6%, the contact force variance decreased by 55.2% and 42.5%, and the US image confidence increased by 22.0% and 23.8%, respectively. Compared with traditional filtering compensation (FC) schemes, this approach reduces the average position variance and contact force variance by 32.0% and 25.3%, respectively, and increases confidence by 7.3%. In a no-compensation test, the IC training group outperformed the FC group. This scheme can assist leader-follower US scanning and rapidly improve surgical skills.

Soft robotic gloves can not only provide timely, effective, safe and cheap rehabilitation training for patients with impaired movement function of hand, but also assist in completing daily grasping activities. However, most soft robotic gloves are completely composed of flexible structures. Although they have high flexibility and safety, there are problems such as poor fit and low output force. In order to solve these problems, this paper refers to the structure of the human hand and designs an articulated rigid-soft hybrid robotic glove, which combines the advantages of rigid robotic gloves and soft robotic gloves, and has high flexibility, high output force and good fit. In addition, soft robotic gloves generally lack the ability to sense the force between the human hand and the glove. Therefore, this paper designed an array flexible force sensor, and studied the structure, signal acquisition and preparation process of the sensor. Finally, a complete test platform was built to test the performance of the rigid-soft hybrid robotic glove with force sensing function. The test results show that the robotic glove has good fit and high output force, can effectively assist training and assist grasping, and can perceive the contact force.

This work presents a humanoid soft robotics finger design with rigid skeletons and proprioceptive sensors. This 4-DOFs dexterous finger has soft joints and rigid phalanxes, which is about the size of human hand. To enhance the overall stiffness and for human-like behavior and configuration, rigid-soft actuators which we called quasi-joints are introduced. Although their lengths are shortened in this design, the soft actuators can still bend over 90°, exhibiting joint-like flexion and abduction/adduction. Thus interphalangeal joints (IPs) and metacarpophalangeal joint (MCP) are realized. EGaIn soft sensors are embedded into the structure for bending detection. In addition, multi-step molding fabrication method is introduced for this complex multi-material structure. This rigid-soft finger is a preliminary work and modular part of a highly dexterous humanoid soft robotic hand.

For a multi-fingered robot hand, the individual control over single joints cannot guarantee their fine collaboration. For achieving a high-precision synchronization, a theory of synchronous control is introduced to multi-fingered robot hands. This paper introduced a new model-free and cross-coupling control strategy. It had been tested on the humanoid robot fingers and showed high positioning performance. For realizing the mutual influence between the control of all joints, we establish the synchronization error by the differential disposal of adjacent actuator errors, then position errors and synchronization errors are incorporated into a unified control frame. Meanwhile, considering the complex dynamic formulations of the dexterous hand and the characteristics of the control system, a model-free, cross-coupled trajectory tracking method is introduced and the explicit dynamic modeling parameters is not necessary. Finally, we tested our method on a multi-fingered hand platform HIT/DLR-II. The results prove that the new method has superior performance over traditional non-synchronous approaches.

How to design a robotic hand for reproducing the move characteristics of human hand joints is a big challenge in robotics. In this paper, we present an approach to determine the actuation configuration based on the statistical results of hand joint angle in different grasps. A relationship between the basic statistical metrics and actuation configuration strategies is built. In this case, a novel actuation configuration is proposed and the joints of four fingers are arranged into five actuation modules. For the mechanical implementation, the motion of human four finger joints is decomposed to proportion motion, differential motion and chain proportion motion, mechanically implemented by pulley, planetary gear differential module and gear transmission chain. Finally, the implemented mechanism is embedded in palm, and the mechanical implementation to the human hand move characteristics is verified by the measured joint angles of the robotic hand when actuators separately move along PC1 and PC2. Meanwhile, the robotic hand can grasp different objects with a versatile grasp function.

Based on HIT/DLR (Harbin Institute of Technology/Deutsches Zentrum für Luft- und Raumfahrt) Prosthetic Hand II, an anthropomorphic controller is developed to help the amputees use and perceive the prosthetic hands more like people with normal physiological hands. The core of the anthropomorphic controller is a hierarchical control system. It is composed of a top controller and a low level controller. The top controller has been designed both to interpret the amputee's intensions through electromyography (EMG) signals recognition and to provide the subject-prosthesis interface control with electro-cutaneous sensory feedback (ESF), while the low level controller is responsible for grasp stability. The control strategies include the EMG control strategy, EMG and ESF closed loop control strategy, and voice control strategy. Through EMG signal recognition, 10 types of hand postures are recognized based on support vector machine (SVM). An anthropomorphic closed loop system is constructed to include the customer, sensory feedback system, EMG control system, and the prosthetic hand, so as to help the amputee perform a more successful EMG grasp. Experimental results suggest that the anthropomorphic controller can be used for multi-posture recognition, and that grasp with ESF is a cognitive dual process with visual and sensory feedback. This process while outperforming the visual feedback process provides the concept of grasp force magnitude during manipulation of objects.

A five-fingered, multi-sensory biomechatronic hand with sEMG interface is presented. The cambered palm is specially designed to enhance the stability while grasping. The location of the thumb is designed by maximizing interaction area between the thumb and other fingers. The opposite thumb could grasp along a cone surface, while maintaining its function. By taken the advantage of coupling linkage mechanism, each finger with three phalanges could fulfill flexion-extension movement independently. Besides, each finger is equipped with torque and position sensors. Thus, the cosmetics and dexterity are improved remarkably compared to conventional prosthesis. The hardware architecture is divided into control system and EMG signal processing system. Moreover, a novel two-stage decision strategy combing the position-based impedance control scheme is implemented to realize the real-time sEMG control of the hand. According to the grasp experiment results, the hand can accomplish several grasp modes stably; the success rate of 10 modes is up to 90%.

The multi-DOF prosthetic hand's myocontrol needs to recognize more hand gestures (or motions) based on myoelectric signals. This paper presents a classification method, which is based on the support vector machine (SVM), to classify 19 different hand gesture modes through electromyographic (EMG) signals acquired from six surface myoelectric electrodes. All hand gestures are based on a 3-DOF configuration, which makes the hand perform like three-fingered. The training performance is very high within each test session, but the cross-session validation is typically low. Acceptable cross-session performance can be achieved by training with more sessions or fewer gesture modes. A fast rhythm muscle contraction is suggested, which can make the training samples more resourceful and improve the prediction accuracy comparing with a relative slow muscle contraction method. For many precise grasp tasks, it is beneficial to the prosthetic hand's myocontrol if we can efficiently extract the grasp force directly from EMG signals. Through grasping a JR3 6 dimension force/torque sensor, the force signal applying to the sensor can be recorded synchronously with myoelectric signals. This paper uses three methods, local weighted projection regression (LWPR), artificial neural network (ANN) and SVM, to find the best regression relationship between these two kinds of signals. It reveals that the SVM method is better than ANN and LWPR, especially in the case of cross-session validation. Also, the performance of grasping force estimation based on specific hand gestures is superior to the performance of grasping with random fingers.

This paper gives an overview of the Chinese intelligent space robotic system. The system consists of a 6 DOF robot arm, a large-error tolerated gripper with two stereo cameras and onboard computer. The robot arm is composed of six identical modular joints, a big central hole in the modular joint was designed for the placement of the cables and plugs in the robot arm, which prevented them from damage of high temperature, radiation in the space environment and the motion of the robot. Joint torque sensor, joint position sensor and temperature sensors etc.., were integrated into the fully modular multi-sensory joint, which made the joint more intelligent. Also, a large-error tolerated gripper with two stereo cameras has been also designed to capture a microtarget satellite (MTS). A fault-tolerant onboard computer (OBC) with dual processing modules has been developed for the robot control. A zero gravity experimental system was developed to verify the functions of the robot arm under zero gravity environment

Based on under-actuated mechanism and coupling principle, a five-fingered, multi-sensory and biomechatronic prosthetic hand has been designed. The multi-DOF hand comprises 13 joints and is controlled by 3 motors. Actuated by only one motor, the thumb can move along a cone surface which is superior in the appearance. Also driven by one motor and transmitted by springs, the mid finger, the ring finger and the little finger can move simultaneously and envelop objects with complex shape. On the other hand, during the hand designation, the handsome appearance has been considered and its glove prototype has been designed. The hardware system and the sensory system have been developed. Through Bluetooth wireless protocol, the hand can be controlled by voice signal. Furthermore, it can also be controlled by electromyography (EMG) signal like most prosthetic hand in existence. It has been verified by experiments that the hand has strong capability of self-adaptation grasp and can accomplish precise and power grasp

A new five-fingered underactuated prosthetic hand control system is presented in this paper. The prosthetic hand control part is based on an EMG motion pattern classifier which combines VLR (variable learning rate) based neural network with wavelet transform and sample entropy. This motion pattern classifier can successfully identify flexion and extension of the thumb, the index finger and the middle finger, by measuring the surface EMG signals through three electrodes mounted on the flexor digitorum profundus, flexor pollicis longus and extensor digitorum. Furthermore, via continuously controlling single finger's motion, the prosthetic hand can achieve more prehensile postures such as power grasp, fingertip grasp, etc. The experimental results show that the classifier has a great potential application to the control of bionic man-machine systems because of its high recognition capability

This paper presents a surface Electromyography (EMG) motion pattern classifier which combines Levenberg-Marquardt (LM) based neural network with parametric Autoregressive (AR) model. This motion pattern classifier can successfully identify three types of motion of thumb, index finger and middle finger, by measuring the surface EMG through two electrodes mounted on the flexor digitorum profundus and flexor pollicis longus. Furthermore, via continuously controlling single finger’s motion, the five-fingered underactuated prosthetic hand can achieve more prehensile postures such as power grasp, centralized grip, fingertip grasp, cylindrical grasp, etc. The experimental results show that the classifier has a great potential application to the control of bionic man-machine systems because of its fast learning speed, high recognition capability and strong robustness.

This paper presents the current work progress of HIT/DLR Dexterous Hand. Based on the technology of DLR Hand II, HIT and DLR are jointly developing a smaller and easier manufactured robot hand. The prototype of one finger has been successfully built. The finger has three DOF and four joints, the last two joints are mechanically coupled by a rigid linkage. All the actuators are commercial brushless DC motors with integrated analog Hall sensors. DSP based control system is implemented in PCI bus architecture and the serial communication between the hand and DSP needs only 6 lines(4 lines power supply and 2 lines communication interface). The fingertip force can reach 10N.