Robot Arm Research Articles

Adversarial generative learning and timed path optimization for real-time visual image prediction to guide robot arm movements

Adversarial generative learning and timed path optimization for real-time visual image prediction to guide robot arm movements

Apple-Harvesting Robot Based on the YOLOv5-RACF Model.

To address the issue of automated apple harvesting in orchards, we propose a YOLOv5-RACF algorithm for identifying apples and calculating apple diameters. This algorithm employs the robot operating dystem (ROS) to control the robot's locomotion system, Lidar mapping, and navigation, as well as the robotic arm's posture and grasping operations, achieving automated apple harvesting and placement. The tests were conducted in an actual orchard environment. The algorithm model achieved an average apple detection accuracy (mAP@0.5) of 98.748% and a (mAP@0.5:0.95) of 90.02%. The time to calculate the diameter of one apple was 0.13 s, with a measurement accuracy within an error range of 1-3 mm. The robot takes an average of 9 s to pick an apple and return to the initial pose. These results demonstrate the system's efficiency and reliability in real agricultural environments.

RobotSDF: Implicit Morphology Modeling for the Robotic Arm.

The expression of robot arm morphology is a critical foundation for achieving effective motion planning and collision avoidance in robotic systems. Traditional geometry-based approaches usually suffer from the contradiction between the high demand for computing resources for fine expression and the insufficient detail expression caused by the pursuit of efficiency. The signed distance function addresses these drawbacks due to its ability to handle complex and arbitrary shapes and lower computational requirements. However, conventional robotic morphology methods based on the signed distance function often face challenges when the robot moves dynamically, since robots with different postures are modeled as independent individuals but the postures of robots are infinite. In this paper, we introduce RobotSDF, an implicit morphology modeling approach that can express the robot shape of arbitrary posture precisely. Instead of depicting a whole model of the robot arm, RobotSDF models the robot morphology as integrated implicit joint models driven by joint configurations. In this approach, the dynamic shape change process of the robot is converted into the coordinate transformations of query points within each joint's coordinate system. Experimental results with the Elfin robot demonstrate that RobotSDF can accurately depict robot shapes across different postures up to the millimeter level, which exhibits 38.65% and 66.24% improvement over the Neural-JSDF and configuration space distance field algorithms, respectively, in representing robot morphology. We further verified the efficiency of RobotSDF through collision avoidance in both simulation and actual human-robot collaboration experiments.

Modeling and Rotation Control Strategy for Space Planar Flexible Robotic Arm Based on Fuzzy Adjustment and Disturbance Observer

In precise space operation tasks, the impact of disturbing torques on the space flexible robotic arm (SFRA) cannot be ignored. Besides, the slender structure of the SFRA is very likely to generate vibration of the robotic arm. These are all potential hidden dangers in space safety. To quantify the potential risk, an accurate dynamics model of the SFRA considering the disturbing torque is built by Lagrange principle and the assumed modal method (AMM). Moreover, the effects of the disturbing torque, modal order and nonlinear terms on the deformation accuracy of the SFRA are compared. It is observed that the simplified dynamics model with neglecting the nonlinear terms (NNTs) has a high model accuracy and be easily solved. Therefore, the NNTs simplified model is chosen for deriving the transfer function of the SFRA. The parameters of the PI controller are adjusted in real time based on fuzzy rules to reduce the tracking error in the SFRA. In addition, the disturbance observer is designed to observe and compensate of the disturbance torque in the SFRA. The control method of adjusting controller parameters with fuzzy rules based on the disturbance observer greatly improves the rotational control accuracy of the SFRA. Finally, the validity of aforementioned control strategy is confirmed by simulation analysis and experimental results.

A new gap balancing technique with functional alignment in total knee arthroplasty using the MAKO robotic arm system: a preliminary study

BackgroundGap tension is an important factor influencing the clinical outcomes of total knee arthroplasty (TKA). Traditional mechanical alignment (MA) places importance on neutral alignment and often requires additional soft tissue releases, which may be related to patient dissatisfaction. Conversely, the functional alignment requires less soft tissue release to achieve gap balance. Conventional gap tension instruments present several shortcomings in practice. The aim of this study is to introduce a new gap balancing technique with FA using the modified spacer-based gap tool and the MAKO robotic arm system.MethodsA total of 22 consecutive patients underwent primary TKA using the MAKO robotic arm system. The gap tension was assessed and adjusted with the modified spacer-based gap tool during the operation. Patient satisfaction was evaluated post-operatively with a 5-point Likert scale. Clinical outcomes including lower limb alignment, Knee Society Score (KSS) and Western Ontario and McMaster Universities Arthritis Index (WOMAC) were recorded before surgery, 3 months and 1 year after surgery.ResultsThe range of motion (ROM) was significantly increased (p < 0.001) and no patients presented flexion contracture after the surgery. KSS and WOMAC score were significantly improved at 3 months and 1 year follow-up (p < 0.001 for all). During the surgery, the adjusted tibial cut showed more varus than planned and the adjusted femoral cut presented more external rotation than planned (p < 0.05 for both). The final hip-knee-ankle angle (HKA) was also more varus than planned (p < 0.05).ConclusionsThis kind of spacer-based gap balancing technique accompanied with the MAKO robotic arm system could promise controlled lower limb alignment and improved functional outcomes after TKA.

RANCANG BANGUN TRAINABLE ROBOT ARM BERBASIS ARDUINO MEGA 2560 SEBAGAI MEDIA PRAKTIKUM DI LABORATORIUM TEKNIK ELEKTRO UNIVERSITAS PEMBANGUNAN PANCABUDI MEDAN

The design of the Trainable Robot Arm is carried out as a learning medium in the lab. Electrical Engineering University of Development Pancabudi Medan. Trainable Robot Arm is a robotic arm designed to facilitate the process of robot applications in carrying out object transfer tasks without the need to do complex programming every time you want to change the robot's movements. The design of this arm robot is divided into 3 parts, the first is the design and manufacture of robot mechanics, the second is the design and manufacture of robot control/electricity and the third is the manufacture of robot software/programs. The mechanical design of the robot begins with making a 3D design of the robot frame utilizing the non-commercial version of the Fusion360 CAD software, then the 3D design is printed using a 3D Printing machine with PETG (Polyethylene Terephthalate) plastic material. The robot arm is designed to have a 5-DOF (Deegre of Freedom) system, namely Base, Shoulder, Elbow, Wrist and Gripper. As a drive, 5 servo motors with torque are used each, 25 kg/cm for Base and Shoulder, 11kg/cm for Elbow and Wrist, and 1.8 kg/cm for Gripper. From the test results, the Trainable robot arm successfully stores and repeats movements that have been trained to move objects with a mass of 100 grams at a position of 23cm from the axis of the robot arm to the x-axis to a position of 20cm from the axis of the robot to the y-axis as many as 8 repetitions without failure.

Arduino-Controlled Multi-Function Robot with Bluetooth and nRF24L01+ Communication

This paper outlines the design and development of an advanced robotic system that integrates hardware implementation with theoretical simulation to address the need for versatile and user-friendly robotic solutions in various environments. Addressing the issue of limited adaptability in existing robotic systems, we propose a wireless, voice and gesture-controlled robot car with an integrated robotic arm capable of performing complex tasks such as line following, obstacle avoidance, object manipulation, and autonomous navigation over one-kilometer range. To improve operational efficiency and user involvement, this paper designs a multifunctional robotic platform that integrates user-friendly control interfaces with inexpensive, state-of-the-art sensor technologies. To achieve this, we integrate a variety of sensors, including ultrasonic sensors for precise distance measurement, infrared sensors for object detection and line following, an L298 motor driver for controlling geared motors, servo motors for controlling robotic arms, a flex sensor for claw control, and an mpu6050 accelerometer for gesture recognition. The system also uses a custom-made Bluetooth app for remote control, nRF24L01+ for long-range wireless control, and Arduino Mega and Nano for processing and control functions. The results demonstrate the robot functions well in dynamic conditions, and it can be used in hospitals to assist healthcare professionals, in restaurants for food delivery, and in industrial settings for object manipulation. The system’s design proves robust in real-world scenarios, offering significant improvements in accessibility and operational efficiency. This study aligns with Sustainable Development Goals (SDGs) 3 (Good Health and Well-being), 9 (Industry, Innovation, and Infrastructure), and 17 (Partnerships for the Goals). The robotic arm's potential application in healthcare settings advances SDG 3, its contribution to industrial productivity advances SDG 9, and collaborations with tech companies to expand and improve the robot's capabilities promote SDG 17.

Development and Validation of a New Type of Displacement-Based Miniatured Laser Vibrometers.

Developing a miniatured laser vibrometer becomes important for many engineering areas, such as experimental and operational modal analyses, model validation, and structural health monitoring. Due to its compact size and light weight, a miniatured laser vibrometer can be attached to various mobilized platforms, such as an unmanned aerial vehicle and a robotic arm whose payloads can usually not be large, to achieve a flexible vibration measurement capability. However, integrating optics into a miniaturized laser vibrometer presents several challenges. These include signal interference from ghost reflectance signals generated by the sub-components of integrated photonics, polarization effects caused by waveguide structures, wavelength drifting due to the semiconductor laser, and the poorer noise characteristics of an integrated laser chip compared to a non-integrated circuit. This work proposes a novel chip-based high-precision laser vibrometer by incorporating two or more sets of quadrature demodulation networks into its design. An additional set of quadrature demodulation networks with a distinct reference arm delay line length can be used to conduct real-time compensation to mitigate linear interference caused by temperature and environmental variations. A series of vibration measurements with frequencies ranging from 0.1 Hz to 1 MHz were conducted using the proposed laser vibrometer to show its repeatability and accuracy in vibration and ultrasonic vibration measurements, and its robustness to test surface conditions. The proposed laser vibrometer has the advantage of directly measuring the displacement response of a vibrating structure rather than integrating its velocity response to yield the measured displacement with a conventional laser Doppler vibrometer.

Rapid, autonomous and ultra-large-area detection of latent fingerprints using object-driven optical coherence tomography

The detection of latent fingerprints plays a crucial role in criminal investigations and biometrics. However, conventional techniques are limited by their lack of depth-resolved imaging, extensive area coverage, and autonomous fingerprint detection capabilities. This study introduces an object-driven optical coherence tomography (OD-OCT) to achieve rapid, autonomous and ultra-large-area detection of latent fingerprints. First, by utilizing sparse sampling with the robotic arm along the slow axis, we continuously acquire B-scans across large, variably shaped areas (∼400 cm2), achieving a scanning speed up to 100 times faster. In parallel, a deep learning model autonomously processes the real-time stream of B-scans, detecting fingerprints and their locations. The system then performs high-resolution three-dimensional imaging of these detected areas, exclusively visualizing the latent fingerprints. This approach significantly enhances the imaging efficiency while balancing the traditional OCT system's trade-offs between scanning range, speed, and lateral resolution, thus offering a breakthrough in rapid, large-area object detection.

SUPER-MAN: SUPERnumerary robotic bodies for physical assistance in huMAN–robot conjoined actions

This paper presents a mobile supernumerary robotic approach to physical assistance in human–robot conjoined actions. The study starts with the description of the SUPER-MAN concept. The idea is to develop and utilize mobile collaborative systems that can follow human loco-manipulation commands to perform industrial tasks through three main components: (i) an admittance-type interface, (ii) a human–robot interaction controller and (iii) a supernumerary robotic body. Next, we present two possible implementations within the framework — from theoretical and hardware perspectives. The first system is called MOCA-MAN, and is composed of a redundant torque-controlled robotic arm and an omni-directional mobile platform. The second one is called Kairos-MAN, formed by a high-payload 6-DoF velocity-controlled robotic arm and an omni-directional mobile platform. The systems share the same admittance interface, through which user wrenches are translated to loco-manipulation commands, generated by whole-body controllers of each system. Besides, a thorough user-study with multiple and cross-gender subjects is presented to reveal the quantitative performance of the two systems in effort demanding and dexterous tasks. Moreover, we provide qualitative results from the NASA-TLX questionnaire to demonstrate the SUPER-MAN approach’s potential and its acceptability from the users’ viewpoint.

Exploring Cognitive Workload Changes in Teleoperators: Collaboration with On-Site Workers and Robots in Assembly Tasks

Teleoperation is commonly employed to perform industrial tasks in remote or inaccessible areas. However, there is a noticeable gap in evaluating cognitive workload of teleoperators in collaboration environments. This study compared the variation of cognitive workload for teleoperators guiding an on-site participant to complete a wire assembly task in two scenarios: one aided by a robot arm (tRH) and another without any robot assistance (HH). Additionally, the task demands for on-site participants were manipulated to measure its impact on teleoperator’s workload. NASA-TLX and EEG activity were utilized to assess workload. The results indicated that EEG theta activity was significantly higher for the HH group than tRH group, potentially showing lower workload for teleoperators in the scenario with robot assistance. Task difficulty did not affect any of the workload measures. The study highlights the importance of cognitive workload assessment in human-robot collaborations to optimize human cognitive demands in complex settings.

Development and application of a mechanical arm-based in situ 3D bioprinting method for the repair of skin wounds

Current treatments for skin wounds typically involve multiple surgical procedures that require complex processes and expensive costs, making it difficult to achieve timely treatment in field environments. We developed an innovative in situ printing method, utilizing robotic arm control, to address the significant challenges of large-scale skin wound repair resulting from natural disasters such as earthquakes, fires, and explosions during relief efforts. Our portable 3D printing equipment, which integrates debridement, precise 3D scanning and modeling of wounds, and compatibility with cell-loaded bioink, facilitates rapid repair of large-area skin wounds in specialized field environments. Compared with traditional methods, this in situ printing method has significant advantages, including the ability to customize treatment according to the unique needs of the wound, achieve rapid healing, and the potential to reduce the total cost. We conducted experiments on rats with full-thickness dorsal skin defects and compared the performance of in situ bioprinting method with commercial skin defect repair dressings. Our results demonstrate that the in situ bioprinted skin achieved faster wound healing and more uniform re-epithelialization than the commercial dressing treatment. This study demonstrates the potential of in situ bioprinting method as a promising and effective strategy for rapid skin wound healing, especially for patients in remote environments where traditional wound treatment methods may not be readily available or practical.

Optimizing parameters of YOLO model through uniform experimental design for gripping tasks performed by an internet of things–based robotic arm

The booming development of automation in industry has seen robotic arms replace much of manual labor for tasks such as casting, processing, packaging, and gripping on production lines. The Internet of Things (IoT) framework enables machines to transmit data over networks, and combining it with artificial intelligence can create smarter systems with higher operational efficiency and quality. However, artificial intelligence models need to be optimized for different applications. This paper proposes a You Only Look Once–uniform experimental design (YOLO–UED) model for gripping tasks performed by an IoT-based robotic arm. The YOLO–UED model was designed by combining the YOLOv4 model with UED to optimize the model architecture, resulting in improved performance in various applications. Considering the huge expense of computational resources required for visual inspection with robotic arms, pairing each robotic arm with a high-performance computing device would substantially increase costs. This study proposed an IoT framework to transmit the images captured by the robotic arm to a computing server for object recognition. Utilizing the IoT framework helps reduce costs and provides scalability and flexibility in handling computational tasks. The proposed method was found to effectively enhance the model's mean average precision to 95 %. The YOLO–UED model exhibited 7–10 % improvement over the YOLOv4 model in terms of target recognition accuracy. Moreover, the proposed method attained a success rate of 90% in gripping tasks performed on objects placed at various angles.

Designing and Implementing a 3DOF Robotic Arm for Color Sorting and Object Identification Using Computer Vision Technology

This paper focuses on designing a three-degree-of-freedom robotic arm for color sorting using computer vision technology. It involves the use of a robotic arm composed of three joints and a gripper for grasping and manipulating objects. Each joint is actuated by a servo motor that is controlled by an Arduino microcontroller to control the angles of the joints and achieve the desired motion. The microcontroller relies on a camera to detect and analyze colors. The camera captures an image of the objects present in the designated area and converts it into digital data to be processed later by a computer. Then, the well-known (OpenCV) and (NumPy) library in the Python programming language is used to process this data and identify the colors. Once the color is determined, the corresponding Python command is executed through the Arduino microcontroller. That allows the robotic arm to move towards the designated location based on the identified color. Consequently, the arm grasps the detected object and places it in the appropriate position according to its color. The effectiveness of the proposed approach has been verified in real-time experiments.

Three-Dimensional Obstacle Avoidance Harvesting Path Planning Method for Apple-Harvesting Robot Based on Improved Ant Colony Algorithm

The cultivation model for spindle-shaped apple trees is widely used in modern standard apple orchards worldwide and represents the direction of modern apple industry development. However, without an effective obstacle avoidance path, the robotic arm is prone to collision with obstacles such as fruit tree branches during the picking process, which may damage fruits and branches and even affect the healthy growth of fruit trees. To address the above issues, a three-dimensional path -planning algorithm for full-field fruit obstacle avoidance harvesting for spindle-shaped fruit trees, which are widely planted in modern apple orchards, is proposed in this study. Firstly, based on three typical tree structures of spindle-shaped apple trees (free spindle, high spindle, and slender spindle), a three-dimensional spatial model of fruit tree branches was established. Secondly, based on the grid environment representation method, an obstacle map of the apple tree model was established. Then, the initial pheromones were improved by non-uniform distribution on the basis of the original ant colony algorithm. Furthermore, the updating rules of pheromones were improved, and a biomimetic optimization mechanism was integrated with the beetle antenna algorithm to improve the speed and stability of path searching. Finally, the planned path was smoothed using a cubic B-spline curve to make the path smoother and avoid unnecessary pauses or turns during the harvesting process of the robotic arm. Based on the proposed improved ACO algorithm (ant colony optimization algorithm), obstacle avoidance 3D path planning simulation experiments were conducted for three types of spindle-shaped apple trees. The results showed that the success rates of obstacle avoidance path planning were higher than 96%, 86%, and 92% for free-spindle-shaped, high-spindle-shaped, and slender-spindle-shaped trees, respectively. Compared with traditional ant colony algorithms, the average planning time was decreased by 49.38%, 46.33%, and 51.03%, respectively. The proposed improved algorithm can effectively achieve three-dimensional path planning for obstacle avoidance picking, thereby providing technical support for the development of intelligent apple picking robots.

Novel Water Probe for High-Frequency Focused Transducer Applied to Scanning Acoustic Microscopy System: Simulation and Experimental Investigation.

A scanning acoustic microscopy (SAM) system is a common non-destructive instrument which is used to evaluate the material quality in scientific and industrial applications. Technically, the tested sample is immersed in water during the scanning process. Therefore, a robot arm is incorporated into the SAM system to transfer the sample for in-line inspection, which makes the system complex and increases time consumption. The main aim of this study is to develop a novel water probe for the SAM system, that is, a waterstream. During the scanning process, water was supplied using a waterstream instead of immersing the sample in the water, which leads to a simple design of an automotive SAM system and a reduction in time consumption. In addition, using a waterstream in the SAM system can avoid contamination of the sample due to immersion in water for long-time scanning. Waterstream was designed based on the measured focal length calculation of the transducer and simulated to investigate the internal flow characteristics. To validate the simulation results, the waterstream was prototyped and applied to the TSAM-400 and W-FSAM traditional and fast SAM systems to successfully image some samples such as carbon fiber-reinforced polymers, a printed circuit board, and a 6-inch wafer. These results demonstrate the design method of the water probe applied to the SAM system.

Эффективность использования новой роботической системы Versius для выполнения операций у детей: результаты клинических испытаний

Authors represent the Russia’s first research demonstrating the results of the use of the Versius modular &amp; portable surgical robot by “CMR Surgical,” Ltd. (Cambridge, Cambridgeshire, United Kingdom) in surgical interventions on pediatric patients that they’ve obtained as part of the clinical trials complex approved by the Federal Service for Surveillance in Healthcare of Russia (“Roszdravnadzor”). The purpose of this research was to obtain practical evidence of the clinical safety and effectiveness of the use of the new Versius® Surgical Robotic System in minimally invasive surgical interventions on children. The criteria for the safety and effectiveness of the method were the absence of complications caused by the Surgical Robotic System (SRS) operation or the surgical technique as well as the absence of dropping into either laparoscopy or open surgery technique. Material and methods used: non-randomized single-center controlled trial had been carried out in children aged 1 to 18 y/o. Areas of application for surgical procedures included urology, abdominal and thoracic surgeries. Results: the number of patients who had undergone the robot-assisted surgeries had totaled 55 (27 (49%) m/28 (51%) f). The patient’s median age of the surgical intervention was 11.5 [7.0; 15.0] y/o; the median weight was 45.5 [27.0; 59.0] kilograms; the median surgical intervention duration was 140.0 [85.0; 175.0] minutes; patients’ stay in the intensive care unit had averaged for 23.0 [19.0; 24.0] hours; the average hospitalization duration was 8.0 [4.0; 14.0] days. Surgical interventions weren’t accompanied by any of the intraoperative complications such as bleeding, organ perforation and/or injury to adjacent anatomical structures. Conversions to either laparoscopic or open surgery technique also weren’t recorded. The equipment technical problems with SRS such as sudden system power-off, synchronous interaction of joysticks/manipulators interruption, robotic arms blocking, manipulators’ uncontrolled movement and/or traumatic effects on the tissues of the abdominal wall/chest weren’t recorded as well. Conclusion: the preliminary results obtained from the clinical trials complex affirm the potential ability of the Versius® Surgical Robotic System to performing robotic surgical interventions on pediatric patients aged 1 to 18 years old.

Design and Characterization of Printed Flexible Humidity Sensor

The development of humidity sensors is essential for applications in the environmental, agriculture, medical and semiconductor industries [1-2]. The basic mechanism of humidity sensors is explained by a Grotthuss chain reaction, which is usually simplified as a proton-hopping process [3]. This refers to a dynamic charge transfer process at a certain relative humidity (RH). The humidity sensors can be of different types such as capacitive type, resistive, colorimetric and surface acoustic wave [4-7]. Capacitive type sensors are linear, require less complex circuitry and can operate for a wide range of humidity [8]. A typical capacitive type humidity sensor uses hydrophilic films as sensing layers which are stable at higher humidity levels [9]. Polymers that are being used for the fabrication of capacitive type humidity sensors include poly (2-hydroxyethyl methacrylate) (pHEMA) [9], cellulose acetate butyrate (CAB) [10], polyimide [11] and polymethyl methacrylate (PMMA) [12]. In this work, a humidity sensor has been fabricated on glass and flexible substrates.The design of humidity sensor consists of printed pattern of a conductor such as silver and a spin coated dielectric such as polymer poly (2-hydroxyethyl methacrylate) (p-HEMA) and polyimide. The printing of conductor was done in an interdigitated pattern as shown in figure 1. The thickness of sensitive materials was controlled by varying the spin time from 10 to 30 seconds. A thick solution of sensitive materials was prepared by adding a 3 gm of polymer in 50 mL ethanol in the case of p-HEMA. Currently we are preparing one more set of samples using polyimide and we will present a comparison of two humidity sensors- one with p-HEMA and another with polyimide. A humidity sensor that employs interdigitated capacitors (IDC) printed with silver on a glass and flexible substrate was successfully fabricated using p-HEMA and polyimide at spin speed of 2000 rpm for 20 seconds. Our goal is to integrate the humidity sensor into a robotic arm to perform real-time object characterization in agricultural applications via comprehensive understanding of the operations of harvesting robots in various crop types and environmental conditions.

High-Capacity Robots in Early Education: Developing Computational Thinking with a Voice-Controlled Collaborative Robot

This study investigates the potential of a voice-controlled collaborative robot to promote computational thinking (CT) skills in preschoolers aged 4 to 6 in Southern Chile. During a week-long workshop, 34 children engaged in activities designed to develop several CT components while interacting with a robotic arm. Pre- and post-workshop assessments utilizing the TechCheck-K tool revealed significant improvements in CT skills. The findings of this preliminary study demonstrate the effectiveness of voice-controlled robots as innovative tools in early education, significantly enhancing young learners’ understanding of CT concepts, practices, and perspectives. This research advocates for further exploration of robotic integration in educational settings, highlighting their potential to improve or extend preschool learning environments and foster CT skill development.

A visual representation of the hand in the resting somatomotor regions of the human brain

Hand visibility affects motor control, perception, and attention, as visual information is integrated into an internal model of somatomotor control. Spontaneous brain activity, i.e., at rest, in the absence of an active task, is correlated among somatomotor regions that are jointly activated during motor tasks. Recent studies suggest that spontaneous activity patterns not only replay task activation patterns but also maintain a model of the body's and environment's statistical regularities (priors), which may be used to predict upcoming behavior. Here, we test whether spontaneous activity in the human somatomotor cortex as measured using fMRI is modulated by visual stimuli that display hands vs. non-hand stimuli and by the use/action they represent. A multivariate pattern analysis was performed to examine the similarity between spontaneous activity patterns and task-evoked patterns to the presentation of natural hands, robot hands, gloves, or control stimuli (food). In the left somatomotor cortex, we observed a stronger (multivoxel) spatial correlation between resting state activity and natural hand picture patterns compared to other stimuli. No task-rest similarity was found in the visual cortex. Spontaneous activity patterns in somatomotor brain regions code for the visual representation of human hands and their use.