Electrical Resistance Tomography (ERT) has emerged as a promising technology for large-area robotic skin due to its ability to reconstruct pressure distribution over extensive regions using a few sparsely distributed electrodes. Despite ERT's potential to reconstruct the external forces applied on 3D surfaces, the uneven distribution of spatial sensitivity leads to significant errors in identifying the physical quantities of contacts, inhibiting this technique from being an effective tactile sensor. To address this issue, this paper proposes a method to equalize the spatial sensitivity by modulating the conductivity of ERT sensors through topology optimization. In a simulation environment, the sensor's conductive domain was converted into a binary image and optimized to equalize spatial sensitivity and reduce disparities between low and highsensitivity areas. Additionally, we present a sensor fabrication method with a complex optimized conductive patch pattern from simulation by applying screen printing techniques. The effectiveness of the implemented spatial sensitivity equalization was validated by comparing it to a conventional ERT sensor in both simulations and real-world environments. The proposed sensitivity optimization method expands the use of ERT-based sensors for distributed tactile sensing in physical human-robot interaction scenarios.

This paper presents a wearable soft sensing band with stretchable sensors to monitorcle activity by estimating muscle volume changes. Unlike conventional surface electromyography (sEMG) sensing techniques, which require excessive pressure or adhesive electrodes, the proposed sensing method allows muscle volume variations to be detected simply by placing the device on the skin without additional pressure or adhesives. The band was evaluated in isometric-static and isometric-varying torque estimation tasks, demonstrating superior accuracy to sEMG, with a relative torque to maximum torque estimation error of less than 11.5%. In isometric-varying conditions, relative torque was estimated with an average error of 10.1% at frequencies of 0.1 Hz, 0.2 Hz and 0.5 Hz. Furthermore, the band achieved a classification accuracy of 92.9% in recognizing ten distinct hand gestures, highlighting its capability to differentiate between multiple muscle activations. The lightweight and flexible design addresses limitations of sEMG, such as signal noise, skin irritation, and complex calibration. Experimental results validate the potential of the proposed sensing method for applications in muscle activity monitoring across healthcare, rehabilitation, and sports, and it also offers potential for use in robot teaching for reference motion generation.

The Koopman operator framework has shown promising results in enabling the analysis of nonlinear dynamics into an infinite-dimensional linear representation. Koopman direct encoding (KDE) is a model-based approach that utilizes inner products and compositions in a Hilbert space to compute the Koopman operator. However, it has primarily been applied to autonomous systems and simulation environments. Here, we extend the application of KDE to nonautonomous systems and real-world environments by introducing Koopman direct encoding-based model predictive control (KDE-MPC). It was validated on nonlinear electromechanical systems with segmented dynamic conditions, such as contact-noncontact transitions, which pose challenges for modeling and control. Simulation results demonstrate a more stable and smoother position profile compared to proportional-integral-derivative control, particularly at discontinuous boundaries. KDE-MPC was also applied to real-world systems, achieving similar position tracking performance to simulation results. We anticipate that KDE-MPC will offer a viable solution for complex robotic control challenges.

Hand gesture recognition (HGR) is crucial in developing advanced prosthetics, neurorobotics, and human-robot interaction (HRI). Surface electromyography (sEMG) and high-density sEMG (HD-sEMG) have gained attention for their ability to capture the muscle activity underlying hand gestures. Although many models achieve high performance within the same subjects, generalizing across different subjects remains a significant challenge, limiting the practical application of these systems in real-world settings. Furthermore, most conventional approaches primarily focus on the steady phase of gestures, which slows down real-time prediction. To address these issues, we propose a cross-subject dynamic hand gesture recognition (DHGR) framework based on the Vision Transformer (ViT) architecture, referred to as ViT-DHGR. Our model focuses explicitly on the signal transient phase before gesture stabilization to reduce gesture prediction latency and counteract system control delays. By incorporating subject embeddings and transfer learning strategies, the proposed ViT-DHGR framework for 34 dynamic hand gestures achieved an accuracy of 76.44% for 10 subjects using only 1 repetition of gesture data, which improved to 85.03% with 2 repetitions. In addition, our proposed framework achieves over 16% higher average accuracy across test subjects using 1 repetition of data compared to training subject-specific models from scratch. This work demonstrates the potential of HD-sEMG for capturing dynamic hand gestures and highlights the benefits of cross-user knowledge transfer in reducing data requirements and enhancing practicality for robotic applications.

When manipulating an object, a robot must recognize not only the parts it directly grasps but also the surfaces in contact with the environment, which we refer to as extrinsic contact surfaces. These surfaces directly affect how the object interacts with its environment, and accurate surface estimation is critical for precise robotic manipulation. This study presents a novel framework for extrinsic contact surface reconstruction using vision-based tactile sensing. By leveraging marker-based tracking and analyzing kinematic constraints, we classify contact types and estimate the locations of both point and line contacts. To reconstruct the extrinsic contact surface, we compare three data integration methods: Mixed Vector Approach (MVA), Orthogonal Distance Regression (ODR), and Random Sample Consensus (RANSAC). Experimental results demonstrate that MVA achieves the highest accuracy in most cases by effectively integrating contact data while minimizing randomness. Experiments conducted on various object geometries validated the robustness of the proposed method, achieving an average positional error of 4.15 mm and an angular deviation of 4.58°. The results confirm that extrinsic contact sensing enables more efficient and precise object shape estimation, providing a promising approach for robotic manipulation.

Electrical impedance tomographic (EIT) tactile sensing holds great promise for whole-body coverage of contact-rich robotic systems, offering extensive flexibility in sensor geometry. However, low spatial resolution restricts its practical use, despite the existing deep-learning-based reconstruction methods. This study introduces EIT-GNN, a graph-structured data-driven EIT reconstruction framework that achieves super-resolution in large-area tactile perception on unbounded form factors of robots. EIT-GNN represents the arbitrary sensor shape into mesh connections, then employs a twofold architecture of transformer encoder and graph convolutional neural network to best manage such the geometrical prior knowledge, resulting in the accurate, generalized, and parameter-efficient reconstruction procedure. As a proof-of-concept, we demonstrate its application using large-area face-shaped sensor hardware, which represents one of the most complex geometries in human/humanoid anatomy. An extensive set of experiments, including simulation study, ablation analysis, single-touch indentation test, and latent feature analysis, confirm its superiority over alternative models. The beneficial features of the approach are demonstrated through its application in active tactile-servo control of humanoid head motion, paving the new way for integrating tactile sensors with intricate designs into robotic systems.

Robotic systems start to coexist around humans but cannot physically interact as humans do due to the absence of tactile sensitivity across their bodies. Various studies have developed a scalable tactile sensor to grant a body-scale robotic skin, yet many faced drawbacks arising from the rapidly increasing number of sensing elements or a limited sensibility to a wide range of touches. This article proposes a body-scale robotic skin composed of multimodal sensing modules and a multilayered fabric, simultaneously utilizing superresolution and tomographic transducing mechanisms. These mechanisms employ fewer sensing elements across a large area and complement each other in perceiving a wide range of stimuli humans can sense. Their measurements are processed to encode spatiotemporal properties of touch, which are decoded by a trained convolutional neural network to classify the touch modality, while their computational costs are minimized for on-device computation. The robotic skin was demonstrated on a commercial robotic arm and interpreted human touches for tactile communication, suggesting its capability as a body-scale robotic skin for further physical interaction.

SUMMARY: With the increasing rates of exome and whole genome sequencing, the ability to classify large sets of germline sequencing variants using up-to-date American College of Medical Genetics-Association for Molecular Pathology (ACMG-AMP) criteria is crucial. Here, we present Automated Germline Variant Pathogenicity (AutoGVP), a tool that integrates germline variant pathogenicity annotations from ClinVar and sequence variant classifications from a modified version of InterVar (PVS1 strength adjustments, removal of PP5/BP6). This tool facilitates large-scale, clinically focused classification of germline sequence variants in a research setting. AVAILABILITY AND IMPLEMENTATION: AutoGVP is an open source dockerized workflow implemented in R and freely available on GitHub at https://github.com/diskin-lab-chop/AutoGVP.

Hand gesture recognition has received considerable attention as an intuitive interaction method in recent years. This research introduces a new wearable hand gesture recognition system that employs pneumatic mechanomyography (pMMG) to directly monitor the wrist tendon group, which transmits muscle force to the fingers. The experimental findings demonstrate that the proposed method provides raw observations proportional to the finger flexion force, with highR-squared values exceeding 0.94. The performance of the proposed system was evaluated by conducting a hand gesture experiment consisting of 28 hand gestures. The proposed method achieved an average accuracy of 98.12%, surpassing the surface electromyography (sEMG) system's accuracy of 93.89%. Furthermore, the fusion of pMMG and sEMG sensors yielded an accuracy of 99.18%. The results suggest that the proposed approach exhibits the potential to enhance the accuracy and efficiency of hand gesture recognition systems.

Human employs different touch patterns to convey diverse social messages; for example, a stroke is an encouragement, whereas a hit is an offense. Various tactile sensors have been developed to grant an intuitive physical interaction with a robotic system, yet many encountered limitations in achieving broad sensibility or fabricating into a large skin. This paper presents a robotic skin with multimodal tactile sensing modules to achieve broad spatiotemporal sensibility with a few sensing elements. The multimodal module is composed of a microphone and a vented screw installed on a conductive sensory domain. A multilayered fabric with a textured surface covers the sensory domain and forms a piezoresistive structure. High and low temporal components of touch elicit a micro-vibration and a conductivity change on the skin, where both are measured with multimodal modules. The measurements are each processed with short-time Fourier transform (STFT) and electrical resistance tomography (ERT) to encode two spatiotemporal feature maps, which are classified into ten touch classes using a convolutional neural network. Due to a sensibility to both high and low temporal components of touch, the skin classifies touches with an accuracy of 97.0 %, whereas only 84.7 % and 90.6 % are achieved when one type of feature map is used. Also, the skin is robust and beneficial in power consumption and fabrication since the multimodal modules are not exposed to an external stimulus and are sparsely distributed.

A lightweight and compliant manipulator design has been considered crucial in safe physical human–robot interaction. Remote actuation relocating the massive parts to the robot base and transmitting power to the distal joint minimizes the actuator inertia and provides series elasticity to the actuator. Rolling diaphragm hydrostatic transmission (RDHT), one of the remote actuation, has recently been studied in physically interacting robots, which can tackle the remaining issues in hydraulic actuation, such as low backdrivability and fluid leakage. However, existing RDHTs are challenging to achieve the desired safety and control performance simultaneously due to their fixed stiffness. This article presents a stiffness-switchable hydrostatic transmission (SwHST) consisting of an RDHT and valve-controlled pneumatic springs. The SwHST has a wide stiffness range of 15–290 N$\cdot$m/rad and a fast response in stiffness transition of less than 50 ms without any complex stiffness tuning mechanism. It is one of the most efficient transmissions in remote actuation and stiffness adjustment. Its static friction is less than 0.4% of full-range torque, and the stiffness-switching module consumes only 6 W of power when valves are open. The dynamic characteristics of the SwHST are experimentally scrutinized under various operational conditions. Safety performance is verified in unconstrained and constrained collision tests, demonstrating that the SwHST can effectively mitigate the clamping force of more than 50% for both the tests. Control performance is evaluated on position tracking tests. We foresee the proposed SwHST being utilized in human–robot collaboration without jeopardizing control performance through a rapid and efficient stiffness-switching mechanism.

Robots performing close physical interaction with humans would require a continuously variable transmission to operate in the region around the peak efficiency or peak power of the driving system. Conventional continuously variable transmission (CVT) has shown advantages in energy-efficient driving systems. However, these CVT designs are heavy and large for robotic applications. This paper presents a passively adaptable toroidal-CVT (pat-CVT) that is coupled with a twisted string actuator (TSA). The proposed combination of pat-CVT with TSA expands the operation range of TSA and mimics the torque-speed characteristics of artificial muscles. The contributions of the proposed system are as follows: 1) It has a high transmission ratio (4.6:1) compared to the level of existing CVT, and compact design; 2) We propose a structure that increases the power transmission efficiency by reducing slip rate during rotation through the application of soft material on the input/output disk and roller surfaces of the CVT; 3) Relations between the external load and the transmission ratio can be determined by selecting the spring in the system to optimize the motor operating conditions over the entire range of loads. We show theoretical modeling of pat-CVT with TSA and characterization of the transmission ratio, energy efficiency, and force-velocity curve.

Electrical resistance tomography (ERT) is an inferential imaging technique that has been utilized to develop large-scale robotic skin due to its scalable and practical properties. The performance of ERT-based sensors has been improved by optimizing the electrode arrangement, but it has relied on a heuristic design due to an absence of quantitative studies on the effects of the electrode arrangement. This article introduces a novel design method to optimize an electrode arrangement for ERT-based robotic skins. The method is based on a neural-gas network, and it finds an optimal design that maximizes the minimum electric current density. The optimal design was comprehensively evaluated using the ERT forward model, and the result revealed that the optimal design achieved the best intrinsic properties in terms of ill-posedness, sensitivity, and spatial discriminability. For validation, we conducted an indentation experiment on ERT-based robotic skin with the optimal design. Although only 30 electrodes were used to cover the 700 cm2sensing area, physical contacts could be localized with an error of 6.6 ± 3.5 mm, and the two-point resolution was adequate for daily tasks. Finally, the developed robotic skin was integrated with a commercial robot arm, and its use in physical human–robot interaction was demonstrated.

Compliance in robot actuation provides a solution to perform safe physical human-robot interaction. Conventional compliant actuators (variable stiffness actuators, series elastic actuators) used more than two motors or closed-loop controller to modulate both stiffness and equilibrium position independently. These actuators are complex, lack of energy efficiency, and have limited stiffness range. In conjunction with an active, positive stiffness modulation, implementing a passive negative stiffness element enabled a compact design of the compliant actuator. This paper suggests a power transmission design of fast and energy-efficient stiffness modulation based on this new compliant actuator concept. First, the double slider-crank mechanism made fast stiffness modulation and high energy-efficiency. Second, positioning the leaf spring’s bending location to the center also enabled the fast stiffness modulation speed and wide range stiffness modulation. Third, optimized elliptical cam with compression spring generated negative stiffness in output. We provide theoretical modeling of each mechanical drivetrains and characterization of positive stiffness modulation (range and speed) and negative stiffness with corresponding power consumption experimentally.

A human can socially interact in a non-verbal manner by understanding the intention behind a tactile stimulus. Patting on one’s back is one of tactile communications, which is considered as a sign of encouragement in most cultures. The majority of such tactile communication is carried out by a dynamic tactile on large passive body parts and differently interpreted by how and where on the body is touched. Thus any robotic system that physically interacts with a human requires a dynamic tactile sensor for further social interaction. This paper presents a large dynamic tactile sensor that could cover a robot’s passive body parts using a few sparsely distributed microphones to cover a large area in an efficient manner. A porous structured mesh, neoprene, and loop fabric are used to form a sensor’s skin that could well generate and transfer a signal to distributed microphones when a touch is introduced. TDOA source localisation algorithms are implemented to find the touch point locating in between the distributed microphones, and a simple convolutional neural network is trained to classify a type of the touch. A localising performance is qualitatively achieved in a testbed of the sensor and applied to a mannequin’s back to show the applicability, which classified a touch into six classes with an accuracy of 88 %.

The tactile and proprioceptive sensation increases human manipulability, and soft tissue compliance stabilizes the grasping function. However, it is challenging to transpose this system to the small confined space of soft robotic fingers due to the material properties and complex wiring entailed. Furthermore, soft robotic fingers also incorporate actuating components, making such a system more difficult to bring to fruition. Therefore, optimizing soft robotic finger structure for greater functionality and manufacturability would be a desirable innovation. In this study, we developed a soft somesthetic robotic finger based on the conductive working liquid and an origami structure. The proposed design comprises an origami structure, porous scaffolds, and a silicone-coated fabric outer layer. The robotic finger was filled with conductive liquid used for both somesthetic sensing and bending actuation simultaneously. The origami structure was fabricated by connecting printed circuit boards (PCBs) and a polyimide film, with electrodes embedded on each PCB to enable somesthetic sensing. The electrodes were used to inject currents and measure voltage, with the measured data then used to reconstruct the deformation map and hinge angle by means of electrical resistance tomography (ERT). The experimental results confirm that the robotic finger could acquire tactile and proprioceptive information in real-time.

Hydrostatic transmission has shown promising results for enabling the manipulator to achieve low effective inertia, high stiffness, and high torque density. However, the incompressibility of fluid causes the lack of compliance, so that it could not provide intrinsic safety. Thus, it would be advantageous to introduce series compliance on the hydrostatic manipulator for adjusting stiffness depending on the situation. Here, we developed a safe and high-performance hydrostatic actuator based on the switchable stiffness mechanism implemented with an air spring and solenoid valve. The hydrostatic transmission is implemented with rolling diaphragms to attain zero fluid leakage and low seal friction. Air spring is serially connected to hydraulic lines for achieving compliance. Its modes (i.e., stiff and compliant modes) can be rapidly switched by modulating water flow via the solenoid valve. Block stiffness experiment shows that the stiffness of stiff mode is 9.63 times stiffer than one of compliant mode at 100 kPa. We experimentally demonstrated that compliant mode could mitigate the impact force to the level that a tangerine would not be crushed. The stiffness was switched within 12 ms; hence it is fast enough to be used for feedback application with vision or tactile sensors. As a result, the developed actuator can ensure safety without sacrificing the dynamic performance, owing to the simple and rapidly switchable stiffness mechanism.

Electrical impedance tomography (EIT) based tactile sensor offers significant benefits on practical deployment because of its sparse electrode allocation, including durability, large-area scalability, and low fabrication cost, but the degradation of a tactile spatial resolution has remained challenging. This article describes a deep neural network based EIT reconstruction framework, the EIT neural network (EIT-NN), alleviating this tradeoff between tactile sensing performance and hardware simplicity. EIT-NN learns a computationally efficient, nonlinear reconstruction attribute, achieving high-resolution tactile sensation and well-generalized reconstruction capability to address arbitrary complex touch modalities. We train EIT-NN by presenting a sim-to-real dataset synthesis strategy for computationally efficient generalizability. Furthermore, we propose a spatial sensitivity aware mean-squared error loss function, which uses an intrinsic spatial sensitivity of the sensor to guarantee a well-posed EIT operation. We validate an outperformance of EIT-NN against conventional EIT sensing methods by conducting a simulation study, a single-touch indentation test, and a two-point discrimination test. The results show improved spatial resolution, sensitivity, and localization accuracy. The beneficial features of the generalized sensing of EIT-NN were demonstrated by examining touch modality discrimination performance.

Pneumatic transmission has several advantages in developing powered ankle foot orthosis (AFO) systems, such as the flexibility in placing pneumatic components for mass distribution and providing high back-drivability via simple valve control. However, pneumatic systems are generally tethered to large stationary air compressors that restrict them for being used as daily assistive devices. In this study, we improved a previously developed wearable (untethered) custom compressor that can be worn (1.5 kg) at the waist of the body and can generate adequate amount of pressurized air (maximum pressure of 1050 kPa and a flow rate of 15.1 mL/sec at 550 kPa) to power a unilateral active AFO used to assist the dorsiflexion (DF) motion of drop-foot patients. The finalized system can provide a maximum assistive torque of 10 Nm and induces an average 0.03±0.06 Nm resistive torque when free movement is provided. The system was tested for two unilateral drop-foot patients. The proposed system showed an average improvement of 13.6° of peak dorsiflexion angle during the swing phase of the gait cycle.

Electrical resistance tomography (ERT) has previously been utilized to develop a large-scale tactile sensor because this approach enables the estimation of the conductivity distribution among the electrodes based on a known physical model. Such a sensor made with a stretchable material can conform to a curved surface. However, this sensor cannot fully cover a cylindrical surface because in such a configuration, the edges of the sensor must meet each other. The electrode configuration becomes irregular in this edge region, which may degrade the sensor performance. In this paper, we introduce an ERT-based robotic skin with evenly and sparsely distributed electrodes. For implementation, we sprayed a carbon nanotube (CNT)-dispersed solution to form a conductive sensing domain on a cylindrical surface. The electrodes were firmly embedded in the surface so that the wires were not exposed to the outside. The sensor output images were estimated using a deep neural network (DNN), which was trained with noisy simulation data. An indentation experiment revealed that the localization error of the sensor was 5.2 ± 3.3 mm, which is remarkable performance with only 30 electrodes. A frame rate of up to 120 Hz could be achieved with a sensing domain area of 90 cm2. The proposed approach simplifies the fabrication of 3D-shaped sensors, allowing them to be easily applied to existing robot arms in a seamless and robust manner.

Humans perform grasping by breaking down the task into a series of action phases, where the transitions between the action phases are based on the comparison between the predicted tactile events and the actual tactile events. The dependency on tactile sensation in grasping allows humans to grasp objects without the need to locate the object precisely, which is a feature desirable in robot grasping to successfully grasp objects when there are uncertainties in localizing the target object. In this paper, we propose a method of implementing a tactile event based grasping algorithm using memorized predicted tactile events as state transition triggers, inspired by the human grasping. First, a simulated robotic manipulator mounted with pressure and vibration sensors on each finger, analogous to the different mechanoreceptors in humans, performed ideal grasping tasks, from which the tactile signals between consecutive states were extracted. The extracted tactile signals were processed and stored as predicted tactile events. Secondly, a grasping algorithm composed of eight discrete states, Reach, Re-Reach, Load, Lift, Hold, Avoid, Place, and Unload was built. The transition between consecutive states is triggered when the actual tactile events match the predicted tactile events, otherwise, triggering the corrective actions. Our algorithm was implemented on an actual robot, equipped with capacitive and piezoelectric transducers on the fingertips. Lastly, grasping experiments were conducted, where the target objects were deliberately misplaced from their expected positions, to investigate the robustness of the tactile event based grasping algorithm to object localization errors.

The fact that we can see each other face to face in the distance has become so natural with the appearance of video calls. However, the desire to touch each other in the distance is far from being resolved. We developed a pair of devices that can transmit touch sense through the shoulder such as tapping, caressing, and pressing. The touch sensors on a telepresence robot transmit touch feedback to a remote user wearing a haptic vest via wireless communication.

An echinoderm can actively modulate the structural stiffness of its body wall by as much as 10 times, using the material and structural features that make up its body, including calcite ossicles, connective tissue and interossicular muscle. This capacity for variable stiffness makes it possible to adapt to the kinematics and dynamics required to perform a given task and the surrounding environment. This characteristic can improve the ability of soft material robots, which currently have limited application because of their low load-bearing capability. This paper presents a stiffness modulation method inspired by the connected ossicle structures of echinoderms. We introduce the mechanism, structure, and stiffness variation of the proposed design with respect to different ossicle shape, interval, and elastomer. Then we built a finger-shaped stiffening structure using the proposed design, measured its stiffness according to vacuum level, and showed its load-bearing capacity under control. The proposed design was then applied to a robotic gripper, a typical device that interacts with unpredictable environments and needs variable stiffening ability.

Robots operating in unstructured environments would benefit from soft whole-body tactile sensors, but implementing such systems typically requires complex electrical wiring to a large number of sensing elements. The reconstruction method called electrical resistance tomography (ERT) has shown promising results (good coverage, manufacturability, and robustness) using electrodes located only along the boundary of the sensing region. However, relatively poor spatial resolution in the sensor's central region is a major drawback of the ERT approach. This paper introduces a new scheme of internal array electrodes to improve spatial resolution. We also systematically derive the optimal pairwise current injection patterns from a mathematical formulation of the ERT system. By highlighting the importance of each electrode pair, this approach enabled us to reduce the number of current injection patterns. Simulation of the standard and proposed sensor designs revealed that the internal array electrodes greatly improve distinguishability in the central region. For validation, a fabric-based soft tactile sensor made of multiple conductive fabrics was developed, including electronics that enable sampling at 200 Hz. During a 225-point localization test conducted without sensor-specific calibration, the constructed sensor showed average localization errors of 2.85 cm ± 1.02 cm. This result is notable because only 16 point electrodes were used to achieve this performance.

Recently, a whole-body tactile sensing have emerged in robotics for safe human-robot interaction. A key issue in the whole-body tactile sensing is ensuring large-area manufacturability and high durability. To fulfill these requirements, a reconstruction method called electrical impedance tomography (EIT) was adopted in large-area tactile sensing. This method maps voltage measurements to conductivity distribution using only a few number of measurement electrodes. A common approach for the mapping is using a linearized model derived from the Maxwell's equation. This linearized model shows fast computation time and moderate robustness against measurement noise but reconstruction accuracy is limited. In this paper, we propose a novel nonlinear EIT algorithm through Deep Neural Network (DNN) approach to improve the reconstruction accuracy of EIT-based tactile sensors. The neural network architecture with rectified linear unit (ReLU) function ensured extremely low computational time (0.002 seconds) and nonlinear network structure which provides superior measurement accuracy. The DNN model was trained with dataset synthesized in simulation environment. To achieve the robustness against measurement noise, the training proceeded with additive Gaussian noise that estimated through actual measurement noise. For real sensor application, the trained DNN model was transferred to a conductive fabric-based soft tactile sensor. For validation, the reconstruction error and noise robustness were mainly compared using conventional linearized model and proposed approach in simulation environment. As a demonstration, the tactile sensor equipped with the trained DNN model is presented for a contact force estimation.

Recognizing and predicting the movement and intention of the wearer in control of an exoskeleton robot is very challenging. It is difficult for exoskeleton robots, which measure and drive human movements, to interact with humans. Therefore, many different types of sensors are needed. When using various sensors, a data design is needed for effective sensing. An electromyographic (EMG) signal can be used to identify intended motion before the actual movement, and the delay time can be shortened via control of the exoskeleton robot. Before using a lower limb exoskeleton to help in walking, the aim of this work is to distinguish the walking environment and gait period using various sensors, including the surface electromyography (sEMG) sensor. For this purpose, a gait experiment was performed on four subjects using the ground reaction force, human-robot interaction force, and position sensors with sEMG sensors. The purpose of this paper is to show progress with the use of sEMG when recognizing walking environments and the gait period with other sensors. For effective data design, we used a combination of sensor types, sEMG sensor locations, and sEMG features. The results obtained using an individual mechanical sensor together with sEMG showed improvement compared to the case of using an individual sensor, and the combination of sEMG and position information showed the best performance in the same number of combinations of three sensors. When four sensor combinations were used, the environment classification accuracy was 96.1%, and the gait period classification accuracy was 97.8%. Vastus medialis (VM) and gastrocnemius (GAS) were the most effective combinations of two muscle types among the five sEMG sensor locations on the legs, and the results were 74.4% in pre-heel contact (preHC) and 71.7% in pre-toe-off (preTO) for environment classification, and 68.0% for gait period classification, when using only the sEMG sensor. The two effective sEMG feature combinations were “mean absolute value (MAV), zero crossings (ZC)” and “MAV, waveform length (WL)”, and the “MAV, ZC” results were 80.0%, 77.1%, and 75.5%. These results suggest that the sEMG signal can be effectively used to control an exoskeleton robot.

High mechanical impedance, sensor resolution, and computing bandwidth are desirable for achieving stability in feedback control. In contrast, although the human body is physically inferior to man-made systems in terms of feedback control due to signal transmission delay, humans can achieve not only stable but also robust and adaptive control ability. For these reasons, the significance of the feedforward control of the human has been emphasized in neuroscience. In previous studies, virtual trajectory control and internal model hypotheses were used to explain the principle of human feedforward control in view of the mechanical stiffness of joints and the internal model of the brain, respectively. Inspired by these insights, in this paper, we focus on the relationship between the joint stiffness and inverse model accuracy and attempted to apply it to the field of robotics. We present a variable stiffness actuator developed using variable radius gear transmission inspired by muscle cross-bridge kinematics. The developed mechanism allows the joint stiffness to be accurately controlled without any sensor-based feedback control. Using the developed actuator, we conduct a feedforward motion generating experiment with respect to variations in the inverse dynamics model uncertainty and verified that joint stiffness can compensate for inverse model uncertainty and external disturbances. These results indicate that the developed variable stiffness actuator can be applied to the robotics field for feedforward applications and support the hypothesis that humans may utilize joint stiffness to compensate for the inverse dynamics model uncertainty.

Despite the advantages of electromyography (EMG), which can grasp intent before actual movement, there have not been many studies on the use of EMG in exoskeletons. In this paper, we conducted an experiment to analyze the characteristics of electromyography when EMG signals are used in exoskeleton robots. We integrated the advantages of EMG sensors and physical sensors to control exoskeleton robots using EMG signals and physical signals. The walking environment was defined using the surface electromyography (sEMG) signal and the physical signal to an accuracy of 88% or more. To compensate for the limitations of physical sensors, we used sEMG to distinguish changes in the load during walking. Moreover, sEMG signals and physical signals were used to distinguish external collisions and to identify other variables that could be distinguished. Finally, to examine the characteristics of muscular fatigue, which is a disadvantage when using electromyography, we conducted a muscle fatigue experiment in the lower limb and summarized how the EMG characteristics change in relation to the degree of force and the muscle fatigue.

Inspired from Hogan's myoelectric processor in 1980, this study presents a stochastic sEMG processing method to estimate the muscle activation level for manipulator control. Hogan's previous study showed the feasibility to estimate the muscle activation level with multi-channel sEMG under static force condition. However, it is difficult to continuously estimate muscle activation during dynamic contraction because of the nonlinear effects by the time-varying nature of sEMG. To enhance the performance of high SNR and rapid response in force-varying contraction, we propose a new method with statistical analysis extended from a whitening method of Hogan's study. The signals from eight sEMG channels were used to estimate the muscle activation level during isometric force-varying contractions. Experimentally, a two-DoF manipulator was controlled by input signals from the estimated muscle activation signal.

The closer distance between robots and human partners in the same space makes compliant robots essential in these days. While torque sensor based impedance control scheme suffers from insufficient bandwidth, musculoskeletal systems can achieve similar ability with smaller resources by using mechanically guaranteed dynamics. In this study, inspired by active stiffness mechanism of biological muscle, we developed a variable stiffness actuator which can realize precise joint stiffness without torque sensor based feedback control. The actuator consists of two antagonistically allocated DC motors with a variable radius gear transmission mechanism. The equilibrium position and joint stiffness can be independently controlled adjusting the activation level of motors by feedforward control. Theoretically predicted stiffness is realized well. In this paper, the design, functional principle and control of the actuator are introduced with analytical investigation.

The stabilization of man-made dynamic systems has been achieved by sensor-based state feedback control with high computational bandwidth, fast signal transmission speed, and stiff joints. In contrast, many biological systems can achieve similar or superior stable behavior with low computational bandwidth, slow signal transmission speed via the nervous system, and flexible joints. The concept of self-stabilization has recently been proposed and widely investigated to explain this phenomenon. Self-stabilization is defined as the ability to restore its original state after a disturbance without any feedback control. In this paper, the stabilizing function of a musculoskeletal system for arbitrary motion in the vertical plane is analytically investigated using Lyapunov stability criteria. Based on this investigation, the method of designing a new actuator that can assign a self-stabilizing function to a robotic arm is introduced and a self-stabilizing manipulator is physically realized. As a result, a theoretically predicted self-stabilizing function is experimentally verified and explains why a biological musculoskeletal system can be stabilized with feedforward control.

This paper presents a printable skin adhesive stretch sensor to estimate rotation angles of multi-axis joint for biomedical engineering applications, such as gait analysis, gesture recognition, and motion monitoring. Silicone rubber mixed with multiwall carbon nanotube composites were fabricated to make a highly stretchable (up to 120%) strain sensors. Embedding liquid state composites to the fabric leaded to enhance the adhesive force between the composites and fabric. The skin adhesive sensor system could be used to estimate rotation angles of the multi-axis joints while providing comfortable physical interfaces. To reduce misalignment to the axis of rotation, a calibration method was formulated based on geometrical relationship between the sensor axes and the joint angle. In order to validate the estimation performance of the sensor in multi-axis joint, shoulder flexion/extension and abduction/adduction angles were estimated. The results showed the system could estimate the rotation angles of the shoulder enough to identify motion intentions of the user. In the future, the proposed system can be applied to the motion monitoring system by direct attachment to skin without discomfort to the user in daily life.

The stabilization of man-made artificial systems has been achieved by sensor based state feedback control with high computational bandwidth and high stiffness structures. In contrast, many biological systems have been achieved similar or superior stable behavior with low speed signal transmission via nervous systems, which is easy to introduce unstable performance from a control engineering perspective. In order to explain this phenomenon, the concept of self-stabilization has recently been proposed and investigated. Self-stabilization is defined as the ability to restore its original state after a disturbance without any feedback control. In this paper, the self-stabilizing function of a musculoskeletal system for arbitrary motion in the vertical plane is analytically investigated using Lyapunov stability theory. Based on this investigation we propose a design method to realize the self-stabilizing function of a musculoskeletal system, and experimentally verify that the self-stabilizing function can be physically realized by the proposed Lyapunov function.

We propose a new method to estimate joint force using a biomechanical muscle model and peaks of surface electromyography (SEMG). The SEMG measurement was carried out from the first dorsal interosseous muscle during isometric index finger abduction. The SEMG peaks were used as the input of the biomechanical muscle model which is a transfer function to generate the force. The force estimation performance ( R(2) ) was evaluated using the proposed method with nine healthy subjects, and a former method using a mean absolute value (MAV), which is the full-wave rectified and averaged (or low-pass filtered) signal of SEMG in a time window, was compared with the proposed method; the performance of the proposed method (0.94 ± 0.03) was better than that of MAV (0.90 ± 0.02). The proposed method could be widely applied to quantitative analysis of muscle activities based on SEMG.

To obtain the quantitative mechanical information, robotic palpation systems have been studied. The aim of this study was to evaluate the reliability of the mechanical mapping using correlation with pathological cancer suspected maps. A total of 60 indentations were performed on 5 specimens taken during radical prostatectomy with a robotic palpation system. Suspected cancer lesions based on the mechanical properties were compared to those of pathological results. The concordance rate was 81.7 % (98/120). Sensitivity and specificity were 93.5 % (29/36) and 91.6 % (22/24). Positive predictive value (PPV) and negative predictive value (NPV) were 93.5 % (29/31) and 75.8 % (22/29). As a result, the mechanically suspected lesions are in close agreement with those of pathological results. This study may contribute on technological progress for overcoming limitations which include many complications due to non-targeted systemic biopsy and late detection of prostate cancer.

A human who uses a SEMG-based power assist robot suffers from unstable assistance due to noisy nature of SEMG signals. This study aimed at analyzing the quantitative stability of the human elbow movements during the power assistance. During self-paced elbow flexion in the sagittal plane, an exoskeleton robot provided the assistive torque that was proportional to the estimated human elbow torque using SEMG. Maximum finite time Lyapunov exponent (MFTLE), the average logarithmic rate of the divergence of neighboring trajectories, during elbow flexion assisted by the robot was computed as an index of movement stability. The results showed a trade-off between decrease of physical effort for the movements and the stability of the movements. The stability of the SEMG-based assistance by MFTLE deteriorated as the amount of the assistive torque was increased to the amount of the human torque, although the physical effort required by the user his forearm was decreased. This study can be used as a guide to determine the amount of SEMG-based assistive torque for maintaining the stability of the assisted movements.

In this paper, the design of a high precision device using a Linear Delta manipulator was proposed to compensate for the tremor signal in three translational directions. A Linear Delta manipulator is a suitable tremor suppression device due to the simple structure and high stiffness with the vertical direction in the application of micro manipulation such as microsurgery and cell manipulation. In order to implement the mechanism of the Linear Delta manipulator to the device, three voice coil motors and three linear encoders with high resolution were used. The flexure mechanism was applied to the device to avoid the friction effect of the small ball joint. Finally, the experiments for the validation of the proposed device were performed as follows: (1) position control in each axis for accuracy, and (2) sine wave tracking (500 μm, 12Hz) for bandwidth of the system.

Microinjection and biopsy of oocytes and embryos in Assisted Reproductive Technology (ART) require highly delicate handling of cells. In particular, efficient control of cell orientation is necessary to maintain their integrity during tool penetration, which currently remains challenging to accomplish by the existing method of repeated aspiration/release via micropipette. We present a microfluidic platform to automate the process of cell orientation control and trapping by means of hydrodyanmic force and vision-based position control. The device is accessible by conventional micropipettes via a cavity, allowing immobilized cells to be operated on. An orientation control algorithm based on the movement of the embryo within the microchannel is proposed. Visual tracking of the polar body is used to provide the information of cell orientation. Experimental results with mouse embryos indicate that cell orientation can be systematically controlled autonomously without human intervention and therefore provides a framework for further development of robotics approach to precise manipulation of microparticles within microfluidic devices.

There has been a paucity of studies to identify the variation of muscle properties due to muscle fatigue, although feature changes of surface electromyographic signals due to fatigue have been reported on. In this paper, we investigated the variation of muscle properties in pre-fatigue condition and post-fatigue condition by using the SEMG and the dynamic muscle model. Five subjects performed index finger isometric abduction contraction by using the first dorsal interosseous (FDI) muscle. After fatigue-inducing contraction, the total twitch duration increased by 30.10%, the contraction time and half relaxation time (RT 1/2) were raised to 7.45% and 13.11%, respectively. These results indicate that the response of the twitch force was slowed and prolonged due to the fatigue-inducing contraction. Our results can be used to monitor and identify the muscle properties of patients in rehabilitation programs.

A wearable sensor for measuring finger flexion force based on volar displacement of flexor tendon is presented. The proposed sensor utilizes a principle that the volar displacement of tendon under a pulley depends on both of tendon tension and finger posture when a external compressive force is applied on the pulley. A prototype sensor is built for the verification of the proposed method. Experiments with isometric conditions are performed in 9 different finger postures to observe the response of the sensor with regard to the finger flexion force and finger posture. The results show that the output of the proposed sensor has dependency on both of finger force and posture. This implies that the sensor can be used for measuring finger flexion force when the finger posture and the corresponding sensor response is known. A simulation with simplified model is performed to explain the behavior of the sensor output.

Microinjection is a method for the delivery of exogenous materials into cells and is widely used in biomedical research areas such as transgenics and genomics. However, this direct injection is a time-consuming and laborious task, resulting in low throughput and poor reproducibility. Here, we describe a telerobotic shared control framework for microinjection, in which a micromanipulator is controlled by the shared motion commands of both the human operator and the autonomous controller. To determine the weightings between the operator and the controller, we proposed a quantitative evaluation method using a model of speed/accuracy trade-offs in human movement. The results showed that a 40%-60% weighting on the human operator (or the controller) produced the best performance for both speed and accuracy of guiding and targeting task in microinjection suggesting that some level of both automation and human involvement is important for microinjection tasks.

Medical robotic system is a reliable method to provide a precise and safe treatment to the patients, especially in the area of urology. The prostate cancers are firmer than normal tissues. Therefore, physicians can detect the prostate cancer with a palpation. In this paper, we developed a robotic system to detect prostate cancers using robotic palpation to find the suspected area of the tissue as well as to localize the area of the needle biopsy. To validate the performance of the developed system, the experiment on soft tissue phantoms was carried out. The results indicate that the system can identify the differences of tissue phantoms within and without inclusions. In addition, the bigger inclusions and the closer inclusions from the surface show larger value of the force response. These results indicate that the developed robotic system can be used to find hard materials such as tumor and malignant tissue in the prostate.

Microinjection is the highly efficient delivery method of exogenous materials into cells, and it has been widely used in biomedical research areas such as transgenics and genomics. However, this direct injection task is time consuming and laborious, resulting in low throughput and poor reproducibility. This paper describes a telerobotic shared control (TSC) framework for the microinjection with high manipulation efficiencies, in which a micromanipulator is controlled by the shared motion commands of both the human operator (direct manipulation) and the autonomous controller. To determine the optimal gains between the operator and the controller, we proposed a quantitative evaluation method using Fitts' and steering laws. The results showed that a 40%-60% weighting on the human operator produced better performance for both speed and accuracy of task completion, and suggested that some level of automation or human involvement is important for microinjection tasks.

Spaceborne synthetic aperture radar(SAR) can provide a high resolution image in all weather conditions. Spaceborne SAR(Synthetic Aperture Radar) obtains electronic image of earth's surface using electromagnetic waves. SAR image inherently contains geo-location error which is caused by SAR image acquisition geometry, imaging mode, characteristics of reflectivity and image formation process. In this paper, a geo- location error correction method is presented to analyze and correct the geometric distortion by terrain height error. Using RADARSAT-1 image, a simulation is performed to evaluate the proposed geo-location algorithm. To analyze the effect of proposed correction method, the corrected SAR images are compared with the reference image by RMSE values.

Robotic palpation is of major interest as a medical technique that could replace subjective palpation and tactile sensation by yielding precisely controlled palpation to tissues and quantitative tactile feedback acquisition. Palpation results and biomechanics based mechanical property characterization are possible solutions that could enable the acquisition of objective and quantitative information on abnormal tissue localization during diagnosis and surgery. This paper presents an integrated approach for robotic palpation and mechanical property characterization. To validate the proposed methods, robotic palpation experiments on silicone soft-tissue phantoms with embedded hard inclusions were performed using a robotic palpation system, and the force responses of the phantoms were measured. Furthermore, we carried out a numerical analysis simulating the experiments and estimating the objective and quantitative properties of the tissues.

A current challenge with human-machine cooperation systems is to estimate human motions to facilitate natural cooperation and safety of the human. It is a logical approach to estimate the motions from their sources (skeletal muscles); thus, we employed surface electromyography (SEMG) to estimate body motions. In this paper, we investigated a cooperative manipulation control by an upper limb motion estimation method using SEMG and joint angular velocities. The SEMG signals from five upper limb muscles and angular velocities of the limb joints were used to approximate the flexion-extension of the limb in the 2-D sagittal plane. The experimental results showed that the proposed estimation method provides acceptable performance of the motion estimation [normalized root mean square error (NRMSE) <0.15, correlation coefficient (CC) >0.9] under the noncontact condition. From the analysis of the results, we found the necessity of the angular velocity input and estimation error feedback due to physical contact. Our results suggest that the estimation method can be useful for a natural human-machine cooperation control.

Due to difficulties in measurement of muscle activities and understanding a user's intention under different configurations, controlling machine forces using surface electromyogram (SEMG) is difficult in a human-machine interface (HMI). This study describes a novel HMI using Hill-based muscle model to control the isometric force of a robotic thumb that considers the importance of the thumb in hand function. In order to estimate force intension, SEMG from the skin surface was measured and converted to muscle activation information. The activations of deep muscles were inferred from the ratios of muscle activations from earlier studies. The muscle length of each contributed muscle was obtained by using a motion capture system and musculoskeletal modeling software packages. Once muscle forces were calculated, thumb-tip force was estimated based on a mapping model from the muscle force to thumb-tip force. The proposed method was evaluated in comparisons with a linear regression and artificial neural network (ANN) under four different thumb configurations to investigate the potential for estimations under conditions in which the thumb configuration changes.

A space-time adaptive processing (STAP) can be effective in detecting the ground moving targets from the airborne/spaceborne moving platform in the severe ground clutter and jammer environments. In this paper, the characteristics of target, ground clutter and jammer signals are analyzed and designed the adaptively weighted clutter notch filter in two dimensional spatial and temporal azimuth angle-Doppler domains. The simulation is performed for ground moving target detection by rejecting both clutter and jammer simultaneously through the STAP processing. The probability of target detection is investigated depending on the SNR and the minimum detectable velocity (MDV) in the given false alarm rate. The simulation results show that the MDV can be ideally achieved up to 2 m/s ~ 3 m/s at the 90 % of detection probability in the given false alarm rate of 10-3and 10-6. This technique may be applied for the surveillance and traffic monitoring of moving vehicle on the road.

Human motion and its intention sensing from noninvasive biosignals is one of the significant issues in the field of physical human-machine interactions (pHMI). This paper presents a real-time upper limb motion prediction method using surface electromyography (sEMG) signals for pHMI. The sEMG signals from 5 channels were collected and used to predict the motion by an artificial neural network (ANN) algorithm. We designed a human-machine interaction system to verify the proposed method. Interaction experiments were performed with or without physical contact, and the effects of instances of contact were investigated. The experimental results were compared with controlled experiments using a customized goniometer, which is able to measure upper limb flexion-extension. The results showed that the proposed method was not superior to the use of direct angle measurements; however, it provides sufficient accuracy and a fast response speed for interactions. SEMG-based interactions will become more natural with further studies of human-machine combination models.

This paper presents a haptic rendering technique for a telemanipulation system of deformable objects using image processing techniques and physically based modeling. The interaction forces between an instrument driven by a haptic device and a deformable object are inferred in real time based on a continuum mechanics model of the object, which consists of a boundary element model and a prior knowledge of the object’s mechanical properties. These models allow users to feel reaction forces during manipulation tasks through a haptic device. Macro- and micro-scale experimental systems, equipped with a telemanipulation system and a commercial haptic display, were developed and tested using silicone (macro-scale) and zebrafish embryos (micro-scale). The developed algorithm can be used with a needle operation robot or a cellular injection system.

This paper presents the development and performance evaluation of a human-computer interface that enables a limb-disabled person to access a computer via neural signals. For this purpose, electromyogram (EMG) signals were extracted from four muscles on the lower arm, and signal statistics (namely the mean and variance) were used for a filtering process. Six patterns were then classified through the application of a supervised multilayer neural network trained by a backpropagation algorithm. To extract the user's intentions, such as cursor movements and a clicking, the authors applied the neural network in the classification of the six patterns. In addition, an on-screen keyboard was developed so that letters of the Roman and Korean alphabets could be keyed into the computer. Finally, to confirm the appropriation of the developed computer interface, the authors applied Fitts' law in an experimental study to evaluate the performance of the computer interface. The experimental results show that the computer interface had an index of performance, or bandwidth, of 1.299. However, although the developed EMG-based human-computer interface had a lower index of performance than a mouse, it provides an alternative means of computer access for those with a disabled upper limb.

This paper proposes a motion duplication control scheme, which not only synchronizes motions between two distributed separate dynamic systems but also perfectly preserves prescribed dynamics. The proposed scheme sophisticatedly utilizes two-way Smith predictor to meet the simultaneous purposes. Closed loop behavior is mathematically investigated, and stability via natural damping and robustness are analyzed over the system, by examining the characteristic equation with delay components. Ways to compute robust stability margins are presented under the uncertainties in plant dynamics and amount of delay. Numerical simulations are presented to verify the theoretical results proposed in this paper