Search Results (75)

This study proposes an indirect approach that estimates roll force from motor current signals in a rolling mill. Motor current is first converted to motor torque using an induction-motor equivalent-circuit model, then to roll torque via the gear ratio, and finally to roll force through a torque-arm relationship. A laboratory-scale rolling mill was designed and fabricated to experimentally validate the approach. Two torque-conversion schemes were examined: Method A, which determines the slip of the induction motor from measured rpm and recalculated motor parameters, and Method B, which estimates slip from measured motor current and applies a finite element (FE)–based response surface function to calibrate the converted torque. The converted roll torques were validated against FE analysis, and the resulting roll forces were compared with load cell measurements under various rolling conditions. Deviation, defined as the average difference between the FE-predicted torque and the converted torques, ranged from −11.9% to 28.8% for Method A and −7.2% to 13.8% for Method B. Roll force deviations from measurements ranged from −14.1% to 14.9% for Method A and −3.7% to 14.2% for Method B. Method A provided a straightforward and computationally light conversion route but was more sensitive to rpm-measurement noise, whereas Method B yielded smoother temporal behavior at the cost of FE-based calibration. The results demonstrate that both methods can reproduce the overall evolution of roll torque and roll force using only motor-side measurements, offering a practical foundation for real-time monitoring in rolling mills where stand-by-stand load cells are unavailable. Full article

Soft robotics provides compliance, safe interaction, and adaptability that rigid systems cannot easily achieve. The octopus offers a powerful biological model, combining reversible suction adhesion, continuum arm motion, and reliable performance in wet environments. This review examines recent octopus-inspired soft grippers through three functional dimensions: structural and sensing devices, control strategies, and AI-driven applications. We summarize suction-cup geometries, tentacle-like actuators, and hybrid structures, together with optical, triboelectric, ionic, and deformation-based sensing modules for contact detection, force estimation, and material recognition. We then discuss control frameworks that regulate suction engagement, arm curvature, and feedback-based grasp adjustment. Finally, we outline AI-assisted and neuromorphic-oriented approaches that use event-driven sensing and distributed, spike-inspired processing to support adaptive and energy-conscious decision-making. By integrating developments across structure, sensing, control, and computation, this review describes how octopus-inspired grippers are advancing from morphology-focused designs toward perception-enabled and computation-aware robotic platforms. Full article

The upper-limb exoskeleton is ergonomically designed to align with human arm motion and can be configured for deployment as a master tool manipulator (MTM) in teleoperation systems. However, existing teleoperated exoskeletons are limited by excessive weight and inadequate force feedback. This study proposes a novel lightweight exoskeleton with optimized shoulder and wrist joint structure, enabling full arm mobility and sufficient force feedback. In practical applications, gravitational forces can lead to muscle fatigue and degrade teleoperation performance, making compensation essential for ergonomic and safety. However, unknown system disturbance caused by unmodeled dynamics (such as internal compliance and cables) pose challenges for compensation precision. A theoretical dynamics model and a Bayesian neural network (BNN) trained on separate datasets to predict joint torques and their corresponding uncertainties were independently developed. Then a Bayesian fusion method was employed to combine model-based and data-driven estimates, using predicted standard deviations to assign fusion weights and produce a refined torque output. Compared to relying solely on the CAD model, the proposed fusion framework combines the physical consistency of model-based approaches with the adaptability of data-driven methods. Experiments ultimately demonstrate that the proposed algorithm effectively reduces modeling errors and enhances the accuracy and robustness of gravity compensation. Full article

Minimally Invasive Surgery is often limited by the lack of tactile feedback. Indeed, surgeons have traditionally relied heavily on tactile feedback to estimate tissue stiffness - a critical factor in both diagnostics and treatment. With this in mind we present in this paper a flexible sensor foil, based on polymer optical waveguide. This sensor has been applied for real-time contact force measurement, material stiffness differentiation and surface texture reconstruction. Interrogated by a commercially available optoelectronic device, the sensor foil offers precise and reproducible feedback of contact forces up to 5 N, with a minimal detectable limit of 0.1 N. It also demonstrates distinct optical attenuation responses when indenting silicone samples of varying stiffnesses under controlled displacement. When integrated onto a 3D-printed module resembling an endoscopic camera and manipulated by a robotic arm, the sensor successfully generated spatial stiffness mapsof a phantom. Moreover, by sliding over structures with varying surface textures, the sensor foil was able to reconstruct surface profiles based on the light attenuation responses. The results demonstrate that the presented sensor foil possesses great potential for surgical applications by providing additional haptic information to surgeons. Full article

This study presents a belt sanding robot for large convex surfaces together with a proportional–integral force control method. Sanding belt tension strongly affects area coverage and spatial normal-force uniformity on large curved surfaces; existing approaches typically use fixed tool positions or lack active tension regulation, which limits coverage and makes force distribution difficult to control. The mechanism consists of two series elastic actuator arms and an active re-tensioner that adjusts belt tension during contact. In contrast to a conventional belt sander, the series elastic configuration enables indirect estimation of the reaction force without load cells and provides compliant interaction with contact transients. The system is evaluated on curved steel plates using vertical scans with a belt width of 50 mm and a drive wheel speed of 300 rpm. Performance is reported for two target curvature values, namely 0.47 and 1.37, with five trials for each condition. The control objective is a constant normal force along the contact, achieved through proportional–integral control of the arms for normal-force tracking and the re-tensioner for belt tension regulation. To quantify spatial force uniformity, the distribution rate is defined as the ratio of the difference between the maximum and minimum normal forces to the maximum normal force measured across the belt–workpiece contact region. Compared with a simple belt sander baseline, the proposed system increased the sanded area coverage by 31.85%, from 62.20% to 94.05%, at the curvature value of 0.47, and by 8.49%, from 81.21% to 89.70%, at the curvature value of 1.37. The distribution rate improved by 113% at the curvature value of 0.47 and by 16.7% at the curvature value of 1.37. Under identical operating conditions of 50 mm belt width, 300 rpm, and five repeated trials, these results indicate higher area coverage and more uniform force distribution relative to the baseline. Full article

The six-degree-of-freedom (6-DoF) interaction forces/torque of the tool-end play an important role in the robotic tool manipulation using a gripper, which are usually indirectly measured by a robot wrist force/torque sensor. However, the real-time decoupling of the tool’s inertial force remains a challenge when different tools and grasping postures are involved. This paper presents a universal tool-end interaction forces estimation approach, which is capable of handling diverse grippers and tools. Firstly, to address uncertainties from varying tools and grasping postures, an online-identifiable tool dynamics model was built based on the Newton–Euler approach for the integrated gripper–tool system. Sensor zero-drift caused by factors such as the tool weight and prolonged operation is incorporated into the dynamic model and identified online in real time, enabling a coarse estimation of the interaction forces. Secondly, a spiking neural network (SNN) is specially employed to compensate for uncertainties caused by the wrist sensor creep effect, since its temporal processing and event-driven characteristics match the time-varying creep effects introduced by tool changes. The proposed method is experimentally validated on a robotic arm with a gripper, and the results show that the root mean square errors of the estimated tool-end interaction forces are below 0.5 N with $x$, $y$, and $z$ axes and 0.03 Nm with ${\tau }_x$, ${\tau }_y$, and ${\tau }_z$ axes, which has a comparable precision with the in situ measurement of the interaction forces at the tool-end. The proposed method is further applied to robotic scraper manipulation with impedance control, achieving the interaction forces feedback during compliant operation precisely and rapidly. Full article

Invasive candidiasis (IC), characterized by the most common clinical manifestation of candidemia, is a fungal infection with a high mortality rate and a significant impact on global public health. It is estimated that each year there are between 227,000 and 250,000 hospitalizations related to IC, with more than 100,000 associated deaths. In Latin America and the Caribbean (LA&C), the absence of a standardized surveillance system has led to multicenter studies documenting incidences ranging from 0.74 to 6.0 cases per 1000 hospital admissions, equivalent to 50,000–60,000 hospitalizations annually, with mortality rates of up to 60% in certain high-risk groups. Armed conflicts and structural violence in LA&C cause forced displacement, the collapse of health systems, and poor living conditions—such as overcrowding, malnutrition, and lack of sanitation—which increase vulnerability to opportunistic infections, such as IC. Insufficient specialized laboratories, diagnostic technology, and trained personnel impede pathogen identification and delay timely initiation of antifungal therapy. Furthermore, the empirical use of broad-spectrum antibiotics and the limited availability of echinocandins and lipid formulations of amphotericin B have promoted the emergence of resistant non-albicans strains, such as Candida tropicalis, Candida parapsilosis, and, in recent outbreaks, Candidozyma auris. Full article

Exoskeletons transfer loads to the human body via physical human–exoskeleton interfaces (pHEI). However, the human–exoskeleton interaction remains poorly understood, and the mechanical properties of the pHEI are not well characterized. Therefore, we present a novel methodology to precisely characterize pHEI interaction stiffnesses under various loading conditions. Forces and torques were applied in three orthogonal axes to the upper arm pHEI of 21 subjects using an electromechanical apparatus. Interaction loads and displacements were measured, and stiffness data were derived as well as mathematically described using linear and non-linear regression models, yielding all the diagonal elements of the stiffness tensor. We find that the non-linear nature of pHEI stiffness is best described using exponential functions, though we also provide linear approximations for simplified modeling. We identify statistically significant differences between loading conditions and report median translational stiffnesses between 2.1 N/mm along and 4.5 N/mm perpendicular to the arm axis, as well as rotational stiffnesses of 0.2 N·m/° perpendicular to the arm, while rotations around the longitudinal axis are almost an order of magnitude smaller (0.03 N·m/°). The resulting stiffness models are suitable for use in digital human–exoskeleton models, potentially leading to more accurate estimations of biomechanical efficacy and discomfort of exoskeletons. Full article

Monitoring forearm muscle contraction force in home-based rehabilitation remains challenging. Electromyography (EMG), as a standard technique, is considered impractical and complex for independent use by patients at home, which poses a risk of device misattachment and inaccurate recorded data. Considering the muscle-related modality, several studies have demonstrated an excellent correlation between stretch sensors and EMG, which provides significant potential for addressing the monitoring issue at home. Additionally, due to its flexible nature, it can be attached to the finger, which facilitates the logging of the kinematic mechanisms of a finger. This study proposes a method for estimating forearm muscle contraction in a cylinder grasping environment during home-based rehabilitation using a stretch-sensor glove. This study employed support vector machine (SVM), multi-layer perceptron (MLP), and random forest (RF) to construct the estimation model. The root mean square (RMS) of the EMG signal, representing the muscle contraction force, was collected from 10 participants as the target learning for the stretch-sensor glove. This study constructed an experimental design based on a home-based therapy protocol known as the graded repetitive arm supplementary program (GRASP). Six cylinders with varying diameters and weights were employed as the grasping object. The results demonstrated that the RF model achieved the lowest root mean square error (RMSE) score, which differed significantly from the SVM and MLP models. The time series waveform comparison revealed that the RF model yields a similar estimation output to the ground truth, which incorporates the contraction–relaxation phases and the muscle’s contraction force. Additionally, despite the subjectivity of the participants’ grasping power, the RF model could produce similar trends in the muscle contraction forces of several participants. Utilizing a stretch-sensor glove, the proposed method demonstrated great potential as an alternative modality for monitoring forearm muscle contraction force, thereby improving the practicality for patients to self-implement home-based rehabilitation. Full article

With the growing demand for personalized and flexible production, human–robot collaboration technology receives increasing attention. However, enabling robots to accurately perceive and align with human motion intentions remains a significant challenge. To address this, a novel human–robot collaborative control framework is proposed, which utilizes electromyography (EMG) signals as an interaction interface and integrates human skill imitation with reinforcement learning. Specifically, to manage the dynamic variation in muscle coordination patterns induced by joint angle changes, a temporal graph neural network enhanced with an Angle-Guided Attention mechanism is developed. This method adaptively models the topological relationships among muscle groups, enabling high-precision three-dimensional dynamic arm force estimation. Furthermore, an expert reward function and a fuzzy experience replay mechanism are introduced in the reinforcement learning model to guide the human skill learning process, thereby enhancing collaborative comfort and smoothness. The proposed approach is validated through a collaborative assembly task. Experimental results show that the proposed arm force estimation model reduces estimation errors by 10.38%, 8.33%, and 11.20% across three spatial directions compared to a conventional Deep Long Short-Term Memory (Deep-LSTM). Moreover, it significantly outperforms state-of-the-art methods, including traditional imitation learning and adaptive admittance control, in terms of collaborative comfort, smoothness, and assembly accuracy. Full article

Soft and flexible capacitive tactile sensors are vital in prosthetics, wearable health monitoring, and soft robotics applications. However, achieving accurate real-time force detection and spatial localization remains a significant challenge, especially in dynamic, non-rigid environments like prosthetic liners. This study presents a real-time force point detection and tracking system using a custom-fabricated soft elastomeric capacitive sensor array in conjunction with image processing and machine learning techniques. The system integrates Otsu’s thresholding, Connected Component Labeling, and a tailored cluster-tracking algorithm for anomaly detection, enabling real-time localization within 1 ms. A $6\times 6$ Dragon Skin-based sensor array was fabricated, embedded with copper yarn electrodes, and evaluated using a UR3e robotic arm and a Schunk force-torque sensor to generate controlled stimuli. The fabricated tactile sensor measures the applied force from 1 to 3 N. Sensor output was captured via a MUCA breakout board and Arduino Nano 33 IoT, transmitting the Ratio of Mutual Capacitance data for further analysis. A Python-based processing pipeline filters and visualizes the data with real-time clustering and adaptive thresholding. Machine learning models such as linear regression, Support Vector Machine, decision tree, and Gaussian Process Regression were evaluated to correlate force with capacitance values. Decision Tree Regression achieved the highest performance ($R^2=0.9996, RMSE=0.0446$), providing an effective correlation factor of $51.76$ for force estimation. The system offers robust performance in complex interactions and a scalable solution for soft robotics and prosthetic force mapping, supporting health monitoring, safe automation, and medical diagnostics. Full article

Background/Objectives: This study aimed to examine how uncertainties in inertial properties and minimal sets of inertial parameters (MSIP) affect inverse-dynamics simulations of high-acceleration sport movements and to demonstrate that applying MSIP identified through the free vibration measurement method improves simulation accuracy. Methods: Monte Carlo simulations were performed for running, side-cutting, vertical jumping, arm swings, and leg swings by introducing uncertainties in inertial properties and MSIP. Results: These uncertainties significantly affect the joint torques and ground reaction forces and moments (GRFs&Ms), especially during large angular acceleration. The mass and longitudinal position of the center of gravity had strong effects. Subsequently, MSIP identified by our methods with free vibration measurement were applied to the same tasks, improving the accuracy of the predicted ground reaction forces compared with the standard regression-based estimates. The root mean square error decreased by up to 148 N. Conclusions: These results highlight that uncertainties in inertial properties and MSIP affected the calculated joint torques and GRFs&Ms, and combining experimentally identified MSIP with dynamics simulations enhances precision. These findings demonstrate that utilizing the MSIP from free vibration measurement in inverse dynamics simulations improves the accuracy of dynamic models in sports biomechanics, thereby providing a robust framework for precise biomechanical analyses. Full article

The current trend in machining with robotic arms involves leveraging Industry 4.0 technologies to propose solutions that reduce path deviation errors. This approach presents significant challenges alongside promising advancements, as well as a substantial increase in the cost of future industrial robotic cells, which is not always amortizable. As an alternative or complementary approach to this trend, methods encouraging the occasional use of Industry 4.0 devices for characterizing the behavior of the actual physical cell, calibration, or adjustment are proposed. One such method, called FlePFaM, predicts flatness errors in face milling operations using robotic arms. This is achieved by estimating tool path deviation errors through the integration of a simple model of the robot arm’s mechanics with the cutting forces vector of the process, thereby optimizing machining conditions. These conditions are determined through prior empirical estimations of mass, stiffness, and damping. The conducted tests enabled the selection of the most favorable combination of variables, such as the robot wrist configuration, the position and orientation of the workpiece, and the predominant milling orientation. This led to the identification of the configuration with the lowest absolute flatness error according to the model’s predictions. The results demonstrated a high degree of similarity—between 97% for the closest case and 57% for the farthest case—between simulated and experimental flatness error values. FlePFaM represents a significant step forward in adopting innovative robotic arm solutions for reliable and efficient production. FlePFaM includes dimensional flatness indicators that provide practical support for decision making. Full article

This article investigates a cable-driven experimental setup to simulate elbow joint assistance in the sagittal plane provided by an exosuit. Cable-driven exosuits, particularly fabric-based designs, significantly enhance rehabilitation by enabling targeted joint exercises and promoting functional recovery. To achieve an optimal design, these devices require an analysis of the cable tension, reaction forces, and moments and their dependency on the anchor position. This study presents a cable-driven experimental setup with two rigid bars and variable anchor positions, designed to mimic the human forearm, upper arm, and elbow joint, to evaluate the performance of a potential cable-driven exosuit. Based on the experimental setup, a static model was developed to validate the measured cable tension and estimate the reaction force at the joint and the moments at the anchor positions. Furthermore, based on the observations, an optimization problem was defined to identify optimal anchor positions to improve the exosuit’s design. The optimal position on the forearm and upper arm is studied between 15% and 50% distance from the elbow joint. Our findings suggest that prioritizing user comfort requires both anchor points to be as far away from the elbow joint as possible, i.e., 50% distance, whereas, for optimal exosuit performance, the forearm anchor position can be adjusted based on the joint angle while keeping the upper arm anchor position at the farthest point. The findings in the current work can be used to decide the anchor point position for designing an elbow exosuit. Full article

Objectives: This study aims to determine the propulsive force and effective arm area contributed by the propulsion through the dynamic balance (power balance) between drag and propulsive power in swimming crawl performance. Methods: Ten male swimmers participated in the study. The athletes conducted the crawl trials at a constant velocity using only the upper limbs. Data were collected using a Spectro instrument to measure the drag and 3D video analysis for kinematic of upper limbs movement. Results: The power balance was confirmed through the Bland–Altman estimation (estimated bias 8.5) and was also demonstrated by a one-way analysis of variance that does not show statistical differences. Subsequently, by applying the power balance, the effective propulsive area could be estimated. The result shows an increase of ~8.5% over the value at the hand area used to verify the power balance. This value appears to be attributable to a percentage of the forearm area to propulsive action. Conclusions: This information will allow athletes and coaches to constantly monitor the propulsive force and power, providing useful data on arm movement and swimming technique. Indeed, deeper knowledge about the athlete’s swimming technique can reduce the possibility of suffering micro-traumas in the elbows and shoulders. Full article