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Pathological tremors can often be debilitating to activities of daily living and significantly affect the quality of life. Such tremulous movements are commonly observed in wrist flexion-extension (FE). To suppress this tremor we present a wearable robot (WR) with a customized mechanical metamaterial (MM) as the physical human-robot interface (pHRI). The MM is optimized to conform to the user’s wrist posture and follow the hand’s Cartesian trajectory. This is done to minimize the shear between the pHRI and the user’s skin and consequently improve wearability. This WR is then used to effect a viscous tremor suppression using the velocity of the user’s wrist FE. We present a model for the interaction between the WR and the user with which we develop the viscous damping approach for tremor. This is then evaluated in simulation and using a dedicated test bed. This tremor suppression approach demonstrates an attenuation of 20–30 dB at various tremulous frequencies resulting in significantly lower tremor amplitudes due to the viscous damping.

Stroke can be a devastating condition that impairs the upper limb and reduces mobility. Wearable robots can aid impaired users by supporting performance of Activities of Daily Living (ADLs). In the past decade, soft devices have become popular due to their inherent malleable and low-weight properties that makes them generally safer and more ergonomic. In this study, we present an improved version of our previously developed gravity-compensating upper limb exosuit and introduce a novel hand exoskeleton. The latter uses 3D-printed structures that are attached to the back of the fingers which prevent undesired hyperextension of joints. We explored the feasibility of using this integrated system in a sample of 10 chronic stroke patients who performed 10 ADLs. We observed a significant reduction of 30.3 ± 3.5% (mean ± standard error), 31.2 ± 3.2% and 14.0 ± 5.1% in the mean muscular activity of the Biceps Brachii (BB), Anterior Deltoid (AD) and Extensor Digitorum Communis muscles, respectively. Additionally, we observed a reduction of 14.0 ± 11.5%, 14.7 ± 6.9% and 12.8 ± 4.4% in the coactivation of the pairs of muscles BB and Triceps Brachii (TB), BB and AD, and TB and Pectoralis Major (PM), respectively, typically associated to pathological muscular synergies, without significant degradation of healthy muscular coactivation. There was also a significant increase of elbow flexion angle ( $12.1\pm 1.5^\circ $ ). These results further cement the potential of using lightweight wearable devices to assist impaired users.

Haptics involves human touch sensing and tactile feedback and plays a crucial role in physical interactions of humans with their environment. There is an ever‐increasing interest in development of haptic technologies, due to their role in various applications such as robotics, virtual and augmented reality, healthcare, and smart electronics. Electrically driven actuation mechanisms for soft materials like dielectric elastomer actuators (DEAs), electrohydraulic soft actuators (ESAs), ionic polymer−metal composites (IPMCs), and liquid crystal elastomers (LCEs) hold the potential for the development of the next generation of haptic feedback devices due to a variety of advantages such as light weight and compact design, untethered activation and control, large actuation strains, and distributed and localized actuation. Herein, a detailed look is taken at the advancement in material designs for these electrically driven soft actuators. A detailed analysis of the different strategies for improving the electromechanical performance of existing material systems is presented. Approaches adopted to synthesize novel material systems are explained. Advancements in compliant electrode materials for the electrically driven soft actuators are also described. The conclusion reflects on the main challenges in the field and provides perspectives on recent advancements expected to have a significant impact.

The lack of intuitive and active human–robot interaction makes it difficult to use upper-limb-assistive devices. In this paper, we propose a novel learning-based controller that intuitively uses onset motion to predict the desired end-point position for an assistive robot. A multi-modal sensing system comprising inertial measurement units (IMUs), electromyographic (EMG) sensors, and mechanomyography (MMG) sensors was implemented. This system was used to acquire kinematic and physiological signals during reaching and placing tasks performed by five healthy subjects. The onset motion data of each motion trial were extracted to input into traditional regression models and deep learning models for training and testing. The models can predict the position of the hand in planar space, which is the reference position for low-level position controllers. The results show that using IMU sensor with the proposed prediction model is sufficient for motion intention detection, which can provide almost the same prediction performance compared with adding EMG or MMG. Additionally, recurrent neural network (RNN)-based models can predict target positions over a short onset time window for reaching motions and are suitable for predicting targets over a longer horizon for placing tasks. This study’s detailed analysis can improve the usability of the assistive/rehabilitation robots.

This paper proposes a novel control algorithm for torque-controlled exoskeletons assisting cyclic movements. The control strategy is based on the injection of energy parcels into the human-robot system with a timing that minimizes perturbations, i.e., when the angular momentum is maximum. Electromyographic activity of main flexor-extensor knee muscles showed that the proposed controller mostly favors extensor muscles during extension, with a statistically significant reduction in muscular activity in the range of 10–20% in 60 out of 72 trials (i.e., 83%), while no effect related to swinging speed was recorded (speed variation was lower than 10% in 92% of the trials). In the remaining cases muscular activity increment, when statistically significant, was less than 10%. These results showed that the proposed algorithm reduced muscular effort during the most energetically demanding part of the movement (the extension of the knee against gravity) without perturbing the spatio-temporal characteristics of the task and making it particularly suitable for application in exoskeleton-assisted cyclic motions.

Localisation of geometric features like holes, edges, slots, etc. is vital to robotic planning in industrial automation settings. Low-cost 3D scanners are crucial in terms of improving accessibility, but pose a practical challenge to feature localisation because of poorer resolution and consequently affect robotic planning. In this work, we address the possibility of enhancing the quality of a 3D scan by a manual ’touch-up’ of task-relevant features, to ensure their automatic detection prior to automation. We propose a framework whereby the operator (i) has access to both the actual work-piece and its 3D scan; (ii) evaluates the missing salient features from the scan; (iii) uses a haptic stylus to physically interact with the actual work-piece, around such specific features; (iv) interactively updates the scan using the position and force information from the haptic stylus. The contribution of this work is the use of haptic mismatch for geometric update. Specifically, the geometry from the 3D scan is used to predict haptic feedback at a point on the work-piece surface. The haptic mismatch is derived as a measure of error between this prediction and the real interaction forces from physical contact at that point on the work-piece. The geometric update is driven until the haptic mismatch is minimised. Convergence of the proposed algorithm is first numerically verified on an analytical surface with simulated physical interaction. Error analysis of the surface position and orientations were also plotted. Experiments were conducted using a motion capture system providing sub-mm accuracy in position and a 6 axis F/T sensor. Missing features are successfully detected after the update of the scan using the proposed method in an experiment.

Motion intention detection is fundamental in the implementation of human-machine interfaces applied to assistive robots. In this paper, multiple machine learning techniques have been explored for creating upper limb motion prediction models, which generally depend on three factors: the signals collected from the user (such as kinematic or physiological), the extracted features and the selected algorithm. We explore the use of different features extracted from various signals when used to train multiple algorithms for the prediction of elbow flexion angle trajectories. The accuracy of the prediction was evaluated based on the mean velocity and peak amplitude of the trajectory, which are sufficient to fully define it. Results show that prediction accuracy when using solely physiological signals is low, however, when kinematic signals are included, it is largely improved. This suggests kinematic signals provide a reliable source of information for predicting elbow trajectories. Different models were trained using 10 algorithms. Regularization algorithms performed well in all conditions, whereas neural networks performed better when the most important features are selected. The extensive analysis provided in this study can be consulted to aid in the development of accurate upper limb motion intention detection models.

The interaction of immune cells with drugs and/or with other cell types should be mechanistically investigated in order to reduce attrition of new drug development. However, they are currently only limited technologies that address this need. In our work, we developed initial but significant building blocks that enable such immune-drug studies. We developed a novel microfluidic platform replicating the Lymph Node (LN) microenvironment called LN-on-a-chip, starting from design all the way to microfabrication, characterization and validation in terms of architectural features, fluidics, cytocompatibility, and usability. To prove the biomimetics of this microenvironment, we inserted different immune cell types in a microfluidic device, which showed an in-vivo-like spatial distribution. We demonstrated that the developed LN-on-a-chip incorporates key features of the native human LN, namely, (i) similarity in extracellular matrix composition, morphology, porosity, stiffness, and permeability, (ii) compartmentalization of immune cells within distinct structural domains, (iii) replication of the lymphatic fluid flow pattern, (iv) viability of encapsulated cells in collagen over the typical timeframe of immunotoxicity experiments, and (v) interaction among different cell types across chamber boundaries. Further studies with this platform may assess the immune cell function as a step forward to disclose the effects of pharmaceutics to downstream immunology in more physiologically relevant microenvironments.

Tactile sensors are needed for effectively controlling the interaction between a robotic hand and the environment, e.g., during manipulation of objects, or for the tactile exploration of unstructured environments, especially when other sensing modalities, such as vision or audition, become ineffective. In the case of hand prostheses, mainly intended for dexterous manipulation of daily living objects, the possibility of quickly detecting slip occurrence, thus avoiding inadvertent falling of the objects, is prodromal to any manipulation task. In this paper we report on a slip sensor with no-moving parts, based on thermo-electrical phenomena, fabricated on a flexible substrate and suitable for integration on curved surfaces, such as robotic finger pads. Experiments performed using a custom made test bench, which is capable of generating controlled slip velocities, show that the sensor detects slip events in less than 50 ms. This response time is short enough for enabling future applications in the field of hand prosthetics.

Biologically inspired robotic systems can find important applications in biomedical robotics, since studying and replicating human behaviour can provide new insights into motor recovery, functional substitution and human-robot interaction. The analysis of human hand motion is essential for collecting information about human hand movements useful for generalizing reaching and grasping actions on a robotic system. This paper focuses on the definition and extraction of quantitative indicators for describing optimal hand grasping postures and replicating them on an anthropomorphic robotic hand. A motion analysis has been carried out on six healthy human subjects performing a transverse volar grasp. The extracted indicators point to invariant grasping behaviours between the involved subjects, thus providing some constraints for identifying the optimal grasping configuration. Hence, an optimization algorithm based on the Nelder-Mead simplex method has been developed for determining the optimal grasp configuration of a robotic hand, grounded on the aforementioned constraints. It is characterized by a reduced computational cost. The grasp stability has been tested by introducing a quality index that satisfies the form-closure property. The grasping strategy has been validated by means of simulation tests and experimental trials on an arm-hand robotic system. The obtained results have shown the effectiveness of the extracted indicators to reduce the non-linear optimization problem complexity and lead to the synthesis of a grasping posture able to replicate the human behaviour while ensuring grasp stability. The experimental results have also highlighted the limitations of the adopted robotic platform (mainly due to the mechanical structure) to achieve the optimal grasp configuration.

In this paper we report on the design, modeling, experimental testing and scaling analysis of a novel MAgnetic Variable stiffnEess spRIng-Clutch (MAVERIC) device, which may be used as the elastic element of Variable Stiffness Actuators (VSAs). The device, comprising two co-axial diametrically magnetized hollow cylinders, has two degrees of freedom: a rotation of the two cylinders around the common axis and a relative translation along the same axis. For small rotations, the torque arising from the magnetic interaction of the two cylinders is almost linearly proportional to their relative rotation, as in mechanical torsion springs. In addition, the stiffness of the equivalent spring can be varied continuously from a maximum value down to exactly zero by changing the axial overlap of the two cylinders. In this way the proposed device can be used both as a clutch (i.e., perfectly compliant element) and as a variable stiffness torsion spring. A prototype, designed after magnetostatic FEM simulations, has been built and experimentally characterized. The developed MAVERIC has an experimentally determined maximum transmissible torque of 109.81mNm, while the calculated maximum stiffness is 110.2mNmrad −1 . The amplitude of the torque-angle characteristic can be tuned linearly with a sensitivity of 12.63mNmmm −1 rad −1 . Further simulations have been computed parameterizing the geometry and the number of pole pairs of the magnets. The maximum torque density reached for one pole pair is 47.21 · 10 3 Nm m −3 , whereas for a fixed geometry similar to that of the developed prototype, the maximum torque is reached for seven pole pairs. Overall, compared to mechanical springs, MAVERIC has no fatigue or overloading issues. Compared to other magnetic couplers, torsion stiffness can be varied continuously from a maximum value down to exactly zero, when the device acts as a disengaged clutch, disconnecting the load from the actuator.

A safe interaction is crucial in wearable robotics in general, while in assistive and rehabilitation applications, robots may also be required to minimally perturb physiological movements, ideally acting as perfectly transparent machines. The actuation system plays a central role because the expected performance, in terms of torque, speed and control bandwidth, must not be achieved at the expense of lightness and compactness. Actuators embedding compliant elements, such as series elastic actuators, can be designed to meet the above-mentioned requirements in terms of high energy storing capacity and stability of torque control. A number of series elastic actuators have been proposed over the past 20 years in order to accommodate the needs arising from specific applications. This paper presents a novel series elastic actuator intended for the actuation system of a lower limb wearable robot, recently developed in our lab. The actuator is able to deliver 300 W and has a novel architecture making its centre of mass not co-located with its axis of rotation, for an easier integration into the robotic structure. A custom-made torsion spring with a stiffness of 272.25 N·m·rad– 1 is directly connected to the load. The delivered torque is calculated from the measurement of the spring deflection, through two absolute encoders. Testing on torque measurement accuracy and torque/stiffness control are reported.

This paper presents dynamic characterization and control of an upper-limb rehabilitation machine aimed at improving robot performance in the interaction with the patient. An integrated approach between mechanics and control is the key issue of the paper for the development of a robotic machine with desirable dynamic properties. Robot inertial and acceleration properties are studied in the workspace via a graphical representation based on ellipses. Robot friction is experimentally retrieved by means of a parametric identification procedure. A current-based impedance control is developed in order to compensate for friction and enhance control performance in the interaction with the patient by means of force feedback, without increasing system inertia. To this end, servo-amplifier motor currents are monitored to provide force feedback in the interaction, thus avoiding the need for force sensors mounted at the robot end-effector. Current-based impedance control is implemented on the robot; experimental results in free space as well as in constrained space are provided.