Search

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.

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. Agency for Science, Technology and Research (A*STAR) Published version This work was partially supported by the grant “Intelligent Human-Robot interface for upper limb wearable robots” (Award Number SERC1922500046, A*STAR, Singapore).

In this paper we present a methodology for optimising the design of a metamaterial structure with one degree of freedom that is able to simultaneously bend and stretch. The structure is intended for assisting flexion-extension of the wrist joint. The metamaterial is comprised of serially connected, in- dividually designed cells. The design parameters can be chosen to optimally fit a desired planar curve such as the curvature of the skin on a plane normal to the flexion/extension axis of the wrist joint. A tool for the optimised design is described and the experimental validation of the output is conducted, to show the ability of the mechanism to conform to a 2D curve, also exhibiting a change in length, which is desirable for reducing sliding and shear on the skin. The design tool allows the generation of metamaterials optimised for a multitude of other applications where actuated mechanisms must be worn by a user for rehabilitation or assistance purposes. Agency for Science, Technology and Research (A*STAR) Nanyang Technological University Accepted version This research was partially supported by the Singapore Agency for Science, Technology and Research (A*STAR) under the project ”Intelligent Human-Robot interface for upper limb wearable robots” (Award Number SERC 1922500046) and by NTU SUG grant - ”Building blocks for next generation soft robots”.

In this paper, we present the modelling of a hyper-redundant mechanism for assisting fingers in achieving desired configurations. The device is composed of serially linked rotating rigid elements and is actuated by a cable-driven mechanism that can provide both flexion and extension. The modelling is based on the analysis of the static equilibrium configurations that the structure acquires, which are dependent on the design parameters of each segment and on the ratio of the tensions applied on the cables. We show how it is possible to obtain different configurations by properly shaping the geometry of each element. Such a relationship was theoretically investigated and experimentally validated to compare the actual configuration with the one predicted by the model. The prototype was designed so that its static equilibrium configuration approximates the geometry of a finger during flexion-extension. It was observed that the customised structure achieves a configuration more closely resembling the desired one than a non-customised actuator. By adapting the design of each element and properly adjusting the ratio of tensions, one is able to compute the final configuration achieved by the actuator so that it can approximate the desired posture.

This paper proposes a detection method to identify the drill bit position during assisted bone drilling. A platform has been developed to support the surgeon in the planning of the drilling path. Albeit constrained along a fixed trajectory, the advancement of the drilling tool is manual, in order to preserve the natural haptic perception of the surgeon, who remains in charge of modulating the drilling force and the feeding rate according to the position of the drill bit in the bone. This paper describes a custom drill, embedded with force and position sensors, which allows the evaluation of a new parameter, referred to as Average Impedance (AI), that is related to the mechanical properties of the tissues in contact with the drill bit. An algorithm for layer identification has been implemented based on the variability of the AI signal. In perspective, the AI can provide the surgeon with additional information about the position of the drill bit in the bone, in order to increase the safety level of the procedure. The algorithm has been tested ex-vivo on swine bones. The tests demonstrated a reliability better than 80% in the discrimination of bone tissues.

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. Published version

Humans are favoured to conventional robotics for some tasks in industry due to their increased dexterity and fine motor skills, however, performance of these tasks can result in injury to the user at a cost to both the user and the employer. In this paper we describe a lightweight, upper-limb exosuit intended to assist the user during lifting tasks (up to 10kg) and while operating power tools, which are common activities for industrial workers. The exosuit assists elbow and shoulder flexion for both arms and allows for passive movements in the transverse plane. To achieve the design criteria an underactuated mechanism has been developed, where a single motor is used to assist two degrees of freedom per arm. In the intended application, the hands are generally busy and cannot be used to provide inputs to the robot, therefore, a voice-activated control has been developed that allows the user to give voice commands to operate the exosuit. Experiments were performed on 5 healthy subjects to assess the change in Muscular Activation (MA), inferred through Electromyography (EMG) signals, during three tasks: i) lifting and releasing a load; ii) holding a position and iii) manipulating a tool. The results showed that the exosuit is capable of reducing EMG activity (between 24.6% and 64.6%) and the recognition rate (94.8%) of the voice recognition module was evaluated. This work was partially supported by the "Smart Exosuits for Human Augmentation in Industrial Working Environment" project funded by Parts Precision Technology (S) Pte Ltd (project definition: M4062147).

Wearable robotic devices and exoskeletons, that assist human beings in physically-demanding tasks have the potential to both increase productivity and reduce the risk of musculoskeletal disorders. Soft exoskeletons, known as exosuit, provide improved portability and fit. While many exosuits are populating the market to support light weights, very few are powerful enough to reinforce workers in lifting heavy loads. Adopting the advantages of novel soft-robotic principles, we propose a voice-controlled upper limb exosuit designed to aid its user in lifting up to 10kg per arm. The exosuit uses a wire-driven mechanism to transmit power from a proximally-located actuation stage to the shoulder and elbow. Forces are transmitted to the human body via soft, textile-based components. We evaluate the impact of the device on the muscular effort of a wearer in both a lifting and a holding task. Holding a weight of 14kg with the exosuit results in an average reduction in muscular effort of the biceps brachii and anterior deltoid of 50% and 68%, respectively. Similarly, lifting a weight of 7kg with the exosuit reduces the muscular activity of the same two muscles by 23.4% and 41.2%, respectively.

The aim of this work is to present a novel wearable mechatronic device (called PDMeter) designed to objectively assess the wrist rigidity in Parkinson's Disease (PD) patients. The system is low-weight, long-term wearable and portable in order to i) perform clinical assessments during Activities of Daily Living (ADLs) in unstructured environments, and ii) to provide several rigidity measurements per day. In this scenario, we defined two different working modalities: i) measurement mode, in which the system measures the wrist rigidity, and ii) backdrivable mode, in which it does not measure, but it has to be transparent for the user during ADLs. In this paper we present the overall mechatronic design of the PDMeter, including the kinematic structure, the actuation system, the sensory system (both force and position) and the control electronics. The overall structure is optimized in terms of dimension and weight: the design of the electronic system allow to integrate in a single compact PCB both the control system and the wireless communication with an external device (laptop or smartphone); the mechanical structure, characterized by one active degree of freedom and five passive ones, is entirely made of aluminium alloy (A16082), and the whole system with the electronics embedded has an overall mass of about 0.46 kg. Future efforts will focus on the implementation and testing of the most suitable algorithms to assess the wrist rigidity, and their validation in clinical trials.

Systems for delivering robot-mediated rehabilitation therapy are useful tools to support clinicians in providing high-intensity, repetitive, and task-specific treatment, in order to maximize the efficacy of the recovery process, especially for neurologically injured patients. Such systems are typically conceived to constantly work in constrained motion with the human body. This is a quite challenging situation for designers: physical human-robot interaction should be properly exploited and optimized, starting from the definition of adequate functional and technical specifications of the overall system up to the integration of some level of real-time, adaptive behavior of the robot that should feature an effective performance while dealing with uncertainty and complexity associated with physiopathologic conditions of human subjects. A top-down, biomechatronic design approach is consequently required to develop systems for robot-mediated therapy: in-depth modeling of the human agent, that is, the patient, must be the first step that provides basic guidance for subsequently running a typical concurrent, iterative mechatronic design cycle of mechanical, electronic, and multilayered control subsystems of the robotic therapeutic machine. The present chapter has the aim to provide an overview on biomechatronic design criteria of generic systems for robot-mediated rehabilitation therapy focused on both the upper and lower extremities. First, basic guidelines on how to build an appropriate model of the human component instrumental for robot design are briefly discussed. Then, criteria for the identification of functional and technical specifications and the selection of key components of the robotic system as a result of the application of a truly biomechatronic approach are introduced. Finally, two design case studies are presented: (a) the CBM-Motus, a planar robot for upper-limb poststroke rehabilitation, and (b) LENAR, a nonanthropomorphic wearable robot for walking assistance.

Underactuated compliant swimming robots are characterized by a simple mechanical structure, capable to mimic the body undulation of many fish species. One of the design issue for these robots is the generation and control of best performing swimming gaits. In this paper we propose a new controller, based on AFO oscillators, to address this issue. After analyzing the effects of the motion on the robot natural frequencies, we show that the closed loop system is able to generate self-sustained oscillations, at a characteristic frequency, while maximizing swimming velocity.

Mozzarella cheese is one of the most counterfeited dairy product worldwide, with implications concerning either economical and health issues. In the context of food quality monitoring and authentication, a non-destructive multi-sensorial analytic approach is proposed. A minimally invasive evaluation of the mechanical properties of the mozzarella cheese as well as of its preserving whey has been performed by means of a mechanical and a liquid sensor, respectively. Data fusion of both approaches allowed a good and easy discrimination between mozzarella cheese samples having different brand provenance and milk composition.

The paper presents the development of segmented artificial crawlers endowed with passive hook-shaped frictional microstructures. The goal is to find design rules for fabricating biomimetic, adaptable and mobile machines mimicking segmented animals with hydrostatic skeleton, and intended to move effectively along unstructured substrates. The paper describes the mechanical model, the design and the fabrication of a SMA-actuated segmented microrobot, whose locomotion is inspired by the peristaltic motion of Annelids, and in particular of earthworms (Lumbricus Terrestris). Experimental locomotion performance are compared with theoretical performance predicted by a purposely developed friction model -taking into account design parameters such as number of segments, body mass, special friction enhancement appendixes--and with locomotion performance of real earthworms as presented in literature. Experiments indicate that the maximum speed of the crawler prototype is 2.5 mm/s, and that 3-segment crawlers have almost the same velocity as earthworms having the same weight (and about 330% their length), whereas 4-segment crawlers have the same velocity, expressed as body lengths/s, as earthworms with the same mass (and about 270% their length).

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.

Impaired gait can be restored to its physiological level using wearable robotic systems acting alongside human lower limbs and providing assistive forces that adapt to the residual sensory-motor capabilities of the wearer. Such systems can be used as assistive aids (to overcome disabilities or age-related impairments) or as rehabilitation tools (to restore physical and neurological abilities through proper training). In order to elicit physiological gait as a behavior emerging from the interaction between the robot and the user, the robot morphology must be considered as an open design variable, thus relaxing the constraint of using basically anthropomorphic architectures. This chapter deals with several design aspects related to this approach for the development of lower limbs wearable robots. In particular, it analyzes kinematic compatibility issues, possible topological connections among robotic elements, morphological optimization of robot properties, actuation solutions with series compliance, interaction control schemes and user’s intention detection strategies. Pilot tests on a novel non-anthropomorphic device, developed according to the proposed approach, are presented as a case-study, exemplifying the main design aspects described within the chapter.

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.

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 · 103 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.

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.

With the aim of developing new solutions for powering pneumatic actuators in the field of wearable robotics, a principle scheme of a pressurized gas supply unit (GSU) based on hydrogen peroxide dec...

Computer-aided tissue engineering comprises a set of methodologies for the fabrication of prosthetic implants by means of rapid prototyping technologies. The paper presents a technique for the preparation of bioactive implants intended to be used in combination with mesenchymal stem cells for the regeneration of bone tissue. The methodology uses additive manufacturing of scaffolds according to a topological optimization algorithm and combines it with chemical functionalization protocols to increase material bioactivity.

The successful introduction of robotic technologies in the rehabilitation arena critically depends on the possibility to design machines able to operate in symbiosis with patients, i. e., adapting the level of assistance to their residual abilities. Safety, easiness of use, and flexibility are key factors for these systems, which typically operate in continuous physical contact with the human body.

Introduction: The kinematic structure of wearable robots designed for lower limb rehabilitation usually replicates that of the human body (anthropomorphism), thus kinematic incompatibilities may arise from the mismatch between robotic and human joint axes of rotation. Conversely, non-anthropomorphic structures can be designed to be robust against misalignments, thus minimizing undesired interaction forces [1]. A non-anthropomorphic treadmill-based robot for hip and knee flexion/extension assistance, using compliant actuators [2] and deriving human joint kinematic patterns with forward kinematics, has been previously presented [3] and [4]. Inaccuracies in the mapping of rotations from the robot joint space to the human joint space may arise from: (1) human segments length estimation errors, which affect the human–robot parallel kinematic chain, (2) non-rigid human–robot connections and (3) uncertainty associated to the position of fastening points. The aim of this work is to validate the human joints kinematics estimated by the robot (RK) using that provided by inertial measurement units (IK) as reference. Methods:Ahealthy subject (male, 28 y.o.) walked on a treadmill at 0.55, 0.7 and 0.85 m/s wearing the robot. Four inertial measurement units (IMUs – Xsens, MTX, sampling frequency 50 Hz) were attached to the pelvis, thigh, and shank cuffs and to the foot on the right side (Fig. 1). The RK of hip and knee were estimated from the encoders measurements (sampling frequency 200 Hz) using the third-order polynomial approximation of the forward kinematic function derived in [3]. At the beginning of each trial, while the subject was standing (reference configuration), thigh and shank IMUs were mathematically aligned to the pelvis IMU. Hip and knee IK were estimated in terms of sagittal components of the relevant orientation vectors provided by the IMUs. Gait cycles were identified using the acceleration peaks at heel strike. Data from the encoders and IMUs were synchronized using an external trigger. The root mean square deviation (RMSD) between hip and knee RK and IK was computed for the three trials over 10 gait cycles. Results: Representative patterns of hip and knee angles in the sagittal plane (treadmill velocity: 0.55 m/s) are reported. RMSD values averaged over the three different gait speeds were 2.9±0.2◦ for the hip and 4.9±1.2◦ for the knee (mean±sd). In all the trials, maximum discrepancies were found at the peak values (RK peak extension angles were overestimated). Discussion: Non-anthropomorphic robots can offer several advantages in terms of wearability but they may introduce some inaccuracies in the estimation of human joints kinematics. However, results showed that hip and knee RK errors were within levels acceptable in the majority of clinical applications. Moreover, the systematic errors observed can be corrected through a proper calibration of the robot-to-human mapping. The estimate of lower limb kinematics from IMUs integrated in the robotic cuffs could facilitate the control of the robot.

Insects exhibit remarkable navigation capabilities that current control architectures are still far from successfully mimic and reproduce. In this chapter, we present the results of a study on conceptualizing insect/machine hybrid controllers for improving autonomy of exploratory vehicles. First, the different principally possible levels of interfacing between insect and machine are examined followed by a review of current approaches towards hybridity and enabling technologies. Based on the insights of this activity, we propose a double hybrid control architecture which hinges around the concept of "insect-in-a-cockpit." It integrates both biological/artificial (insect/robot) modules and deliberative/reactive behavior. The basic assumption is that "low-level" tasks are managed by the robot, while the "insect intelligence" is exploited whenever high-level problem solving and decision making is required. Both neural and natural interfacing have been considered to achieve robustness and redundancy of exchanged information.

Recent research in the emerging field of phenomics aims at developing unobtrusive and ecological technologies which allow monitoring the behavior of infants and toddlers. Orientation tracking devices based on accelerometers and magnetometers represent a very promising technology since orientation in 3D space can be derived by solely relying upon the direction of the natural geomagnetic and gravitational fields which constitute an absolute coordinate frame of reference, i.e. sourceless. Many commercially available devices allow on-board calibration by means of addition of external circuitry, mainly used to generate artificial fields which act on the sensor itself as a known forcing input. Addition of external circuits is a major drawback in applications such as the one of interest, where the technology has to be worn by infants. When external fields, (e.g. gravitational and geomagnetic fields) are present, alternative calibration techniques are possible which rely on predefined orientation sequences of the sensor. In standard procedures, prior knowledge of the external field (magnitude and direction) as well as accuracy in performing the predefined orientation sequences contribute to determine the calibration parameters. In this work, a novel procedure for in-field calibration of magnetometric sensors is presented which does not rely on previous knowledge of magnitude and direction of the geomagnetic field and which does not require accurately predefined orientation sequences. Such a method proves especially useful in clinical applications since the clinician is no longer compelled to execute accurate calibration protocols

In the last few years mechatronic implantable drug delivery systems have been found to be suitable for a large variety of applications for the treatment of pathologies such as cancer, diabetes or neurological diseases. In particular, a large part of the aforementioned pathologies asks for direct drug delivery at brain level. Unfortunately, the field of biomechatronic systems fitted for a direct infusion of drugs into the brain is still in its infancy. In this paper, as a design starting point, we examine a conventional system for drug delivery against typical medical requirements for brain applications; then we try and define the architecture of a biomechatronic system for drug delivery into the cerebral compartment, taking into account the close interaction with the anatomical compartment and its physiological constraints. A model of the whole system is proposed, and some simulation results are given, together with a simple control strategy

The basic idea underlying this research is that the performances of wearable robots (WR) might be improved by optimizing the dynamics of the system comprised of the robot and the human body wearing it. This problem is not amenable to analytical investigations, and it asks for suitable numerical techniques able to simultaneously account for both robot mechanical structure dynamics and control laws. This paper presents on-going research efforts oriented to demonstrate a novel methodology for the design of an active lower limbs orthosis.

Robots have been used in physical rehabilitation to increase the intensity of practice and relieve therapists from the demanding task of manually assisting the patient. Robot-assisted therapy has shown encouraging results, comparable to the ones achieved with traditional therapy, while allowing greater patient compliance and a quantitative, more accurate monitoring of the subject’s performance. Unfortunately, currently available robotic platforms are not logistically capable of following the patient after discharge from physical therapy. Patients go back home, training stops and their conditions plateau or even deteriorate. The recent introduction of soft materials to design robotic devices has hada significant impact on assistive technologies. Wearable robots made of fabric and elastomers, also known as exosuit, are a promising way of delivering power to the human body, with potential applications in the medical field. Being lightweight, ergonomic and low power-demanding, exosuits are an attractive tool to provide assistance, not only in clinical settings but also in daily life. In this thesis, we present the development and evaluation of a wearable exosuit for assistance of the elbow joint and introduce a controller that compensates for gravitational forces acting on the limb while allowing the suit to move cooperatively with its wearer. We examine the feasibility of using the device to assist human movement by testing its effect on the kinetics and kinematics of healthy subjects. Doctor of Philosophy

Diseases or trauma affecting either the sensory or motor functions in humans leads to movement impairment. They severely affect the independence of the user and the ability to perform activities of daily living. Assistive technologies aim to function in parallel to the affected human body and provide assistance. However, it is important for the robots to understand the user’s motion intention so as to provide assistance in the desired movement; the intention detection is especially challenging in the case of upper-limb motions which are primarily involved in performing dexterous manipulation tasks. Myoelectric signals are capable of providing relevant information about the intent of motion and the extent of effort applied by a person. As such, it is a practically viable solution to utilize electromyographic (EMG) signals to build intuitive human-machine interfaces for applications in prosthetics, orthotics, tele-manipulation and functional electrical stimulation. However, there are quite a lot of challenges with regards to reliably translating the human intention for functional use and efficient control of a multifunctional device. The primary reason is that the EMG signals are time-varying and noisy. Moreover, there is a complex non-linear relationship between the numerous muscles and the corresponding output forces. The aim of this thesis is in providing improvements and solutions to some of the limitations in decoding myoelectric signals, and the work is centered around four themes with focus on the upper limb motions. The first goal is to identify strategies for improving the reliability of myoelectric-based motion decoding. We explored the use of extreme learning machines and quantified its performance for online decoding and evaluating the differences in accuracy while using both muscle synergy and time-domain based features. Secondly, our focus is on building efficient algorithms by using dimensionality reduction techniques to simplify the control complexity; we explored the use of non-negative matrix factorization and linear discriminant analysis for improving and enhancing the decoding capability of EMG-based control interfaces. The third objective is to incorporate simultaneous decoding capability, thereby enable dexterous control of the device by the user. We developed and evaluated the decoding performance of an algorithm capable of classifying both simple and compound movements, by recording only the EMG activity associated to simple movements. The final goal is to implement a user-modulated position and stiffness control of an exoskeleton device, to transfer the impedance characteristics of the human to the device, thereby enabling better transparency and safety in applications involving human-machine interactions. We evaluated the efficacy of simple models to identify the stiffness characteristics from the user's EMG signals, in a trajectory tracking task. Doctor of Philosophy