Sit-to-Stand (StS) is a fundamental daily activity that can be challenging for stroke survivors due to strength, motor control, and proprioception deficits in their lower limbs. Existing therapies involve repetitive StS exercises, but these can be physically demanding for therapists while assistive devices may limit patient participation and hinder motor learning. To address these challenges, this work proposes the use of two lower-limb exoskeletons to mediate physical interaction between therapists and patients during a StS rehabilitative task. This approach offers several advantages, including improved therapist-patient interaction, safety enforcement, and performance quantification. The whole body control of the two exoskeletons transmits online feedback between the two users, but at the same time assists in movement and ensures balance, and thus helping subjects with greater difficulty. In this study we present the architecture of the framework, presenting and discussing some technical choices made in the design.

Controlling the interaction forces between a human and an exoskeleton is crucial for providing transparency or adjusting assistance or resistance levels. However, it is an open problem to control the interaction forces of lower-limb exoskeletons designed for unrestricted overground walking. For these types of exoskeletons, it is challenging to implement force/torque sensors at every contact between the user and the exoskeleton for direct force measurement. Moreover, it is important to compensate for the exoskeleton's whole-body gravitational and dynamical forces, especially for heavy lower-limb exoskeletons. Previous works either simplified the dynamic model by treating the legs as independent double pendulums, or they did not close the loop with interaction force feedback. The proposed whole-exoskeleton closed-loop compensation (WECC) method calculates the interaction torques during the complete gait cycle by using whole-body dynamics and joint torque measurements on a hip-knee exoskeleton. Furthermore, it uses a constrained optimization scheme to track desired interaction torques in a closed loop while considering physical and safety constraints. We evaluated the haptic transparency and dynamic interaction torque tracking of WECC control on three subjects. We also compared the performance of WECC with a controller based on a simplified dynamic model and a passive version of the exoskeleton. The WECC controller results in a consistently low absolute interaction torque error during the whole gait cycle for both zero and nonzero desired interaction torques. In contrast, the simplified controller yields poor performance in tracking desired interaction torques during the stance phase

Previous implementations of closed-loop peripheral electrical stimulation (PES) strategies have provided evidence about the effect of the stimulation timing on tremor reduction. However, these strategies have used traditional signal processing techniques that only consider phase prediction and might not model the non-stationary behavior of tremor. Here, we tested the use of long short-term memory (LSTM) neural networks to predict tremor signals using kinematic data recorded from Essential Tremor (ET) patients. A dataset comprising wrist flexion-extension data from 12 ET patients was pre-processed to feed the predictors. A total of 180 models resulting from the combination of network (neurons and layers of the LSTM networks, length of the input sequence and prediction horizon) and training parameters (learning rate) were trained, validated and tested. Predicted tremor signals using LSTM-based models presented high correlation values (from 0.709 to 0.998) with the expected values, with a phase delay between the predicted and real signals below 15 ms, which corresponds approximately to 7.5% of a tremor cycle. The prediction horizon was the parameter with a higher impact on the prediction performance. The proposed LSTM-based models were capable of predicting both phase and amplitude of tremor signals outperforming results from previous studies (32--56% decreased phase prediction error compared to the out-of-phase method), which might provide a more robust PES-based closed-loop control applied to PES-based tremor reduction.

This work presents the design and development of a semi-active hybrid orthotic system for support and facilitation of unilateral pathological human walking. The system is based on a novel lower limb orthosis with mechanical control and its combination with non-invasive muscle electrostimulation. The paper presents the concept design and realization of a novel Stance Control Knee Ankle Foot Orthoses (SCKAFO) and the design of Functional Electrical Stimulation (FES) strategies for artificial activation of ankle joint muscles in this hybrid scheme for gait support. In particular, we present the investigation of the effects of electrical stimulation patterns of ankle muscles synchronized in real-time with gait events., on the possible biomechanical alterations to gait in able-bodied individuals (n=8). Bilateral 3D ground reaction forces (GRF) were analyzed between barefoot overground walking with and without FES of ankle muscles. The observed effects of the tested FES strategy are coherent with a physiological gait strategy and compatible with the proposed SCKAFO.

Motor rehabilitation based on the association of electroencephalographic (EEG) activity and proprioceptive feedback has been demonstrated as a feasible therapy for patients with paralysis. To promote long-lasting motor recovery, these interventions have to be carried out across several weeks or even months. The success of these therapies partly relies on the performance of the system decoding movement intentions, which normally has to be recalibrated to deal with the nonstationarities of the cortical activity. Minimizing the recalibration times is important to reduce the setup preparation and maximize the effective therapy time. To date, a systematic analysis of the effect of recalibration strategies in EEG-driven interfaces for motor rehabilitation has not yet been performed. Data from patients with stroke (4 patients, 8 sessions) and spinal cord injury (SCI) (4 patients, 5 sessions) undergoing two different paradigms (self-paced and cue-guided, respectively) are used to study the performance of the EEG-based classification of motor intentions. Four calibration schemes are compared, considering different combinations of training datasets from previous and/or the validated session. The results show significant differences in classifier performances in terms of the true and false positives (TPs) and (FPs). Combining training data from previous sessions with data from the validation session provides the best compromise between the amount of data needed for calibration and the classifier performance. With this scheme, the average true (false) positive rates obtained are 85.3% (17.3%) and 72.9% (30.3%) for the self-paced and the cue-guided protocols, respectively. These results suggest that the use of optimal recalibration schemes for EEG-based classifiers of motor intentions leads to enhanced performances of these technologies, while not requiring long calibration phases prior to starting the intervention.

Inertial measurement units (IMUs) have a long-lasting popularity in a variety of industrial applications from navigation systems to guidance and robotics. Their use in clinical practice is now becoming more common, thanks to miniaturization and the ability to integrate on-board computational and decision-support features. IMU-based gait analysis is a paradigm of this evolving process, and in this study its use for the assessment of Parkinson's disease (PD) is comprehensively analyzed. Data coming from 25 individuals with different levels of PD symptoms severity and an equal number of age-matched healthy individuals were included into a set of 6 different machine learning (ML) techniques, processing 18 different configurations of gait parameters taken from 8 IMU sensors. Classification accuracy was calculated for each configuration and ML technique, adding two meta-classifiers based on the results obtained from all individual techniques through majority of voting, with two different weighting schemes. Average classification accuracy ranged between 63% and 80% among classifiers and increased up to 96% for one meta-classifier configuration. Configurations based on a statistical preselection process showed the highest average classification accuracy. When reducing the number of sensors, features based on the joint range of motion were more accurate than those based on spatio-temporal parameters. In particular, best results were obtained with the knee range of motion, calculated with four IMUs, placed bilaterally. The obtained findings provide data-driven evidence on which combination of sensor configurations and classification methods to be used during IMU-based gait analysis to grade the severity level of PD.

This study present an intervention combining an electroencephalography-based brain computer interface with a hybrid robotic system for the modulation of the cortical excitability (plasticity). Plasticity is intended to be elicited through the association of the voluntary motor-related cortical processes with the hybrid assistance during the execution of reaching movement. The cortical excitability was assessed before and after the intervention measuring the peak-to-peak amplitude of the Motor Evoked Potentials (MEPs) induced through transcranial magnetic stimulation pulses. Five healthy subjects participated in the experiments. Results showed an overall and distributed increase in the cortical excitability as a result of the proposed intervention.

Research on wearable robotic devices is fostered by the need to assist and restore human locomotion, which is essential for most daily life activities. Despite a continuous technological advancement, many challenges are to be faced before establishing the use of these devices as part of the rehabilitation process. A main concern is how to actively engage the users and monitor how they are affected by the exoskeleton. Basic analyses, such as measuring walking speed, must be extended to include analysis on balance, brain activity, and evaluation of neuromechanical effects. This scenario requires the use of different distributed sensor devices that must be carefully synchronized. This work describes the architecture we implemented to extend the capabilities of the H2 exoskeleton. As a proof of the architecture validity, we show the online estimation of users' muscle forces through the acquisition of electromyographic signals, that drive neuromusculoskeletal models. The proposed framework, built upon the Robot Operating System (ROS), aims to be reusable for a wide range of setups, including different exoskeletons and sensors.

Every year, stroke significantly affects thousands of individuals who require rehabilitation to regain lost movements and return to a normal life. Exoskeletons are becoming a very powerful tool to help therapists in this rehabilitation process. This work presents a robotic exoskeleton designed to assist overground gait training for stroke survivors with deficits in gait coordination despite conventional rehabilitation. The device is a bilateral exoskeleton with six degrees of freedom and is designed to implement two control strategies. An adaptive trajectory control has been developed to guide the patient's limb within a desired path, allowing a deviation based on torque of interaction between the user and the exoskeleton. An admittance control strategy allows the robotic platform to capture the user's movements during assistive training and to replicate it during active training. Experimental results show that the exoskeleton can adapt a pre-recorded gait pattern for the gait pattern of a specific user. Future investigations will evaluate the device in the rehabilitation of patients who have suffered from stroke and make a comparative analysis of the effectiveness of different robotic therapies.

Tremor constitutes the most common motor disorder, and poses a functional problem to a large number of patients. Despite of the considerable experience in tremor management, current treatment based on drugs or surgery does not attain an effective attenuation in 25 % of patients, motivating the need for research in new therapeutic alternatives. In this context, this paper presents the concept design, development, and preliminary validation of a soft wearable robot for tremor assessment and suppression. The TREMOR neurorobot comprises a Brain Neural Computer Interface that monitors the whole neuromusculoskeletal system, aiming at characterizing both voluntary movement and tremor, and a Functional Electrical Stimulation system that compensates for tremulous movements without impeding the user perform functional tasks. First results demonstrate the performance of the TREMOR neurorobot as a novel means of assessing and attenuating pathological tremors.

Pathological tremor constitutes the most common movement disorder, and is increasing its prevalence with ageing. Treatment forms range from drugs to surgery in those patients refractory to drugs, however, tremor is not effectively managed in about 25% of patients. According to this, new management techniques such as wearable robots that take advantage of selective biomechanical loading seem an interesting alternative. Our objective is to design robotic exoskeletons which suppress tremor, letting the user perform a voluntary movement, by means of intelligent control approaches that include accurate tremor models. In this context, we propose a two-stage algorithm for real-time estimation of time varying tremor amplitude and frequency. It is based on the assumption that tremor alters voluntary motion in an additive manner, and happens in a higher frequency band. The two-stage algorithm first generates an estimation of voluntary movement based on its inherent slower dynamics, and then removes it from the total motion, directly providing an estimate of tremor. This tremor estimation is then fed into an adaptive filter, which provides instantaneous tremor characteristics. Accurate and robust tremor amplitude and frequency estimates are obtained.

Knee ankle foot orthoses are prescribed to provide stability and maintain lower limb joints at their functional position. Current devices provide stability by locking joints permanently during the unsafe phase of a pathological gait (the stance phase). Though stability is obtained with such orthoses, gait patterns are unnatural and non-cosmetic. Other systems adapt more dynamically during gait, applying different strategies to recover or improve mobility. The system presented consist in a wearable set of sensors, actuators at knee and ankle joints, and a control and monitoring ambulatory unit, all integrated in a custom designed knee-ankle-foot robotic exoskeleton. A base unit allows wireless communication of the ambulatory unit, trough a Bluetooth link, with a PC software platform conceived for on-line and off-line data evaluation. Sensors adapted to the mechanical frame of the orthosis collect kinematics, such as angles at knee and ankle joints, and angular positions and accelerations at lower limb segments; kinetics, such as forces at the orthosis rods and fixation parts, and also foot contact information.

Biomechanical loading, in particular, viscous loading of the upper limb has been proposed in the literature as a means for suppressing pathologic tremor. It is expected that an improvement on manipulative function can be obtained by reducing the tremorous motion associated to some neurological disorders. This article presents two non-grounded control strategies to suppress tremor by means of a orthotic (wearable) exoskeleton. These two strategies are based on biomechanical loading and notch filtering of tremor via internal forces. Both controls strategies are evaluated and validated on the robotic exoskeleton called WOTAS (wearable orthosis for tremor assessment and suppression). At the end, results obtained in the pre-clinical trials and conclusions of this study are presented

Exoskeletal devices can provide assistance and supplement to the biological musculoskeletal members. We present a wearable robotic exoskeleton conceived as an improved lower leg orthosis for compensation of human knee and ankle joints disorders provoked by muscle weaknesses during walking, introducing novel biomechanical and user acceptance aspects and daily performance monitoring functionalities.

There is a need for wearable powered upper limb exoskeletons able to apply forces to the upper limb for use by people with disabilities and/or limb weakness or injury. The robotic exoskeleton called WOTAS (Wearable Orthosis for Tremor Assessment and Suppression) presented in this paper will provide a means of testing non-grounded control strategies in order to help these people. For instance, biomechanical loading, in particular, viscous loading of the upper limb has been proposed in the literature as a means for suppressing pathologic tremor. This article describes in detail the general concept for WOTAS, outlining the special features of the design and selection of system components.

The present paper address the analysis of Ultrasonic motors, UM, as driving technology in impedance controlled robotic manipulators. A focus is given on rehabilitation robotics as an emerging field in which impedance controlled approaches are more suited. The article gives some general remarks on impedance control, briefly introduces a UM driven miniature SCARA robot and describes a model of the manipulator and UM that serves the goal of gaining insight in the expected behavior of the controlled system. The experimental results and evaluation of UM as driving technology make use of a set of figures of merit previously defined. Eventually, some concluding remarks are given.

The application of a repetitive controller with zero phase error tracking compensation (ZPETC) to the speed control problem of travelling wave ultrasonic motors is investigated. The proposed control strategy is aimed at reducing the typical speed ripples of ultrasonic motors as well as compensating the load and temperature changes. Repetitive controllers are capable of improving the response of dynamical systems under periodic disturbances by learning from the stored error valves of preceding periods. Since it is desirable for the stabilizing compensator of repetitive controllers to be zero phase and perfect error tracker, the ZPET controller is a natural choice when the servo's closed-loop transfer function has non-minimum zeros. The performance of the proposed controller is compared against that of a PI-repetitive controller and its effectiveness experimentally demonstrated.

Application of robotics to agriculture involves specific design of manipulators to account for the need of light structures working at high speeds and acceleration, while preserving the environment against damage. All these requirements, mainly the speed and acceleration, define the choice of manipulator's structure and optimize the mechanical design for the manipulator dynamics. This paper presents the specific design of a manipulator for harvesting fresh fruit (oranges). First, we discuss the optimum manipulator structure taking into account all the necessary requirements. We then use, in order to improve the resulting dynamics, the nonlinear performance index defined in a our previous work, to reflect the nonlinear effects on the actuators due to internal parameters of the structure, and extend it to take into account the changing payload for an improved design.