Search

Over the past few decades, there have been many studies of human-human physical interaction to better understand why humans physically interact so effectively and how dyads outperform individuals in certain motor tasks. Because of the different methodologies and experimental setups in these studies, however, it is difficult to draw general conclusions as to the reasons for this improved performance. In this study, we propose an open-source experimental system for the systematic study of the effect of human-human interaction, as mediated by robots, at the ankle joint. We also propose a new framework to study various interactive behaviors (i.e., collaborative, cooperative, and competitive tasks) that can be emulated using a virtual spring connecting human pairs. To validate the proposed experimental framework, we perform a transparency analysis, which is closely related to haptic rendering performance. We compare muscle EMG and ankle motion data while subjects are barefoot, attached to the unpowered robot, and attached to the powered robot implementing transparency control. We also validate the performance in rendering virtual springs covering a range of stiffness values (5–50 Nm/rad) while the subjects track several desired trajectories (sine waves at frequencies between 0.1 and 1.1 Hz). Finally, we demonstrate the feasibility of the system in studying human-human interaction under different interactive behaviors.

Optimizing skill acquisition during novel motor tasks and regaining lost motor functions have been the interest of many researchers over the past few decades. One approach shown to accelerate motor learning involves haptically coupling two individuals through robotic interfaces. Studies have shown that an individual's solo performance during upper-limb tracking tasks may improve after haptically-coupled training with a partner. In this study, our goal was to investigate whether these findings can be translated to lower-limb motor tasks, more specifically, during an ankle position tracking task. Using one-degree-of-freedom ankle movements, pairs of participants (i.e., dyads) tracked target trajectories independently. Participants alternated between tracking trials with and without haptic coupling, achieved by rendering a virtual spring between two ankle rehabilitation robots. In our analysis, we compared changes in task performance across trials while training with and without haptic coupling. The tracking performance of both individuals (i.e., dyadic task performance) improved during haptic coupling, which was likely due to averaging of random errors of the dyadic pair during tracking. However, we found that dyadic haptic coupling did not lead to faster individual learning for the tracking task. These results suggest that haptic coupling between unimpaired individuals may not be an effective method of training ankle movements during a simple, one-degree-of-freedom task.

This study proposes and clinically tests intramuscular electrical stimulation below motor threshold to achieve prolonged reduction of wrist flexion/extension tremor in Essential Tremor (ET) patients. The developed system consisted of an intramuscular thin-film electrode structure that included both stimulation and electromyography (EMG) recording electrodes, and a control algorithm for the timing of intramuscular stimulation based on EMG (closed-loop stimulation). Data were recorded from nine ET patients with wrist flexion/extension tremor recruited from the Gregorio Marañón Hospital (Madrid, Spain). Patients participated in two experimental sessions comprising: 1) sensory stimulation of wrist flexors/extensors via thin-film multichannel intramuscular electrodes; and 2) surface stimulation of the nerves innervating the same target muscles. For each session, four of these patients underwent random 60-s trials of two stimulation strategies for each target muscle: 1) selective and adaptive timely stimulation (SATS) - based on EMG of the antagonist muscle; and 2) continuous stimulation (CON) of target muscles. Two patients underwent SATS stimulation trials alone while the other three underwent CON stimulation trials alone in each session. Kinematics of wrist, elbow, and shoulder, together with clinical scales, were used to assess tremor before, right after, and 24 h after each session. Intramuscular SATS achieved, on average, 32% acute (during stimulation) tremor reduction on each trial, while continuous stimulation augmented tremorgenic activity. Furthermore, tremor reduction was significantly higher using intramuscular than surface stimulation. Prolonged reduction of tremor amplitude (24 h after the experiment) was observed in four patients. These results showed acute and prolonged (24 h) tremor reduction using a minimally invasive neurostimulation technology based on SATS of primary sensory afferents of wrist muscles. This strategy might open the possibility of an alternative therapeutic approach for ET patients.

The hippocampus is considered a fundamental cortical structure in the memory process of encoding, retaining, and recalling information. Transcranial Magnetic Stimulation (TMS) following a Paired Associative Stimulation (PAS) protocol is known to induce nervous system excitability enhancement and promote cortical plasticity mechanisms, synchronizing two stimuli in the same neural pathway. However, PAS has not been shown to improve memorization capacity yet. Here, we present an innovative PAS based on the paired application of single-pulse TMS towards the hippocampus synchronized with the endogenous hippocampal activity during a memory task. 96 volunteers were randomized across one experimental group and three parallel groups (motor cortex stimulation, sham stimulation and no stimulation) for a single PAS session. This new configuration of PAS resulted in an increased memorization capacity in the working memory task, dependent on the cortical location of the stimulation and subjects’ basal memory performance. Future research in PAS parameter variability would be required to unveil the PAS derived changes in neural mechanisms that enhance the ability to memorize. These results are potentially significant to clinical research on memory dysfunction and related neurological disorders.

Background Wearable powered exoskeletons are a new and emerging technology developed to provide sensory-guided motorized lower limb assistance enabling intensive task specific locomotor training utilizing typical lower limb movement patterns for persons with gait impairments. To ensure that devices meet end-user needs it is important to understand and incorporate end-users perspectives, however research in this area is extremely limited in the post-stroke population. The purpose of this study was to explore in-depth, end-users perspectives, persons with stroke and physiotherapists, following a single-use session with a H2 exoskeleton. Methods We used a qualitative interpretive description approach utilizing semi-structured face to face interviews, with persons post-stroke and physiotherapists, following a 1.5 h session with a H2 exoskeleton. Results Five persons post-stroke and 6 physiotherapists volunteered to participate in the study. Both participant groups provided insightful comments on their experience with the exoskeleton. Four themes were developed from the persons with stroke participant data: (1) Adopting technology; (2) Device concerns; (3) Developing walking ability; and, (4) Integrating exoskeleton use. Five themes were developed from the physiotherapist participant data: (1) Developer-user collaboration; (2) Device specific concerns; (3) Device programming; (4) Patient characteristics requiring consideration; and, (5) Indications for use. Conclusions This study provides an interpretive understanding of end-users perspectives, persons with stroke and neurological physiotherapists, following a single-use experience with a H2 exoskeleton. The findings from both stakeholder groups overlap such that four over-arching concepts were identified including: (i) Stakeholder participation; (ii) Augmentation vs. autonomous robot; (iii) Exoskeleton usability; and (iv) Device specific concerns. The end users provided valuable perspectives on the use and design of the H2 exoskeleton, identifying needs specific to post-stroke gait rehabilitation, the need for a robust evidence base, whilst also highlighting that there is significant interest in this technology throughout the continuum of stroke rehabilitation.

AimTranscutaneous electrical nerve stimulation (TENS) aims to supplement sensory feedback to improve force steadiness or motor function. In this study, we directly assessed potential changes in corticospinal excitability and motor unit discharge characteristics from the first dorsal interosseous (FDI) muscle due to TENS by using transcranial magnetic stimulation (TMS) and high-density surface electromyography (HDsEMG).MethodsEleven healthy young adults performed a series of submaximal isometric index abductions. We estimated i) motor evoked potential (MEP) amplitudes, ii) persistent inward current amplitudes (PIC, i.e., delta F), iii) motor unit recruitment thresholds and discharge rates, and iv) common synaptic input to motor units before and after TENS.ResultsTENS did not affect force steadiness (2.5 ± 0.9% and 3.3 ± 1.9% (p = 0.010)). MEP amplitudes decreased at 110% of the resting motor threshold (rMT; 0.72 ± 0.66 mV vs. 0.59 ± 0.63 mV; p < 0.001), increased at 130% rMT (1.18 ± 1.10 mV vs. 1.41 ± 1.29 mV; p < 0.001). Delta F increased after TENS (3.7 ± 2.2 pps vs. 4.5 ± 2.6 pps; p = 0.010). We did not find a change in the level of common synaptic input or in the temporal variability of motor unit discharge rates after the session of TENS.ConclusionThese results suggest that TENS can modulate corticospinal excitability through supraspinal and spinal processes and, thus act as a priming technique. At the same time, TENS does not generate short-term changes in the neural control of force in young, healthy adults.

Transcutaneous electrical stimulation has been applied in tremor suppression applications. Out-of-phase stimulation strategies applied above or below motor threshold result in a significant attenuation of pathological tremor. For stimulation to be properly timed, the varying phase relationship between agonist-antagonist muscle activity during tremor needs to be accurately estimated in real-time. Here we propose an online tremor phase and frequency tracking technique for the customized control of electrical stimulation, based on a phase-locked loop (PLL) system applied to the estimated neural drive to muscles. Surface electromyography signals were recorded from the wrist extensor and flexor muscle groups of 13 essential tremor patients during postural tremor. The EMG signals were pre-processed and decomposed online and offline via the convolution kernel compensation algorithm to discriminate motor unit spike trains. The summation of motor unit spike trains detected for each muscle was bandpass filtered between 3 to 10 Hz to isolate the tremor related components of the neural drive to muscles. The estimated tremorogenic neural drive was used as input to a PLL that tracked the phase differences between the two muscle groups. The online estimated phase difference was compared with the phase calculated offline using a Hilbert Transform as a ground truth. The results showed a rate of agreement of 0.88 ± 0.22 between offline and online EMG decomposition. The PLL tracked the phase difference of tremor signals in real-time with an average correlation of 0.86 ± 0.16 with the ground truth (average error of 6.40° ± 3.49°). Finally, the online decomposition and phase estimation components were integrated with an electrical stimulator and applied in closed-loop on one patient, to representatively demonstrate the working principle of the full tremor suppression system. The results of this study support the feasibility of real-time estimation of the phase of tremorogenic neural drive to muscles, providing a methodology for future tremor-suppression neuroprostheses.

ObjectivePrevious studies have demonstrated promising results in estimating the neural drive to muscles, the net output of all motoneurons that innervate the muscle, using high-density electromyography (HD-EMG) for the purpose of interfacing with assistive technologies. Despite the high estimation accuracy, current methods based on neural networks need to be trained with specific motor unit action potential (MUAP) shapes updated for each condition (i.e., varying muscle contraction intensities or joint angles). This preliminary step dramatically limits the potential generalization of these algorithms across tasks. We propose a novel approach to estimate the neural drive using a deep convolutional neural network (CNN), which can identify the cumulative spike train (CST) through general features of MUAPs from a pool of motor units.MethodsWe recorded HD-EMG signals from the gastrocnemius medialis muscle under three isometric contraction scenarios: 1) trapezoidal contraction tasks with different intensities, 2) contraction tasks with a trapezoidal or sinusoidal torque target, and 3) trapezoidal contraction tasks at different ankle angles. We applied a convolutive blind source separation (BSS) method to decompose HD-EMG signals to CST and segmented both signals into windows to train and validate the deep CNN. Then, we optimized the structure of the deep CNN and validated its generalizability across contraction tasks within each scenario.ResultsWith the optimal configuration for the HD-EMG data window (overlap of 20 data points and window length of 40 data points), the deep CNN estimated the CST close to that from BSS, with a correlation coefficient higher than 0.96 and normalized root-mean-square-error lower than 7% with respect to the BSS (golden standard) within each scenario.ConclusionThe proposed deep CNN framework can utilize data from different contraction tasks (e.g., different intensities), learn general features of MUAP variants, and estimate the neural drive for other contraction tasks.SignificanceWith the proposed deep CNN, we could potentially build a neuraldrive-based human-machine interface that is generalizable to different contraction tasks without retraining.

The Paired Associated Stimulation (PAS) protocol has been probed to successfully induce motor cortico-spinal plasticity. However, there is not an official consensus for the optimal Transcranial Magnetic Stimulation (TMS) intensity in PAS applications. The aim of this pilot study is to research the implication of intensity factor during the plasticity induction by PAS in later excitability assessment through each measurable intensity (90, 100, 110, 120 and 130% of the rMT). The results point to a conditioning of the intensity on the Motor Evoked Potentials (MEPs) towards a polarized outcome between high and low frequencies. Regardless, a complete research would be necessary to determine the impact extent of the PAS intensity factor.

Pathological tremor can be a disabling condition that affects millions of people worldwide. Tremor reduction strategies based on peripheral electrical stimulation of afferent pathways are a promising alternative minimizing the adverse effects of traditional solutions, although a better understanding of the targeted mechanisms is required. Here, we performed a pilot study with two stimulation strategies based on bilateral and multi-joint stimulation below motor threshold, which were tested in two patients with Essential Tremor.

Paired Associative Stimulation (PAS) application is a neuromodulation strategy for the promotion of plastic changes and motor rehabilitation. Different stimuli configuration has been applied following a PAS protocol, although using same technical parameters. In this pilot study, we compared three different PAS conditions with a control group. Movement task involved PAS resulted to be less specific although induced excitability potentiation to adjacent muscles. A further methodological research in favour of plastic mechanism understanding and optimization of PAS application will be developed.

BackgroundTranscranial Magnetic Stimulation (TMS) can induce synaptic plasticity potentiation following a paired associative stimulation (PAS) protocol, synchronizing a TMS single pulse with a movement task, named movement-related cortical stimulation (MRCS). However, MRCS plasticity induction and performance potentiation has been related exclusively to single movement tasks.MethodIn order to unveil the changes in motor learning produced by the MRCS protocol in complex movements, associated to Activities of Daily Living (ADL), we induced PAS changes in synchronization with a movement-related dynamic task by performing a customized videogame. We measured the task performance as well as nervous system excitability neuromodulation in 22 healthy subjects, analyzing Reaction Time (RT) and the peak-to-peak amplitude of the Motor Evoked Potentials (MEPs) respectively. The MEPs were recorded in the main task executor muscle, Abductor Pollicis Brevis (APB), and a secondary muscle, Abductor Digiti Minimi (ADM), before, right after, and 30 minutes after the intervention, in a real against sham group experimental parallel design. ResultsPAS application in synchronization with a complex task resulted in a motor performance potentiation effect, inducing shorter RTs when compared to the sham group. Moreover, it triggered long-term corticospinal plasticity mechanisms reflected in a MEP amplitude depression for the APB muscle at the higher intensity of recruitment curve and an enhancement of the corticospinal excitability of ADM muscle at around threshold intensity. RTs and ADM MEP amplitudes correlated positively in around threshold and high intensity assessments.ConclusionsWe conclude that the proposed PAS protocol facilitated the learning of time-accuracy movement in complex movement tasks, even if fatigue could be affecting the executor muscle excitability, and enhanced potentiation towards a passive muscle. This phenomenon can be very useful to develop neurorehabilitation strategies with complex movements (more similar to ADLs) and to avoid maladaptive plasticity related likely to fatigue.

Robot rehabilitation is now recognized as an important method for the efficient recovery. In European Project FP7 BioMot, we have discussed the potential of the robot rehabilitation and proposed the suitable process for it. In this paper, we describe the proposed rehabilitation process and create the theoretical basis for the robot rehabilitation through designing the control system and the patient model. To design the patient model, we describe the source of paralysis and motion controller separately and define the recovery function from the paralysis. In the theoretical analysis of the control system, we show that the robot motions are first adapted to the patient abnormal motions and gradually drive the patient motions to the better ones by the motion support. The singular perturbation analysis proves that the stabilities of the two different process, adaptation to the patient motions and the motion support to the better ones, as a slow motion subsystem and a fast motion subsystem, respectively. The simulation results show that the proposed control system can drive the patients to the better state depending on the patient conditions such as recovery speed and recovery potential. The proposed system can be tuned to fit to the variety of the real patient conditions when we apply it to the real applications., This study was funded by a grant from the European Commission within its Seventh Framework Programme (IFP7-ICT-2013-10-611695: BioMot – Smart Wearable Robots with Bioinspired Sensory-Motor Skills).

Interest in the investigation of novel software and control algorithms for wearable robotics is growing. However, entry into this field requires a significant investment in a testing platform. This work introduces CANopen Robot Controller (CORC)—an open source software stack designed to accelerate the development of robot software and control algorithms—justifying its choice of platform, describing its overall structure, and demonstrating its viability on two distinct platforms.

A flexible simulation platform for human gait with exoskeletons is presented to support the analysis of the human-robot interaction and performance assessment of control strategies design, e.g., assist-as-needed. The platform includes the dynamic modeling of the lower limb’s exoskeleton-human with six degrees of freedom in sagittal plane, actuated by servomotors, where the movement equations are explicitly obtained through the Euler-Lagrange approach. The full model is hybrid and it contains four sub-models that are switched between themselves according to the gait cycle phases; also, it comprises the joint actuators dynamics and the low level control system, this modeling was assembled in Simulink-Matlab. It is shown that the proposed platform is a useful tool for assistance control strategies in the gait rehabilitation. Furthermore, simulation results for the Exo-H3 exoskeleton show the successful tracking of angular trajectories, the level of human participation, and the realistic torques.

Arm cycling is a bimanual motor task used in medical rehabilitation and in sports training. Understanding how muscle coordination changes across different biomechanical constraints in arm cycling is a step toward improved rehabilitation approaches. This exploratory study aims to get new insights on motor control during arm cycling. To achieve our main goal, we used the muscle synergies analysis to test three hypotheses: 1) body position with respect to gravity (sitting and supine) has an effect on muscle synergies; 2) the movement size (crank length) has an effect on the synergistic behavior; 3) the bimanual cranking mode (asynchronous and synchronous) requires different synergistic control. Thirteen able-bodied volunteers performed arm cranking on a custom-made device with unconnected cranks, which allowed testing three different conditions: body position (sitting vs. supine), crank length (10cm vs. 15cm), and cranking mode (synchronous vs. asynchronous). For each of the eight possible combinations, subjects cycled for 30s while electromyography of eight muscles (four from each arm) were recorded: biceps brachii, triceps brachii, anterior deltoid, and posterior deltoid. Muscle synergies in this eight-dimensional muscle space were extracted by nonnegative matrix factorization. Four synergies accounted for over 90% of muscle activation variances in all conditions. Results showed that synergies were affected by body position and cranking mode but practically unaffected by movement size. These results suggest that the central nervous system may employ different motor control strategies in response to external constraints such as cranking mode and body position during arm cycling. NEW & NOTEWORTHY Recent studies analyzed muscle synergies in lower limb cycling. Here, we examine upper limb cycling and specifically the effect of body position with respect to gravity, movement size, and cranking mode on muscle coordination during arm cranking tasks. We show that altered body position and cranking mode affects modular organization of muscle activities. To our knowledge, this is the first study assessing motor control through muscle synergies framework during upper limb cycling with different constraints., This work was funded by the National Research, Development andInnovation Office, Hungary, under Grant GINOP 2.3.2-15-2016-00022 and the 2020-4.1.1-TKP2020 program of the University of Pecs and the European Union’s Horizon 2020 research and innovation program (Project EXTEND—Bidirectional Hyper-Connected Neural System) under Grant 779982

The interindividual variability in the neural drive sent from the spinal cord to muscles is largely unknown, even during highly constrained motor tasks. Here, we investigated individual differences in the strength of neural drive received by the vastus lateralis (VL) and vastus medialis (VM) during an isometric task. We also assessed the proportion of common neural drive within and between these muscles. Twenty-two participants performed a series of submaximal isometric knee extensions at 25% of their peak torque. High-density surface electromyography recordings were decomposed into motor unit action potentials. Coherence analyses were applied on the motor unit spike trains to assess the degree of neural drive that was shared between motor neurons. Six participants were retested ∼20 mo after the first session. The distribution of the strength of neural drive between VL and VM varied between participants and was correlated with the distribution of normalized interference electromyography (EMG) signals (r > 0.56). The level of within- and between-muscle coherence varied across individuals, with a significant positive correlation between these two outcomes (VL: r = 0.48; VM: r = 0.58). We also observed a large interindividual variability in the proportion of muscle-specific drive, that is, the drive unique to each muscle (VL range: 6%-83%, VM range: 6%-86%). All the outcome measures were robust across sessions, providing evidence that the individual differences did not depend solely on the variability of the measures. Together, these results demonstrate that the neural strategies to control the VL and VM muscles widely vary across individuals, even during a constrained task.NEW & NOTEWORTHY We observed that the distribution of the strength of neural drive between the vastus lateralis and vastus medialis during a single-joint isometric task varied across participants. Also, we observed that the proportion of neural drive that was shared within and between these muscles also varied across participants. These results provide evidence that the neural strategies to control the vastus lateralis and vastus medialis muscles widely vary across individuals, even during a mechanically constrained task.

Objectives. This paper aims to investigate the feasibility and the validity of applying deep convolutional neural networks (CNN) to identify motor unit (MU) spike trains and estimate the neural drive to muscles from high-density electromyography (HD-EMG) signals in real time. Two distinct deep CNNs are compared with the convolution kernel compensation (CKC) algorithm using simulated and experimentally recorded signals. The effects of window size and step size of the input HD-EMG signals are also investigated. Approach. The MU spike trains were first identified with the CKC algorithm. The HD-EMG signals and spike trains were used to train the deep CNN. Then, the deep CNN decomposed the HD-EMG signals into MU discharge times in real time. Two CNN approaches are compared with the CKC: (a) multiple single-output deep CNN (SO-DCNN) with one MU decomposed per network, and (b) one multiple-output deep CNN (MO-DCNN) to decompose all MUs (up to 23) with one network. Main results. The MO-DCNN outperformed the SO-DCNN in terms of training time (3.2–21.4 s epoch−1 vs 6.5–47.8 s epoch−1, respectively) and prediction time (0.04 vs 0.27 s sample−1, respectively). The optimal window size and step size for MO-DCNN were 120 and 20 data points, respectively. It results in sensitivity of 98% and 85% with simulated and experimentally recorded HD-EMG signals, respectively. There is a high cross-correlation coefficient between the neural drive estimated with CKC and that estimated with MO-DCNN (range of r-value across conditions: 0.88–0.95). Significance. We demonstrate the feasibility and the validity of using deep CNN to accurately identify MU activity from HD-EMG with a latency lower than 80 ms, which falls within the lower bound of the human electromechanical delay. This method opens many opportunities for using the neural drive to interface humans with assistive devices.

The hypothesis of modular control, which stands on the existence of muscle synergies as building blocks of muscle coordination, has been investigated in a great variety of motor tasks and species. Yet, its role during learning processes is still largely unexplored. To what extent is such modular control flexible, in terms of spatial structure and temporal activation, to externally or internally induced adaptations, is a debated issue. To address this question, we designed a biofeedback experiment to induce changes in the timing of muscle activations during leg cycling movements. The protocol consisted in delaying the peak of activation of one target muscle and using its electromyography (EMG) envelope as visual biofeedback. For each of the 10 healthy participants, the protocol was repeated for three different target muscles: Tibialis Anterioris (TA), Gastrocnemius Medialis (GM), and Vastus Lateralis (VL). To explore the effects of the conditioning protocol, we analyzed changes in the activity of eight lower limb muscles by applying different models of modular motor control [i.e., fixed spatial components (FSC) and fixed temporal components (FTC)]. Our results confirm the hypothesis that visual EMG biofeedback is able to induce changes in muscle coordination. Subjects were able to shift the peak of activation of the target muscle, with a delay of (49 ± 27°) across subjects and conditions. This time shift generated a reorganization of all the other muscles in terms of timing and amplitude. By using different models of modular motor control, we demonstrated that neither spatially invariant nor temporally invariant muscle synergies alone were able to account for these changes in muscle coordination after learning, while temporally invariant muscle synergies with adjustments in timing could capture most of muscle activity adaptations observed after the conditioning protocol. These results suggest that short-term learning in rhythmic tasks is built upon synergistic temporal commands that are robust to changes in the task demands., This research has been supported by Spanish project ASSOCIATE “A comprehensive and wearable robotics based approach to the rehabilitation and assistance of people with stroke and SCI” (DPI2014-58431-C4-1-R), and the H2020 project EUROBENCH “European Robotic Framework for Bipedal Locomotion Benchmarking” (Grant Agreement No. 779963).

Background: Robotic devices have been used to rehabilitate walking function after stroke. Although results suggest that post-stroke patients benefit from this non-conventional therapy, there is no agreement on the optimal robot-assisted approaches to promote neurorecovery. Here we present a new robotic therapy protocol using a grounded exoskeleton perturbing the ankle joint based on tacit learning control. Method: Ten healthy individuals and a post-stroke patient participated in the study and were enrolled in a pilot intervention protocol that involved performance of ankle movements following different trajectories via video game visual feedback. The system autonomously modulated task difficulty according to the performance to increase the challenge. We hypothesized that motor learning throughout training sessions would lead to increased corticospinal excitability of dorsi-plantarflexor muscles. Transcranial Magnetic Stimulation was used to assess the effects on corticospinal excitability. Results: Improvements have been observed on task performance and motor outcomes in both healthy individuals and post-stroke patient case study. Tibialis Anterior corticospinal excitability increased significantly after the training; however no significant changes were observed on Soleus corticospinal excitability. Clinical scales showed functional improvements in the stroke patient. Discussion and Significance: Our findings both in neurophysiological and performance assessment suggest improved motor learning. Some limitations of the study include treatment duration and intensity, as well as the non-significant changes in corticospinal excitability obtained for Soleus. Nonetheless, results suggest that this robotic training framework is a potentially interesting approach that can be explored for gait rehabilitation in post-stroke patients., This research has been funded by the Commission of the European Union under the BioMot project–Smart Wearable Robots with Bioinspired Sensory-Motor Skills (Grant Agreement number IFP7-ICT-2013-10-611695), also under the ASTONISH Project–Advancing Smart Optical Imaging and Sensing for Health (Grant Agreement number H2020-EU.2.1.1.7.-ECSEL-04-2015-692470); with financial support of Spanish Ministry of Economy and Competitiveness (MINECO) under the ASSOCIATE project—A comprehensive and wearable robotics based approach to the rehabilitation and assistance to people with stroke and spinal cord injury (Grant Agreement number 799158449-58449-45-514); and with grant RYC-2014-16613, also by Spanish Ministry of Economy and Competitiveness.

The human being in his blessed curiosity has always wondered how to make machines feel, and, at the same time how a machine can detect emotions. Perhaps some of the tasks that cannot be replaced by machines are the ability of human beings to feel emotions. In the last year, this hypothesis is increasingly questioned by scientists who have done work that seeks to understand the phenomena of brain functioning using the state of the art in instrumentation, sensors, and signal processing. Today, the world scientists have powerful machine learning methods developed to challenge this issue.The field of emotion detection is gaining significance as the technology advances, and particularly due to the current developments in machine learning, the Internet of Things, industry 4.0 and Autonomous Vehicles. Machines will need to be equipped with the capacity to monitor the state of the human user and to change their behaviour in response. Machine learning offers a route to this and should be able to make use of data collected from questionnaires, facial expression scans, and physiological signals such as electroencephalograms (EEG), electrocardiograms, and galvanic skin response. In this study, an approach was proposed to identify the emotional state of a subject from the collected data in the elicited emotion experiments. An algorithm using EEG data was developed, using the power spectral density of the frequency cerebral bands (alpha, beta, theta, and gamma) as features for classifier training. A K Nearest Neighbors algorithm using Euclidian distance was used to predict the emotional state of the subject. This article proposes a novel approach for emotion recognition that not only depends on images of the face, as in the previous literature, but also on the physiological data. The algorithm was able to recognize nine different emotions (Neutral, Anger, Disgust, Fear, Joy, Sadness, Surprise, Amusement, and Anxiety), nine valence positions, and nine positions on arousal axes. Using the data from only 14 EEG electrodes, an accuracy of approximately 97% was achieved. An approach has been developed for evaluating the state of mind of an driver in the context of a semi-autonomous vehicle context, for example. However, the system has a much wider range of potential applications, from the design of products to the evaluation of the user experience.

Improvement of upper extremity function is one of the greatest needs in patients with tetraplegia. Functional electrical stimulation (FES) directly controlled by the patient’s intention, through a brain–machine interface (BMI), could be used with a rehabilitative purpose. To date, there is scarce evidence about the feasibility of these systems in patients with incomplete spinal cord injury (iSCI), as studies usually focus on assistive technologies for complete injuries. The aim of this work is to design a system combining BMI, FES and realistic visual feedback, and test its feasibility as a therapeutic tool for hand rehabilitation of iSCI patients. A system integrating a BMI with FES and visual realistic feedback was developed as a neurorehabilitation tool. Movement-related cortical potentials and event-related desynchronization were used as features and a sparse discriminant analysis (SDA) to classify between rest and motor attempt. Four patients with iSCI performed five therapy sessions with that system in one of their hands only. Initial and final clinical assessments were fulfilled, as well as usability and exertion tests. The system showed a high accuracy, with an average success of 79.13% in rewarding the patients according to their brain activity. There were higher improvements of their prehension in the stimulated hand than in the non-stimulated. All patients reported that they would like to use this application in therapy and they felt very motivated. Our results suggest the feasibility of this integration of technologies to be considered as a therapeutic tool for upper limb rehabilitation. The results, despite being preliminary, provide promising insights for the use of BMI rehabilitation in incomplete SCI patients.

© The Author(s)., [Background]: Brain injury survivors often present upper-limb motor impairment affecting the execution of functional activities such as reaching. A currently active research line seeking to maximize upper-limb motor recovery after a brain injury, deals with the combined use of functional electrical stimulation (FES) and mechanical supporting devices, in what has been previously termed hybrid robotic systems. This study evaluates from the technical and clinical perspectives the usability of an integrated hybrid robotic system for the rehabilitation of upper-limb reaching movements after a brain lesion affecting the motor function. [Methods]: The presented system is comprised of four main components. The hybrid assistance is given by a passive exoskeleton to support the arm weight against gravity and a functional electrical stimulation device to assist the execution of the reaching task. The feedback error learning (FEL) controller was implemented to adjust the intensity of the electrical stimuli delivered on target muscles according to the performance of the users. This control strategy is based on a proportional-integral-derivative feedback controller and an artificial neural network as the feedforward controller. Two experiments were carried out in this evaluation. First, the technical viability and the performance of the implemented FEL controller was evaluated in healthy subjects (N = 12). Second, a small cohort of patients with a brain injury (N = 4) participated in two experimental session to evaluate the system performance. Also, the overall satisfaction and emotional response of the users after they used the system was assessed. [Results]: In the experiment with healthy subjects, a significant reduction of the tracking error was found during the execution of reaching movements. In the experiment with patients, a decreasing trend of the error trajectory was found together with an increasing trend in the task performance as the movement was repeated. Brain injury patients expressed a great acceptance in using the system as a rehabilitation tool. [Conclusions]: The study demonstrates the technical feasibility of using the hybrid robotic system for reaching rehabilitation. Patients’ reports on the received intervention reveal a great satisfaction and acceptance of the hybrid robotic system., This work was supported by the Spanish Ministry of Economy and Competitiveness by the project ASSOCIATE (agreement: DPI2014–58431-C4–1-R) and the HYPERNET network (agreement: DPI2015–71676-REDC).

[Background] In this paper we propose the use of global Kalman filters (KFs) to estimate absolute angles of lower limb segments. Standard approaches adopt KFs to improve the performance of inertial sensors based on individual link configurations. In consequence, for a multi-body system like a lower limb exoskeleton, the inertial measurements of one link (e.g., the shank) are not taken into account in other link angle estimations (e.g., foot). Global KF approaches, on the other hand, correlate the collective contribution of all signals from lower limb segments observed in the state-space model through the filtering process. We present a novel global KF (matricial global KF) relying only on inertial sensor data, and validate both this KF and a previously presented global KF (Markov Jump Linear Systems, MJLS-based KF), which fuses data from inertial sensors and encoders from an exoskeleton. We furthermore compare both methods to the commonly used local KF., [Results] The results indicate that the global KFs performed significantly better than the local KF, with an average root mean square error (RMSE) of respectively 0.942° for the MJLS-based KF, 1.167° for the matrical global KF, and 1.202° for the local KFs. Including the data from the exoskeleton encoders also resulted in a significant increase in performance., [Conclusion] The results indicate that the current practice of using KFs based on local models is suboptimal. Both the presented KF based on inertial sensor data, as well our previously presented global approach fusing inertial sensor data with data from exoskeleton encoders, were superior to local KFs. We therefore recommend to use global KFs for gait analysis and exoskeleton control., This work was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), under Grant 2012/05552–9; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), under Grant 456089/2014–4; the HYPER project of the CONSOLIDER-INGENIO 2010 program of Spain, under Grant CSD2009–00067; the XoSoft project, Soft modular biomimetic exoskeleton to assist people with mobility impairments, contract H2020– ICT24–2016–688175; and by a grant from the Flemish agency for Innovation by Science and Technology (MIRAD, IWT–SBO 120057).

In order to prepare humanoid robots for real-world applications, it is necessary to assess the robots’ locomotion performance and to determine their readiness level. We have carried out an extensive statistical literature review of 204 publications evaluating what kind of benchmarking scenarios and performance indicators are used and if they are sufficient to take a robot from prototype to production. We report the results of our analysis and discuss the most relevant findings such as the overall increase in the number of publications and motion tasks considered. Previous benchmarking efforts have been devoted to the functional assessment of the robotic system itself. A trend towards goal level oriented performance indicators such as robustness to external disturbance and dependability can be observed. We also identify a deficiency in benchmarking of non-functional aspects like safety or human-robot interaction. Based on our results, we outline the creation of a benchmarking framework with respect to current benchmarking approaches also introducing testbeds considering currently neglected motion tasks with a focus on a high degree of standardization.

Background and objective: Robotic exoskeletons have emerged as a promising tool in gait rehabilitation in patients with a spinal cord injury. The aim of this study was to assess the clinical applicability of a new robotic exoskeleton model (Exo H2) in the rehabilitation of people with incomplete spinal cord injury. Material and methods: Exo H2 exoskeleton training was performed for 15 sessions in patients with incomplete subacute spinal cord injury. We analysed the appearance of undesirable events and the patient's perception of pain, fatigue and comfort. In addition, a pilot test was carried out on the possible effectiveness of the device by analysing gait characteristics before and after treatment measured by the 10mWT, the 6mWT, the TUG, the WISCI-II, and the impact on the SCIM III scale.Results: Of a group of 8 patients recruited, we were able to analyse data from 4. No undesirable effects were reported. The VAS value was 2.28 ± 1.55 for pain, 3.75 ± 1.55 for fatigue and 4.17 ± 1.68 for comfort. All values improved on the WISCI-I and the TUG and almost all in the 10MWT and in the 6MWT. Conclusions: The performance of the Exo H2 exoskeleton was robust during a clinical protocol for gait rehabilitation. The treatment was safe, without undesirable effects and with good patient tolerance. These results might justify the performance of clinical trials with an adequate sample size., Antecedentes y objetivo: Los exoesqueletos robotizados han aparecido como una herramienta prometedora en la rehabilitación de la marcha de pacientes con lesión medular. El objetivo es valorar la aplicabilidad en un entorno clínico de un nuevo modelo de exoesqueleto robotizado (Exo H2) en la rehabilitación de la marcha de personas con lesión medular incompleta. Material y métodos: Se realizó el entrenamiento con el exoesqueleto Exo H2 durante 15 sesiones a pacientes con lesión medular incompleta subaguda. Se analizaron la aparición de eventos no deseados, la percepción del paciente en cuanto a dolor, fatiga y comodidad. Además, se realizó una prueba piloto sobre la posible eficacia del dispositivo analizando las características de la marcha antes y después del tratamiento medidas por el 10mWT, el 6mWT, el TUG, el WISCI-II, y la repercusión en la escala SCIM III. Resultados: De los 8 pacientes reclutados, se pudieron analizar los datos de 4. No se reportó ningún efecto no deseado. El valor de EVA sobre el dolor fue de 2,28 ± 1,55; sobre la fatiga fue de 3,75 ± 1,55 y la comodidad fue de 4,17 ± 1,68. Todos mejoraron en el WISCI-II, en el TUG y casi todos en el 10MWT y en el 6MWT. Conclusiones: El funcionamiento del exoesqueleto Exo H2 fue consistente durante un protocolo clínico de rehabilitación de la marcha. Se mostró como una terapia segura, sin efectos no deseados y con buena tolerancia por parte de los pacientes. Estos resultados justifican la realización de ensayos clínicos con un tamaño muestral adecuado.

Comunicació presentada a: International Conference on NeuroRehabilitation (ICNR) celebrat del 13 al 16 d'octubre de 2020 de manera virtual. Wireless active implantable medical devices (AIMDs) can be an alternative for overcoming the drawbacks faced with superficial and percutaneous technologies. However, current AIMDs require bulky and rigid components for powering, hampering their miniaturization. AIMDs based on power transfer by volume conduction do not need these voluminous parts, allowing the development of thread-like devices that could be used for distributed stimulation and sensing of the neuromuscular system. In this paper, we present an in vitro evaluation of a protocol and an architecture for bidirectional communications in networks of injectable wireless implants powered and controlled by volume conduction. The wireless prototypes were successfully addressed from the external systems, and end-to-end bidirectional communication was performed at 256 kbps with a success rate of 87%. This work was funded by the European Union’s Horizon 2020 Research and Innovation Programme (Project EXTEND—Bidirectional HyperConnected Neural System) under grant agreement No 779982. Antoni Ivorra gratefully acknowledges the financial support by ICREA under the ICREA Academia programme.

Pathological tremor is an oscillation of body parts at 3-10 Hz, determined by the output of spinal motor neurons (MNs), which receive synaptic inputs from supraspinal centers and muscle afferents. The behavior of spinal MNs during tremor is not well understood, especially in relation to the activation of the multiple muscles involved. Recent studies on patients with essential tremor have shown that antagonist MN pools receive shared input at the tremor frequency. In this study, we investigated the synaptic inputs related to tremor and voluntary movement, and their coordination across antagonist muscles. We analyzed the spike trains of motor units (MUs) identified from high-density surface electromyography from the forearm extensor and flexor muscles in 15 patients with essential tremor during postural tremor. The shared synaptic input was quantified by coherence and phase difference analysis of the spike trains. All pairs of spike trains in each muscle showed coherence peaks at the voluntary drive frequency (1-3 Hz, 0.2 ± 0.2, mean ± SD) and tremor frequency (3-10 Hz, 0.6 ± 0.3) and were synchronized with small phase differences (3.3 ± 25.2° and 3.9 ± 22.0° for the voluntary drive and tremor frequencies, respectively). The coherence between MN spike trains of antagonist muscle groups at the tremor frequency was significantly smaller than intramuscular coherence. We predominantly observed in-phase activation of MUs between agonist/antagonist muscles at the voluntary frequency band (0.6 ± 48.8°) and out-of-phase activation at the tremor frequency band (126.9 ± 75.6°). Thus MNs innervating agonist/antagonist muscles concurrently receive synaptic inputs with different phase shifts in the voluntary and tremor frequency bands.NEW & NOTEWORTHY Although the mechanical characteristics of tremor have been widely studied, the activation of the affected muscles is still poorly understood. We analyzed the behavior of motor units of pairs of antagonistic wrist muscle groups in patients with essential tremor and studied their activity at voluntary movement- and tremor-related frequencies. We found that the phase relation between inputs to antagonistic muscles is different at the voluntary and tremor frequency bands., This study was supported by European Projects EXTEND Grant H2020-ICT-23-2017-779982 and NeuroTREMOR Grant ICT-2011.5.1-287739. G.Puttaraksa was supported by the Royal Thai Government Scholarship. J. A.Gallego received funding from Community of Madrid Atracción de Talento Grant 2017-T2/TI C-5263. A. Holobar was supported by Slovenian Research Agency Program Funding P2-0041 and Projects J2-7357 and L7-9421.

Wearable robots, such as exoskeletons and soft exosuits, can augment normal function or serve as prostheses for missing limbs. In both cases, they extend, complement, substitute, or enhance human functions and capability and can empower or replace human limbs. Cognitive and physical interactions between human and robot are key for these robots to seamlessly deliver assistance when required. The physical interaction between a robot and its wearer generates forces to overcome the wearer's physical limits, and cognitive interactions allow the wearer to guide and control the robot at all times. On page 668 of this issue, Kim et al. (1) report on a soft exosuit that switches assistance profiles for different physical interaction strategies—in this case, walking versus running—through a versatile cognitive interaction in which algorithms accurately determine and detect the wearer's gait.

Exoskeleton technology has made significant advances during the last decade, resulting in a considerable variety of solutions for gait assistance and rehabilitation. The mechanical design of these devices is a crucial aspect that affects the efficiency and effectiveness of their interaction with the user. Recent developments have pointed towards compliant mechanisms and structures, due to their promising potential in terms of adaptability, safety, efficiency, and comfort. However, there still remain challenges to be solved before compliant lower limb exoskeletons can be deployed in real scenarios. In this review, we analysed 52 lower limb wearable exoskeletons, focusing on three main aspects of compliance: actuation, structure, and interface attachment components. We highlighted the drawbacks and advantages of the different solutions, and suggested a number of promising research lines. We also created and made available a set of data sheets that contain the technical characteristics of the reviewed devices, with the aim of providing researchers and end-users with an updated overview on the existing solutions., This work is supported by the project EUROBENCH (European ROBotic framework for bipedal locomotion bENCHmarking) funded by H2020 Topic ICT 27–2017: “System abilities, SME & benchmarking actions, safety certification” (reference: 779963). We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).

Objective. We propose, design and test a novel thin-film multichannel electrode that can be used for both recording from and stimulating a muscle in acute implants. Approach. The system is built on a substrate of polyimide and contains 12 recording and three stimulation sites made of platinum. The structure is 420 μm wide, 20 μm thick and embeds the recording and stimulation contacts on the two sides of the polyimide over an approximate length of 2 cm. We show representative applications in healthy individuals as well as tremor patients. The designed system was tested by a psychometric characterization of the stimulation contacts in six tremor patients and three healthy individuals determining the perception threshold and current limit as well as the success rate in discriminating elicited sensations (electrotactile feedback). Also, we investigated the possibility of using the intramuscular electrode for reducing tremor in one patient by electrical stimulation delivered with timing based on the electromyographic activity recorded with the same electrode. Main results. In the tremor patients, the current corresponding to the perception threshold and the current limit were 0.7 ± 0.2 and 1.4 ± 0.7 mA for the wrist flexor muscles and 0.4 ± 0.2 and 1.5 ± 0.7 mA for the extensors. In one patient, closed-loop stimulation resulted in a decrease of the tremor power >50%. In healthy individuals the perception threshold and current limits were 0.9 ± 0.6 and 2.1 ± 0.6 mA for the extensor carpi radialis muscle. The subjects could distinguish four or six stimulation patterns (two or three stimulation sites × two stimulation current amplitudes) with true positive rate >80% (two subjects) and >60% (one subject), respectively. Significance. The proposed electrode provides a compact multichannel interface for recording electromyogram and delivering electrical stimulation in applications such as neuroprostheses for tremor suppression and closed-loop myoelectric prostheses., This study was financed by the European Projects EXTEND (H2020-ICT-23-2017-779982) and NeuroTREMOR (FP7-ICT-2011-7-287739). The funding agency was not involved in the collection, analyses, and interpretation of data or writing and publication of this article.

[Objective]: Controlled leg-cycling modulates H-reflex activity after spinal cord injury (SCI). Preserved cutaneomuscular reflex activity is also essential for recovery of residual motor function after SCI. Here the effect of a single leg-cycling session was assessed on cutaneomuscular-conditioned H-reflex excitability in relation to residual lower limb muscle function after incomplete SCI (iSCI)., [Methods]: Modulation of Soleus H-reflex activity was evaluated following ipsilateral plantar electrical stimulation applied at 25–100 ms inter-stimulus intervals (ISI’s), before and after leg-cycling in ten healthy individuals and nine subjects with iSCI., [Results]: Leg-cycling in healthy subjects increased cutaneomuscular-conditioned H-reflex excitability between 25 and 75 ms ISI (p < 0.001), compared to a small loss of excitability at 75 ms ISI after iSCI (p < 0.05). In addition, change in cutaneomuscular-conditioned H-reflex excitability at 50 ms and 75 ms ISI in subjects with iSCI after leg-cycling predicted lower ankle joint hypertonia and higher Triceps Surae muscle strength, respectively., [Conclusion]: Leg-cycling modulates cutaneomuscular-conditioned spinal neuronal excitability in healthy subjects and individuals with iSCI, and is related to residual lower limb muscle function., [Significance]: Cutaneomuscular-conditioned H reflex modulation could be used as a surrogate biomarker of both central neuroplasticity and lower limb muscle function, and could benchmark lower-limb rehabilitation programs in subjects with iSCI., This project was funded by “Consolider-Ingenio 2010 Hyper” from the “Ministerio de Ciencia e Innovación – Spain” (CSD2009-00067).

Despite aggressive conventional therapy, lasting hemiplegia persists in a large percentage of stroke survivors. The aim of this article is to critically review the rationale behind targeting multiple sites along the motor learning network by combining robotic therapy with pharmacotherapy and virtual reality–based reward learning to alleviate upper extremity impairment in stroke survivors. Methods for personalizing pharmacologic facilitation to each individual’s unique biology are also reviewed. At the molecular level, treatment with levodopa was shown to induce long-term potentiation-like and practice-dependent plasticity. Clinically, trials combining conventional therapy with levodopa in stroke survivors yielded statistically significant but clinically unconvincing outcomes because of limited personalization, standardization, and reproducibility. Robotic therapy can induce neuroplasticity by delivering intensive, reproducible, and functionally meaningful interventions that are objective enough for the rigors of research. Robotic therapy also provides an apt platform for virtual reality, which boosts learning by engaging reward circuits. The future of stroke rehabilitation should target distinct molecular, synaptic, and cortical sites through personalized multimodal treatments to maximize motor recovery.

BACKGROUND: After cervical Spinal Cord Injury (SCI), upper limb movements made by patients have a lack of smoothness and a hand velocity profile characterized by a high number of velocity peaks. OBJECTIVE: The aim of the present paper is to propose three novel kinematic indices for quantifying movement agility and smoothness, and to analyze their discriminative capability between healthy and pathological people. METHODS: 18 people, healthy and two groups of patients with cervical SCI, participated in the study. Kinematic indices in relation to movement agility and smoothness were computed from hand trajectories and velocity profiles during the performance of the ADL of drinking from a glass. RESULTS: The proposed indices discriminated between healthy and SCI people. The results are greater in healthy than SCI people. Both smoothness indices detected significant differences between healthy and both SCI groups. Moreover, the Agility index showed capacity for discriminating between both patients groups. CONCLUSIONS: The main contribution of this research consists on the proposal of kinematic indices from experimental data, whose results are dimensionless and relative to a pattern of healthy subjects. We hope that kinematic indices proposed are a step toward the standardization of the quantitative assessment of movement characteristics and functional impairments.

Patients with patellofemoral pain (PFP) present altered activation of the hip and knee muscles. Electromyography (EMG) analyses in PFP patients are usually performed individually for each muscle. However, several studies suggest that the central nervous system may modulate neural commands directed to groups of co-activated muscles, called muscle synergies. Investigating the synergistic organization in PFP will advance our knowledge of the influence of pain on muscle coordination. This study aimed to compare the motor coordination between women with and without PFP during the lateral step down (LSD). 15 women with PFP and 14 asymptomatic women underwent three-dimensional kinematics and EMG assessment during LSD. The Movement Deviation Profile (MDP) was calculated from kinematic data. Muscle synergies were extracted from EMG data of eight lower limb muscles using a non-negative factorization algorithm. Results revealed differences in MDP and an altered synergistic control between women with PFP and asymptomatic while performing the same motor gesture. Particularly, the variability accounted for (VAF) when using 3 synergies to reconstruct EMGs of the PFP group was higher than in the control group, suggesting reduced complexity of motor control in PFP. Detailed synergy analyses highlighted specific differences between groups in vastii and rectus femoris, which are muscles with a crucial role during the squat phase and the transition to rise phase of the LSD. This study shows the ability of muscle synergies analysis to reveal impaired motor coordination in PFP patients, and has the potential to be explored as a complementary tool to current clinical assessment techniques., The study was financed in part by the Coordenação de Aper-feiçoamento de Pessoal de Nível Superior – Brasil (CAPES) –Finance Code 001.

This work was funded by the European Union’s Horizon 2020 research and innovation programme (Project EXTEND—Bidirectional Hyper-Connected Neural System) under grant agreement No 779982 and by the EFOP-3.6.1-16-2016-00004 grant.

Trabajo presentado en 2019 Wearable Robotics Association Conference (WearRAcon) in Scottsdale, AZ, EE.UU., el 26 Marzo de 2019., This study is part of our research on the use of a submaximal force generation task for early rehabilitation of ankle joint movements after stroke. We present the evaluation of metrics related to force generation and position control, and their relationship with submaximal force generation control learning, as well as their attainability for robot-mediated treatment after a cerebrovascular accident. In experiments with a group of healthy individuals we have found a decrease in the error and increase in the score of our proposed biofeedback game. We concluded that the proposed protocol and the rehabilitation robotic tool are able to promote motor learning in healthy individuals.

Walking rehabilitation processes include many repetitions of the same physical movements in order to replicate, as close as possible, the normal gait trajectories, and kinematics of all leg joints. In these conventional therapies, the therapist's ability to discover patient's limitations-and gradually reduce them-is key to the success of the therapy. Lower-limb robotic exoskeletons have strong deficiencies in this respect as compared to an experienced therapist. Most of the currently available robotic solutions are not able to properly adapt their trajectories to the biomechanical limitations of the patient. With this in mind, much research and development is still required in order to improve artificial human-like walking patterns sufficiently for valuable clinical use. The work herein reported develops and presents a method to acquire and saliently analyze subject-specific gait data while the subject dons a passive lower-limb exoskeleton. Furthermore, the method can generate adjustable, yet subject-specific, kinematic gait trajectories useful in programming controllers for future robotic rehabilitation protocols. A human-user study with ten healthy subjects provides the experimental setup to validate the proposed method. The experimental protocol consists in capturing kinematic data while subjects walk, with the donned H2 lower-limb exoskeleton, across three experimental conditions: walking with three different pre-determined step lengths marked on a lane. The captured ankle trajectories in the sagittal plane were found by normalizing all trials of each test from one heel strike to the next heel strike independent of the specific gait features of each individual. Prior literature suggests analyzing gait in phases. A preliminary data analysis, however, suggests that there exist six key events of the gait cycle, events that can adequately characterize gait for the purposes required of robotic rehabilitation including gait analysis and reference trajectory generation. Defining the ankle as an end effector and the hip as the origin of the coordinate frame and basing the linear regression calculations only on the six key events, i.e., Heel Strike, Toe Off, Pre-Swing, Initial Swing, Mid-Swing, and Terminal Swing, it is possible to generate a new calculated ankle trajectory with an arbitrary step length. The Leave-One-Out Cross Validation algorithm was used to estimate the fitting error of the calculated trajectory vs. the characteristic captured trajectory per subject, showing a fidelity average value of 95.2, 96.1, and 97.2%, respectively, for each step-length trial including all subjects. This research presents method to capture ankle trajectories from subjects and generate human-like ankle trajectories that could be scaled and computed on-line, could be adjusted to different gait scenarios, and could be used not only to generate reference trajectories for gait controllers, but also as an accurate and salient benchmark to test the human likeness of gait trajectories employed by existing robotic exoskeletal devices., The research was partially funded by CONACyT Doctoral program, Tecnolgico de Monterrey/Robotics Lab and research chair program for distinguished professors in Biomechatronics.