Haptic Adaptive Feedback to Promote Motor Learning With a Robotic Ankle Exoskeleton Integrated With a Video Game

ORIGINAL RESEARCH published: 21 February 2020 doi: 10.3389/fbioe.2020.00113 Frontiers in Bioengineering and Biotechnology | www.frontiersin.org 1February 2020 | Volume 8 | Article 113 Edited by: Leonardo Gizzi, University of Stuttgart, Germany Reviewed by: Strahinja Dosen, University Medical Center Göttingen, Germany Michele Barsotti, Sant’Anna School of Advanced Studies, Italy *Correspondence: Guillermo Asín-Prieto [email protected] †These authors have contributed equally to this work and share first authorship ‡These authors have contributed equally to this work and share last authorship Specialty section: This article was submitted to Bionics and Biomimetics, a section of the journal Frontiers in Bioengineering and Biotechnology Received: 29 August 2019 Accepted: 04 February 2020 Published: 21 February 2020 Citation: Asín-Prieto G, Martínez-Expósito A, Barroso FO, Urendes EJ, Gonzalez-Vargas J, Alnajjar FS, González-Alted C, Shimoda S, Pons JL and Moreno JC (2020) Haptic Adaptive Feedback to Promote Motor Learning With a Robotic Ankle Exoskeleton Integrated With a Video Game. Front. Bioeng. Biotechnol. 8:113. doi: 10.3389/fbioe.2020.00113 Haptic Adaptive Feedback to Promote Motor Learning With a Robotic Ankle Exoskeleton Integrated With a Video Game Guillermo Asín-Prieto1*†, Aitor Martínez-Expósito1†, Filipe O. Barroso1, Eloy J. Urendes2, Jose Gonzalez-Vargas3, Fady S. Alnajjar4, Carlos González-Alted5, Shingo Shimoda6, Jose L. Pons1,7,8,9‡ and Juan C. Moreno1‡ 1Neural Rehabilitation Group, Cajal Institute, CSIC–Spanish National Research Council, Madrid, Spain, 2Department of Information Systems Engineering, University San Pablo CEU, Boadilla del Monte, Spain, 3Department of Translations Research and Knowledge Management, OttoBock Healthcare GmbH, Duderstadt, Germany, 4College of Information Technology, The United Arab Emirates University, Al-Ain, United Arab Emirates, 5Centro de Referencia Estatal de Atención al Daño Cerebral, Madrid, Spain, 6Intelligent Behaviour Control Unit, RIKEN, Nagoya, Japan, 7Legs & Walking AbilityLab, Shirley Ryan AbilityLab, Chicago, IL, United States, 8Department of Biomedical Engineering and Mechanical Engineering, McCormick School of Engineering, Northwestern University, Chicago, IL, United States, 9Department of PM&R, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States 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. Keywords: bioinspired, exoskeleton, video game, motor learning, corticospinal, plasticity, stroke, TMS Asín-Prieto et al. Haptic Adaptive Feedback Motor Learning 1. INTRODUCTION Stroke affects each year around 13.7 million people worldwide, is the second leading cause of disability and may result in a series of motor impairments including gait abnormalities (Barroso et al., 2017; World Stroke Organization, 2018). Regarding walking rehabilitation after stroke, there has been considerable controversy and debate on the effectiveness of the various approaches used (Pollock et al., 2014). In the past 20 years, other rehabilitation modalities, such as robotic therapy have been introduced to motor rehabilitation practice aiming at promoting gait recovery in patients who suffered neural-impairments (Moreno et al., 2013), including post-stroke patients. So far, results suggest that robotic therapy may be beneficial to treat acute and chronic post-stroke patients (Van der Loos et al., 2016). Nonetheless, there is no agreement on the optimal robot-assisted approaches to promote neurorecovery through plasticity mechanisms following neural injury (Kim and You, 2017; Belas dos Santos et al., 2018; Gassert, 2018; Barroso et al., 2019). One of the most widely tested approaches is robotic guidance, which supervises trajectories during motor tasks and prevents the user from performing undesired (and possibly unsafe) deviations from prescribed trajectories. This type of robotic assistance is frequently implemented as a “tunnel” of allowed deviation around the prescribed trajectory (Ren et al., 2011; Bortole et al., 2015). Robotic guidance can be combined with virtual environments or video games. Adding video games to the therapy turns the potential motor learning into a transparent process to the user. Moreover, engagement with the training and entertainment are very important psychological aspects of games (Patton and Mussa-Ivaldi, 2004). In fact, visual feedback has been shown to improve robotic guidance therapy scenarios (Liu et al., 2006; Tamburella et al., 2019) and video games seem to be effective to improve motor function and health after stroke (Swanson and Whittinghill, 2015). Thus, different combinations of robotic guidance and video games have been proposed. A possible shortcoming of robotic guidance is that this approach might as well reduce patients’ effort, and thus, the possible benefits of the therapy (Rowe et al., 2017). In this vein, Goodman et al. (2014) designed a video game that decreased the level of assistance delivered to the ankle joint by the robot if the performance (assessed as a function of the smoothness of trajectories) increased. Other strategies involve adding resistance to make the task more challenging when the performance of the user improves, which can potentially increase engagement in the task (Ren et al., 2011). Interestingly, these two opposite strategies found evidence of enhanced motor learning markers, although there is still no consensus regarding the effects of using either type of robotic guidance. As a counterpart of robotic guidance, error-augmentation based approaches have been also proposed to enhance motor learning. Emken and Reinkensmeyer (2005) used movementperturbation approach with a robotic device while the user was performing the target task and concluded that motor learning can be accelerated by exploiting the error-based learning mechanism. Reinkensmeyer and Patton (2009) suggested that starting with guidance force and gradually removing it and increasing error-augmentation approaches may lead to motor learning. Marchal-Crespo et al. (2014) showed that adding random disturbances while executing a simple dorsi-plantarflexion task improved motor learning and suggested that the variability introduced to the task may increase recovery due to increased effort and attention needed to perform the task. Moreover, another study showed that “challenge-based” controllers (where guidance force is given on the first stages of the recovery and error-augmentation is given later on the rehabilitation) were more beneficial for the recovery, since this represents an adaptation of the therapy to the patients’ motor learning process (Marchal-Crespo et al., 2017). These functional benefits observed in these studies suggest that the nervous system learns by forming the internal model of the dynamics of the environment via error reduction (Emken and Reinkensmeyer, 2005), leading to plastic changes presumably at the cortical level (Perez et al., 2004). Given the aforementioned literature on different approaches tested in robotic therapy, there is evidence supporting the integration of video games in challenge-based therapies, that are able to adapt the difficulty of the task to the patient’s skills, always trying to keep the user motivated and engaged. This might help promoting motor learning via activity-dependent neuroplasticity (Sweatt, 2016; Gassert, 2018). In this context, the present study proposes a novel therapy protocol that combines a grounded exoskeleton perturbing the ankle joint motion with a video game based visual feedback. Ankle joint is fundamental for gait and balance as plantarflexor passive stiffness causes reduced plantarflexion torque before starting the swing phase in gait, and may as well limit dorsiflexion, compromising foot clearance in post-stroke patients (Lamontagne et al., 2002). The major novelty that this therapy protocol introduces is the autonomous modulation of the perturbations provided to the user via haptic adaptive feedback approach based on the task performance. This protocol was first tested on a validation study with healthy subjects, and later on as an usability case study with a post-stroke patient. We hypothesized that the use of the proposed ankle rehabilitation robot would promote motor learning and increase corticospinal excitability of the dorsi-plantarflexor muscles. Although there is not a clear relationship between motor learning and corticospinal excitability (Bestmann and Krakauer, 2015), several authors have established a relation between them (Perez et al., 2004; Kida et al., 2016; Naros et al., 2016; Mawase et al., 2017; Christiansen et al., 2018; Raffin and Siebner, 2018; Mrachacz-Kersting et al., 2019). Corticospinal excitability can be assessed with Transcranial Magnetic Stimulation (TMS), by eliciting Motor Evoked Potentials (MEPs) (Rotenberg et al., 2014). Validation of our hypotheses would provide preliminary evidence of the usefulness of this novel robotic therapy to promote motor learning in the context of a pregait mobilization task, i.e., mobilization before undergoing gait-centered rehabilitation. 2. MATERIALS AND METHODS 2.1. Participants Ten healthy subjects (29.80 ±6.32 years old) participated in the study. They signed an informed consent for the experiment. Frontiers in Bioengineering and Biotechnology | www.frontiersin.org 2February 2020 | Volume 8 | Article 113 Asín-Prieto et al. Haptic Adaptive Feedback Motor Learning Experiments were conducted in accordance with the declaration of Helsinki. All experimental procedures were approved by the Bioethical subcommittee of the Ethical committee of CSIC (Spanish National Research Council), reference 008/2016. We also performed an usability case study with one poststroke patient (age 37). The patient suffered an haemorrhagic transformation of ischemic stroke, affecting the right middle cerebral artery, thus the most affected side of the body was the left. The experiment with the patient was performed in the facilities, and under the supervision of the professionals of Centro de Referencia Estatal de Atención Al Daño Cerebral (CEADAC). The patient was assessed by a physician using the most common scales: the Disability Rating Scale (DRS), Functional Independence Measure (FIM) and the Barthel Index (BI). For the DRS, the value was 2, corresponding with a partial level of disability. The BI score was 100, reflecting independence in the activities of daily living, while for the FIM it was 119 (85 for motor subscale and 34 for cognitive scale). The patient signed the Informed Consent, acknowledging the risks and the inclusion criteria (he was previously examined by a physician, who validated the suitability for the training). These experimental procedures were approved by the local scientific committee in CEADAC. 2.2. Experimental Platform The Biomot ankle robot (Moltedo et al., 2016) was used in this study. Footedness preference for each subject was established according to the Waterloo footedness test (Elias et al., 1998). This actuator is based on the MACCEPA (mechanically adjustable compliance and controllable equilibrium position actuator) concept (Bacek et al., 2015), which is driven by a joint torque control. MACCEPA concept is based on a torque-controlled rotational actuator with adjustable compliance (Figure 1). The motor is rigidly connected to the Lever Arm (LA), which is in turn connected to the Fixed Link (FL) via a spring (K). FL is attached to the wearer’s foot and thus its angle represents the user’s ankle angular position, and LA represents the robot position. Both LA and FL move with respect to the Output Link (OL), which is attached to the wearer’s shank. Consequently, if the motor reference is set to a particular position, the wearer still has the possibility to pivot the ankle by compressing the spring. This permits to calculate the interaction torque between the wearer and the actuator by measuring the subsequent deflection of the spring (αangle =LA −FL). MACCEPA actuator allows to provide controlled torque profiles by using a simple position controller without the need of a complex torque sensor, and with the reliability of position sensors. 2.3. Robot Control The controller of this robotic platform comprises a zero torque controller (based on a classic Proportional/Integral/Derivative (PID) implementation) and the haptic adaptive feedback (HAF) component based on tacit adaptability—a symbiotic control strategy on exoskeletons inspired by biomimetic mechanisms, which, in turn, is based on the “tacit learning” approach for bipeds (Shimoda et al., 2015; Asín-Prieto, 2016), adapted by the performance of the user in the experimental task. The HAF module is schematically introduced in the control architecture, depicted in Figure 2. The controller is described by Equation (1). u=τPID +uHAF (1) where uis the output of the controller (pulse width modulation), τPID corresponds to the output of the torque controller (Equation 2), and uHAF to the output of the haptic adaptive feedback module (Equation 3). τPID =Kp·error +Ki·Zt 0 error ·dt +Kd·d dt error (2) uHAF =KHAFi ·Zt 0 α·dt +KHAFp ·α(3) where Kp,Ki, and Kdare respectively the PID constants; KHAFi and KHAFp are respectively the integral and proportional constants of the HAF module; αangle is proportional to the interaction torque between human and robot; and error = LAref −LA.LA is the actual sensor information for the Lever Arm angle, whereas LAref is the calculated reference LA angle. This reference LA angle is calculated with the approximation of the MACCEPA actuator to a torsion spring actuator described by Equation (4). LAref =τref Kts +FL (4) where τref is the reference disturbance torque to the controller, Kts is the empirically obtained torsional stiffness constant, and FL is the Fixed Link angle, i.e., the user’s ankle angle. The objective of the controller is to apply higher disturbance torques when higher performance is reached (consequently adding more difficulty to the task), and vice versa (rendering the task easier with lower performances). To do this, KHAFi and KHAFp are empirically set to KHAF 1000 and KHAF 5, where KHAF provides the modulation of the disturbance torque following this simple rule: KHAF =100 −performance [%], thus, the value of the constant KHAF is updated based on task performance. Section 2.5.1 explains how this constant is modulated. 2.4. Protocol The longitudinal intervention protocol applied on each participant is graphically described in Figure 3. The intervention lasted 4 days. Training sessions were performed in days 1–3. Four corticospinal assessments were performed in days 1, 3 (two assessments), and 4. The training follows this daily structure: forty training repetitions (randomized trajectory profiles, as shown in Figure 4), disturbance torque modulated by the system; followed by ten evaluation repetitions [two types of disturbance torque profiles—Figure 5A, multiplied by the five possible trajectories— Figure 4] for the assessment of immediate effect. The disturbance torque provided in these assessment repetitions was set at the Frontiers in Bioengineering and Biotechnology | www.frontiersin.org 3February 2020 | Volume 8 | Article 113 Asín-Prieto et al. Haptic Adaptive Feedback Motor Learning FIGURE 1 | MACCEPA actuator model schematics and actual actuator. (A) MACCEPA actuator schematics, with all its components. (B) MACCEPA attached to a cartoon foot, with the different components depicted. FIGURE 2 | The controller of the robot comprises a zero torque Proportional/Integral/Derivative (PID) controller and the haptic adaptive feedback module (HAF constant—KHAF —multiplied by alpha, angle proportional to the interaction between the robot and the subject), tweaking KHAF with the performance. The subject controls the location of the character on the screen by means of the ankle joint angle. In the figure, ustands for the output of the controller, τref is the disturbance torque reference, τPID and uHAF are respectively the outputs from the PID torque and HAF controllers; and FL,LA, and LAref are respectively the angles for Fixed Link, Lever Arm, and reference for Lever Arm computed from the reference disturbance torque. maximum given by the robot: 15 N·m. All repetitions had a duration of 10 s per trajectory. The resting position of the ankle was set at −2.5◦(slightly plantarflexed) as the most comfortable position for the users. The task instruction was to follow the trajectories delineated in the visual paradigm by means of the sequence of onscreen items (gas bottles) following the shortest linear path inbetween. The user had to move a character (gyrocopter) with the angular position of the ankle via dorsi-plantarflexion to collect the gas bottles: dorsiflexion implied moving the avatar upwards in the screen, whereas plantarflexion implied going downwards. Meanwhile, the robot disturbed the user motion by performing plantar and dorsiflexion alternated disturbance torque profiles (see Figure 5A). These disturbance torque profiles were developed with the aim of stimulating both agonist and antagonist muscle groups, both in dorsiand plantarflexion movements. For the patient, we focused only on dorsiflexion disturbance torque patterns, because he was unable to avoid the full drop of the foot. Besides, the disturbance torque was modified (as seen in Figure 5B) to remove abrupt changes in the direction of the force exerted by the robot. We empirically set a maximum disturbance torque of 5 N·m. All healthy subjects were asked to train and find a strategy to actively compensate the disturbance torque by the ankle robot, to successfully follow the trajectory on the screen, along three sessions (one every day), of 50 repetitions. For the patient, the length of the protocol was modified to 5 days (replicating the protocol used in Asín-Prieto et al., 2018). 2.5. Metrics 2.5.1. Robot-Based We used two different metrics to quantify the performance of the user: SCORE and root mean squared error (RMSE). SCORE was calculated for each trial as the percentage of collected onscreen items, whereas RMSE was calculated by subtracting the performed trajectory from an ideal linear path between onscreen items. Note that it could be possible to collect all the Frontiers in Bioengineering and Biotechnology | www.frontiersin.org 4February 2020 | Volume 8 | Article 113 Asín-Prieto et al. Haptic Adaptive Feedback Motor Learning FIGURE 3 | Experiment schematics. (A) Upper left figure shows the Transcranial Magnetic Stimulation (TMS) assessment setup. (B) Upper right figure shows the experimental setup together with the daily training structure: 40 training repetitions, and ten last repetitions to survey the execution after the training (at a settled disturbance torque, the maximum given by the robot: 15 N·m). And (C) Lower figure shows the longitudinal intervention structure: (1) TMS assessment (represented by the figure-of-8 coil) PRE-intervention; (2) first day training (represented by the visual paradigm); (3) second day training; (4) third day training; (5) TMS assessment POST-intervention; (6) POST30: TMS assessment 30 min after intervention; and (7) POST24h: TMS assessment 24 h after intervention. Adapted from Asín-Prieto et al. (2018), copyright 2019, Springer Nature Switzerland AG. onscreen items by performing a high error trajectory between them (see Figure 6 for an example). The total SCORE for each trial was shown to encourage the user to improve it along the session. The value for KHAF was updated when the gyrocopter exceeded a (collected or uncollected) gas bottle, based on the instantaneous SCORE in the current trial, thus modulating the disturbance torque. Each trial consisted of 20 collectible bottles, thus rendering a KHAF refresh rate of 2 Hz (20 gas bottles per 10 s). Figure 7 depicts an example of the modulation of the disturbance torque based on the SCORE metric. SCORE and RMSE were used to quantify two different sets of data: (a) assessment post-training repetitions, i.e., the 10 last repetitions of each training day (see ROBOTIC TRAINING in Figure 3), in what we called POST-train values; (b) linear fit on the sequence of the 120 training repetitions (40 training repetitions per day, concatenated for the 3 days), and selected the values of the resulting linear fitting coinciding with the first (1) and last (120) repetitions, in what we called MOD (modulated) values, where PRE-MOD and POST-MOD were the first and last values of the linear fit, respectively (see Figure 8). In addition to RMSE and SCORE after each day training (POST-train), we used two other metrics for the patient: changes in range of motion (ROM) and velocity. Before and after the training from the second to the fifth day, the patient underwent a robotic evaluation of the possible ROM. This evaluation consisted on moving up and down a ball on the screen via dorsi-plantarflexion during 30 s. The patient was asked to alternatively reach two horizontal lines (one up and one down), and the position of these lines was changed to the maximum reached in order to make the task more difficult. Although the separation between lines meant a wider ROM, the absolute position of them remained the same onscreen in order to be unnoticeable for the patient. The maximum velocity was calculated by multiplying the maximum achieved angular amplitude by the fundamental frequency (calculated with Fast Fourier Transform). We computed the change in this metric by comparing the results before and after the intervention. 2.5.2. Clinical Assessment In the rehabilitation process there are three main phases that need to be characterized: (1) initial assessment, to identify and measure the extent of the pathology; (2) planning, to assess the problem and establish the objectives; and (3) final assessment, after the treatment. In addition to the aforementioned metrics, the Frontiers in Bioengineering and Biotechnology | www.frontiersin.org 5February 2020 | Volume 8 | Article 113 Asín-Prieto et al. Haptic Adaptive Feedback Motor Learning FIGURE 4 | Five possible trajectory profiles: (A) constant −2.5◦;(B) straight increasing from −4 to 1◦;(C) straight decreasing from −1 to −6◦;(D) from −6 to −2.5 and back to −6 again; and (E) from 1 to −2.5 and back to 1. Modified from Asín-Prieto et al. (2019), copyright 2019, IEEE. Frontiers in Bioengineering and Biotechnology | www.frontiersin.org 6February 2020 | Volume 8 | Article 113 Asín-Prieto et al. Haptic Adaptive Feedback Motor Learning clinicians at CEADAC performed a functional clinical assessment at the beginning and the end of the week for the patient, before the first training session, and after the last one. In the functional assessment protocol developed in CEADAC, among the broad set of clinical functional scales that aim to provide an objective insight in the recovery process of patients, the clinicians focus FIGURE 5 | The behavior of the HAF module is depicted: (1) KHAF =100 prompts zero-torque control; (2) KHAF =0 normal torque control, no influence of HAF, so up to 15 N·m reference; and (3) KHAF between 0 and 100, nearer to a zero-torque control the higher the constant KHAF is, thus allowing to modulate the magnitude of the applied disturbance torque amplitude. Dashed line corresponds to an example of disturbance torque profile with KHAF between 0 and 100. (A) Possible disturbance torque profiles: torque to dorsiflexion (up) and torque to plantarflexion (down). (B) Possible disturbance torque to dorsiflexion (up) direction for the patient. on: Timed 10 m walk, as a measure of gait speed; 6 Min Walking Test (6MWT), as a measure of resistance; Step Test, as a measure of dynamic balance; Timed Up and Go (TUG) test, that demands several potentially destabilizing maneuvers for the subject. 2.5.3. Neurophysiological Assessment Corticospinal excitability was assessed by recording the MEPs elicited by a Magstim 2002TMS stimulator in single pulse modality in combination with a figure-of-eight double-coned coil. We followed the instructions by SENIAM (Hermens et al., 1999) to place the surface electromyography Ag/AgCl electrodes (22.225 ×34.925 mm, Vermed), recorded with a g.USBamp amplifier (g.tec), sampled at 24 KHz and highpass filtered with a 20 Hz first order Butterworth filter. In order to map the hot spot [place where Tibialis Anterior (TAnt) MEPs peak-to-peak amplitude is higher] on the scalp, several supra-threshold pulses were delivered nearby the vertex. The hot spot, ineon, and vertex were drawn with a permanent marker on a swimming cap, in order to ensure repeatability between sessions. After locating the hot spot, the resting motor threshold (RMT), defined as the stimulation intensity that elicits MEPs of ∼50 µV peak-to-peak amplitude in 5 out of 10 applied pulses (Temesi et al., 2014), was set for each participant. We recorded ipsilaterally (Kamibayashi et al., 2009) TAnt and Soleus (SO), as well as Rectus Femoris (RF, as a control muscle not involved in the robotic ankle task). The assessment consisted in delivering 20 pulses to each of the volunteers at an intensity of 120 % of the RMT to elicit MEPs. The peak-to-peak amplitude of the MEPs was averaged. This assessment procedure was performed four times (see Figure 3): (1) before the training of the first day (PRE); immediately after the training of the third day (last training, POST); 30 min after to evaluate plastic effects (POST30); and finally 24 h after, in order to check lasting effects (POST24h). 2.5.4. Satisfaction Questionnaire After the treatment, all subjects filled out a Likert scale (1–Very unsatisfied; 2–Unsatisfied; 3–Not satisfied nor unsatisfied; 4–Satisfied; 5–Very satisfied) questionnaire for assessing the satisfaction level with the experimental procedure. 2.6. Data Analysis Data were analyzed with Matlab, IBM SPSS Statistics version 25, and R Studio. After examining with Shapiro-Wilk test, our FIGURE 6 | Trajectory example, with the best trajectory between items in continuous red line, and a high error trajectory between items (with 100% SCORE as all the items are collected) in dotted blue line. Frontiers in Bioengineering and Biotechnology | www.frontiersin.org 7February 2020 | Volume 8 | Article 113 Asín-Prieto et al. Haptic Adaptive Feedback Motor Learning FIGURE 7 | Example of the modulation of KHAF . Dashed line in the upper panel depicts the actual trajectory followed by a subject, with the uncollected bottles remaining onscreen. The instantaneous SCORE is presented in the table, both in collected/total (#) and percentage (%), as well as the computed value for KHAF . Lower panel shows in blue the reference torque (corresponding to a SCORE of 100%), and in red the actual reference disturbance torque applied to the user’s ankle modulated according to the SCORE. data showed variables with normal distributions and variables violating the normality. Thus, for those without a normal distribution, we provide the results for non-parametric tests; and for those that present a normal distribution, we provide parametric analyses. First of all, changes in RMSE POST-train metric, both for healthy subjects and the patient, were tested using a Friedman test of differences among repeated measures along the study, finally evaluating the size effect with Average Spearman rho (ρs), and performing a Pairwise post-hoc Test for Multiple Comparisons of Rank Sums for Unreplicated Blocked Data (Conover-test) with Bonferroni correction. For the SCORE POST-train, both for healthy subjects and the patient, we performed a One-way repeated measures ANOVA, with Huynh-Feldt correction due to lack of sphericity (Mauchly’s test), with partial squared omega (ω2 p) for the size effect, and pairwise t-test post-hoc analysis, with Bonferroni correction. Then, we tested the correlation between SCORE and RMSE with a Spearman bivariate analysis (p value of 0.05), for the evaluation ratings after each day training (POST-train), to check the relationship between metrics. To assess changes in the PRE-MOD vs. POST-MOD of the SCORE and RMSE on the modulated repetitions, we conducted t-Student analyses, providing Cohen’s das the size effect. Finally, changes in the corticospinal excitability were also tested using a Friedman test, and FIGURE 8 | MOD metric calculation example, for RMSE. evaluating the size effect with Average Spearman rho, and performing Conover post-hoc Test with Bonferroni correction. Frontiers in Bioengineering and Biotechnology | www.frontiersin.org 8February 2020 | Volume 8 | Article 113 Asín-Prieto et al. Haptic Adaptive Feedback Motor Learning 3. RESULTS Data showed normal distribution for: SCORE POSTtrain both for healthy subjects and the patient, and MOD for SCORE and RMSE. All the other variables TABLE 1 | Descriptive statistics for the variables analyzed for the group of healthy individuals. Mean Median Standard deviation Min Max RMSE POST-train 1st day 3.22 2.82 2.31 1.52 9.59 2nd day 2.43 2.39 1.05 1.23 4.95 3rd day 2.24 2.14 0.99 1.11 4.57 SCORE POST-train 1st day 54.65 54.25 19.53 15.50 84.50 2nd day 65.15 62.25 9.92 55.00 82.50 3rd day 68.00 64.75 11.97 54.00 87.00 RMSE modulated PRE-MOD 4.02 3.65 1.77 1.62 6.86 POST-MOD 1.99 1.79 0.95 0.98 4.05 SCORE modulated PRE-MOD 43.54 40.15 18.34 24.07 80.11 POST-MOD 67.84 66.77 9.77 54.92 85.05 TAnt MEPs PRE 238.99 176.33 144.07 107.16 505.69 POST 312.94 344.67 154.82 108.96 504.29 POST30 281.59 296.33 111.02 107.32 411.23 POST24h 403.56 385.64 222.82 114.94 807.08 SO MEPs PRE 94.36 90.51 43.42 28.33 162.03 POST 109.00 119.30 50.21 30.48 169.56 POST30 99.35 100.20 44.19 29.41 176.05 POST24h 107.46 115.84 50.21 31.87 198.76 RF MEPs PRE 228.17 209.44 163.19 34.49 495.57 POST 215.42 243.36 129.98 34.38 387.78 POST30 190.06 146.14 171.15 22.50 555.03 POST24h 236.82 210.45 164.92 31.45 487.19 Satisfaction 4.80 5.00 0.42 4.00 5.00 presented a non-normal distribution (see Table 1 for the descriptive statistics). 3.1. Study With Healthy Individuals There was a significant change in SCORE POST-train metric [ANOVA, F(1.18, 10.60) =6.84; p<0.05; ω2 p=0.35; large effect size according to Field, 2018] but not in RMSE POST-train (p>0.05). Post-hoc tests revealed that the SCORE at the third training day was significantly increased (p=0.03) as compared to the SCORE on the first training day (see Figure 9). We found significant (p<0.05) strong correlations (ρ > 0.70) in the evaluation ratings after each day training (POST-train); both for SCORE 1st day and RMSE 1st day (ρ= −0.89), and SCORE 3rd day and RMSE 3rd day (ρ= − 0.86). t-Student indicated that the SCORE POST-MOD of the modulated training was significantly higher than the SCORE PRE-MOD [t(9) = −4.39; p<0.05; Cohen’s d= 1.39], and that the RMSE POST-MOD was significantly lower than the RMSE PRE-MOD [t(9) = 3.05; p<0.05; Cohen’s d= 0.96]. There was a large size effect for both metrics’ t-tests according to Kotrlik and Williams (2003). TAnt MEPs peak-to-peak amplitude was significantly changed [Friedman, χ2=9.12; p<0.05; 3 DoF; ρs=0.22; small effect size according to Kotrlik and Williams (2003)] across assessment sessions. Post-hoc tests revealed that TAnt MEPs peak-to-peak amplitude was significantly increased at the POST24h moment when compared to PRE (p<0.01), POST (p=0.03), and POST30 (p<0.01) moments (see Figure 10). On the other hand, there was no significant change in SO nor in RF MEPs peak-to-peak amplitude across assessment moments (p>0.05). The satisfaction questionnaire rendered an average of 4.8 (being 5 Very satisfied), with a standard deviation of 0.42. 3.2. Usability Case Study With Post-stroke Patient In the case study, we used the repetitions for each of the 5 training days to conduct the statistical analyses, as we had data from one FIGURE 9 | Results for the RMSE and SCORE after each day training (POST-train) for the group of healthy individuals. Statistical significance (post-hoc comparison) depicted by the asterisk (*). (A) Mean and standard error of POST-train for RMSE per day. (B) Mean and standard error of POST-train for SCORE per day. Frontiers in Bioengineering and Biotechnology | www.frontiersin.org 9February 2020 | Volume 8 | Article 113