EMG patterns during assisted walking in the exoskeleton

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Non-registered authors will have the default profile image displayed by their name on the article page. “Letian Wang” “Freygardur Thorsteinsson” “Jeremi Gancet” “Ralf Hauffe” “Frank Zanov” “Edwin van Asseldonk.” Q3 If you decide to use previously published, copyrighted figures in your article, please keep in mind that it is your responsibility as author to obtain the appropriate permissions and licences and to follow any citation instructions requested by third-party rights holders. If obtaining the reproduction rights involves the payment of a fee, these charges are to be paid by the authors. Q4 Ensure that all the figures, tables and captions are correct. Q5 Verify that all the equations and special characters are displayed correctly. Q6 Please provide the name of the department for the following affiliations. “Centre of Space Bio-medicine, University of Rome Tor Vergata, Rome, Italy” “Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium.” Q7 Please provide significance for “∗” in “Figure 3.” Q8 Please provide part label caption for “Figure 5E.” Q9 Please provide the page range and doi for “Berens, 2009.” Author’s Response Query No. Details required Q10 Please provide doi for the following references. Ivanenko et al., 2000; Ivanenko et al., 2002; Sale et al., 2012; Yakovenko et al., 2002.” Q11 Please provide the volume number, page range and doi for the following references. “Pisotta et al., 2014; Wang et al., 2014.” Q12 Please provide the city name for “Wang et al., 2013.” Author’s Response ORIGINAL RESEARCH ARTICLE published: xx June 2014 doi: 10.3389/fnhum.2014.00423 HUMAN NEUROSCIENCE 001 002 Q1 Q2 003 004 005 006 007 008 009 Q6 010 011 012 Q6 013 014 015 016 EMG patterns during assisted walking in the exoskeleton 1,2 1,2 1 3 Francesca Sylos-Labini *, Valentina La Scaleia , Andrea d’Avella , Iolanda Pisotta , Federica Tamburella 3 , Giorgio Scivoletto 3 , Marco Molinari 3 , Shiqian Wang 4 , Letian Wang 5 , Edwin van Asseldonk 5 , Herman van der Kooij 4,5 , Thomas Hoellinger 6 , Guy Cheron 6 , Freygardur Thorsteinsson 7 , Michel Ilzkovitz 8 , Jeremi Gancet 8 , Ralf Hauffe 9 , Frank Zanov 9 , Francesco Lacquaniti 1,2,10 and Yuri P. Ivanenko 1 1 Laboratory of Neuromotor Physiology, Santa Lucia Foundation, Rome, Italy 2 Centre of Space Bio-medicine, University of Rome Tor Vergata, Rome, Italy 3 Spinal Cord Rehab Unit and CaRMA Lab, Santa Lucia Foundation, Rome, Italy 4 Biomechanical Engineering, Delft University of Technology, Delft, Netherlands 5 Biomechanical Engineering, University of Twente, Enschede, Netherlands 6 Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium 7 OSSUR, Reykjavík, Iceland 8 Space Applications Services N.V./S.A., Zaventem, Belgium 9 ANT Neuro, Berlin, Germany 10 Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy 021 022 023 024 025 026 027 028 029 030 061 062 063 064 065 068 069 070 071 072 073 074 075 Edited by: Marco Iosa, Fondazione Santa Lucia, Italy Reviewed by: Juan C. Moreno, Spanish National Research Council, Spain Stefano Masiero, University of Padua, Italy *Correspondence: Francesca Sylos-Labini, Laboratory of Neuromotor Physiology, IRCCS Fondazione Santa Lucia, 306 via Ardeatina, 00179 Rome, Italy e-mail: [email protected] 031 032 033 034 035 036 037 038 Neuroprosthetic technology and robotic exoskeletons are being developed to facilitate stepping, reduce muscle efforts, and promote motor recovery. Nevertheless, the guidance forces of an exoskeleton may influence the sensory inputs, sensorimotor interactions and resulting muscle activity patterns during stepping. The aim of this study was to report the muscle activation patterns in a sample of intact and injured subjects while walking with a robotic exoskeleton and, in particular, to quantify the level of muscle activity during assisted gait. We recorded electromyographic (EMG) activity of different leg and arm muscles during overground walking in an exoskeleton in six healthy individuals and four spinal cord injury (SCI) participants. In SCI patients, EMG activity of the upper limb muscles was augmented while activation of leg muscles was typically small. Contrary to our expectations, however, in neurologically intact subjects, EMG activity of leg muscles was similar or even larger during exoskeleton-assisted walking compared to normal overground walking. In addition, significant variations in the EMG waveforms were found across different walking conditions. The most variable pattern was observed in the hamstring muscles. Overall, the results are consistent with a non-linear reorganization of the locomotor output when using the robotic stepping devices. The findings may contribute to our understanding of human-machine interactions and adaptation of locomotor activity patterns. Keywords: robotic exoskeleton, assisted gait, EMG patterns, spinal cord injury, neuroprosthetic technology 076 077 078 079 080 081 082 083 084 085 086 087 088 089 090 091 092 093 094 095 039 096 040 097 041 Q5 060 067 018 020 059 066 017 019 058 098 042 INTRODUCTION 043 Exoskeleton robotic devices are now often used in the rehabilitation practice to assist physical therapy of individuals with neurological disorders (Sale et al., 2012; Moreno et al., 2013). To provide patients with some degree of locomotion capability, passive (unpowered) orthoses are often prescribed (Hsu et al., 2008). However, passive devices have many limitations, including the high energy expenditure and low utilization by individuals with severe walking impairments (Wang et al., 2014). Active (powered) exoskeletons and new control implementations are extensively developed in recent years to provide new possibilities for severely paralyzed patients to walk (Fitzsimmons et al., 2009; Swinnen et al., 2010; Cheron et al., 2012; del-Ama et al., 2012; Roy et al., 2012; Sale et al., 2012; Wang et al., 2014). Many of these devices include some form of body weight support and adjustable levels of robotic guidance forces. 044 045 046 047 048 049 050 051 052 053 054 055 056 057 Frontiers in Human Neuroscience Investigating locomotor responses in individuals after neurological lesions, as well as in healthy subjects, when using the robotic devices, is fundamental to the development of improved rehabilitation strategies and to explore the mechanisms involved in improving locomotor function (Ivanenko et al., 2013). Even in neurologically intact subjects, the use of external devices for stepping can affect motor patterns (Hidler and Wall, 2005; Lam et al., 2008; Van Asseldonk et al., 2008; Moreno et al., 2013), modify the “locomotor body scheme” and result in distortions in the body and space representation (Ivanenko et al., 2011). There is still a lack of knowledge on the effect of robotic gait assistance on the locomotor function and its recovery in injured humans due to the complex nature of the control of locomotion, compensatory strategies, and plasticity of neuronal networks. Several studies emphasized the importance of minimizing passive guidance and stabilization provided during gait rehabilitation www.frontiersin.org June 2014 | Volume 8 | Article 423 | 1 099 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 EMG patterns during assisted gait Sylos-Labini et al. 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 (Israel et al., 2006), establishing baseline patterns (Hidler and Wall, 2005) and reduction of metabolic cost of ambulant exoskeletons (Malcolm et al., 2013). Different artificial control schemes can induce different locomotor patterns. Here we used a control strategy of the exoskeleton consisting in weight shift to the stance side to trigger a step and to provide predefined reference joint trajectories (Wang et al., 2013). This exoskeleton assisted both posture (knee stabilization during stance, weight shift, lateral stabilization) and leg movements. The main purpose of this study was to report the muscle activation patterns in a sample of intact and injured subjects while walking with a robotic exoskeleton and, in particular, to quantify the level of muscle activity during assisted gait. It can be argued that robotic-guided walking should reduce leg muscle activity in healthy subjects to a lower level and might affect the motor output in patients as well. To verify this hypothesis, we investigated the adaptation of muscle activation patterns in neurologically intact human adults and spinal cord injury (SCI) patients using a recently developed exoskeleton (called MINDWALKER, https://www.mindwalker-project.eu). 134 135 METHODS 136 PARTICIPANTS 137 Six healthy volunteers (age range between 21 and 36 years, five males and one female, mean height 1.72 ± 0.09 m [mean ±SD (standard deviation)], weight 69 ± 12 kg) participated in this study. We also tested the MINDWALKER exoskeleton on SCI subjects (Table 1). Patient inclusion criteria were the following: age 18–45 years, traumatic/non-traumatic SCI, at least 5 month after injury with stable neurological score, complete lesion (AIS A, B at the time of inclusion) from below T7, inability to ambulate over ground without at least moderate assistance, Mini-Mental State Examination score >26. Exclusion criteria were: presence of transmissible diseases, such as (but not limited to) hepatitis, human immunodeficiency virus or Creutzfeldt-Jacob disease, symptomatic orthostatic hypotension or 30-mmHg drop when upright, subjects with spine-stabilizing devices for whom their treating surgeon contraindicates gait, contraindications for lower extremities weight bearing (pelvic or leg fracture, chronic joint pain), untreatable chronic pain, untreatable spasticity (Ashworth scale score >3), severe reduction in lower limb joint’s range of motion, pressure sore stage 2 or higher, skin injuries or problems such as blisters, burns, wounds from operation, or other superficial wounds at the scalp, debilitating disease prior to SCI that causes exercise intolerance and limits mobility-related self-care and instrumental activities of daily living, premorbid 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 major depression or psychosis, suicide attempt caused the SCI, unlikely to complete the intervention or return for follow-up, participation in another research. The studies conformed to the Declaration of Helsinki, and informed consent was obtained from all participants according to the procedures of the Ethics Committee of the Santa Lucia Foundation. 174 175 176 177 178 179 The detailed description of the exoskeleton and its control is provided elsewhere (Wang et al., 2013, 2014). Briefly, this exoskeleton is aimed at providing a research prototype that can empower lower limb disabled people (especially SCI patients) to walk on level ground (Figure 1A). Based on human anatomy and joint range of motion (RoM), the desired degrees of freedom (five at each leg) and joint RoM for the exoskeleton are specified to allow sitting, standing, and walking. In each leg, three degrees of freedom (DoFs: hip ab/adduction, hip flexion/extension and knee flexion/extension) are powered by series elastic actuators and two DoFs (hip endo/exo rotation and ankle dorsi/plantar flexion), are passively sprung with certain stiffness (800 and 180 Nm/rad, respectively). The exoskeleton weighs 28 kg excluding batteries and it bears its own weight by transferring the weight via its footplates to the ground. The exoskeleton can be attached to the wearer at five main locations: footplate, shank, thigh, pelvis, and torso (Figure 1A). A finite-state machine based controller was implemented for providing gait assistance in both sagittal and frontal planes and the swing phase initiation was triggered using trunk motion (Wang et al., 2013). For example, leaning to the left and forward triggers a right step: when the estimated center of mass (CoM) falls into a predefined region, the controller detected the intention of the subject and initiates assisted weight shift to left. Then the state transits automatically to right swing. Weight shift is initiated by the subject and completed by the exoskeleton. This control strategy is relatively simple, as well as it takes advantage of natural lateral trunk oscillations that always accompany normal walking (Cappellini et al., 2010). In this study, two control modes of the exoskeleton were used, namely, “EXO assisted” and “EXO-unassisted.” In the EXOunassisted mode, healthy subjects wore the exoskeleton with all motorized joints in torque control mode, in which the references were 0 torque. In this mode, the exoskeleton joints were moved by the human. As the controller bandwidth is limited (Wang et al., 2013) the actual exerted torques by the exoskeleton will not be zero and therefore we quantified the actual torques 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 161 218 Table 1 | Subject characteristics. 219 163 164 173 BRIEF DESCRIPTION OF MINDWALKER EXOSKELETON 160 Q4 162 172 220 Patient Age, year Gender Weight, kg Height, m Lesion level ASIA Aethiology Lesion time, months 165 221 222 166 p1 19 M 50 1.80 T12-L1 B Trauma 5 223 167 p2 21 M 67 1.78 T7 A Trauma 26 224 168 p3 22 M 70 1.80 T11-T12 A Trauma 36 225 169 p4 43 M 78 1.74 T9-T10 A Trauma 49 226 170 171 227 Lesion level indicates the clinical neurological level, lesion time the time interval between lesion diagnosis and data recording. Frontiers in Human Neuroscience www.frontiersin.org 228 June 2014 | Volume 8 | Article 423 | 2 EMG patterns during assisted gait Sylos-Labini et al. Q3 Q4 229 286 230 287 231 288 232 289 233 290 234 291 235 292 236 293 237 294 238 295 239 296 240 297 241 298 242 299 243 300 244 301 245 302 246 303 247 304 248 305 249 306 250 307 251 308 252 309 253 310 254 311 255 312 256 313 257 314 258 315 259 316 260 317 261 318 262 319 263 320 264 321 265 322 266 323 267 324 268 325 269 326 270 327 271 328 272 329 273 330 274 331 275 332 276 333 277 334 278 279 280 281 282 283 FIGURE 1 | Experimental setup. (A) MINDWALKER exoskeleton. Each leg has five degrees of freedom. Shank and thigh segments have telescopic tubular structure to accommodate different subject statues. The exoskeleton is attached to the wearer at five main locations: footplate, shank, thigh, pelvis, and torso. Footplates are made of carbon fiber and have braces to host human feet. Shank braces are used to support most of the weight of the user in standing and walking while thigh braces are added to loosely constrain the upper leg and support the wearer during standing up. Pelvis and backpack braces are used to attach the upper body to the wearer. (B) A healthy subject during walking in the exoskeleton. (C) Definition of touchdown and lift-off events from the hip joint angle during walking in the exoskeleton. (D) Definition of touchdown and lift-off events from the shank inertial sensor accelerations during normal walking. 335 336 337 338 339 340 284 341 285 342 Frontiers in Human Neuroscience www.frontiersin.org June 2014 | Volume 8 | Article 423 | 3 EMG patterns during assisted gait Sylos-Labini et al. 343 344 345 346 347 348 349 350 351 measured by exoskeleton sensors (see Results and Figure 2B). In the EXO-assisted mode, all exoskeleton joints were following predefined joint angles provided with variable joint impedances, the walking trajectories during the swing phase (reference joint angles) were defined based on walking patterns of a healthy subject walking in the MINDWALKER exoskeleton in the EXOunassisted mode (Wang et al., 2013). Hip and knee flexion angles were slightly increased during swing to ensure sufficient foot clearance. Since the reference trajectories for the swing phases were predefined, the swing phase durations were similar across conditions. 400 401 402 EXPERIMENT DESCRIPTION 403 Four experimental conditions in healthy individuals were recorded in the same experimental session: EXO-assisted, EXOunassisted, NM slow, NM self-selected. “NM slow” referred to normal slow walking without the exoskeleton and “NM selfselected” normal walking at self-selected speed without the 404 405 406 407 408 352 409 353 410 354 411 355 412 356 413 357 414 358 415 359 416 360 417 361 418 362 419 363 420 364 421 365 422 366 423 367 424 368 425 369 426 370 427 371 428 372 429 373 430 374 431 375 432 376 433 377 434 378 435 379 436 380 437 381 438 382 439 383 440 384 441 385 442 386 443 387 444 388 445 389 446 390 447 391 448 392 449 393 450 394 451 395 396 397 398 399 452 FIGURE 2 | Joint angles and exoskeleton torques recorded in healthy subjects during walking in the exoskeleton (EXO-assisted and EXO-unassisted). (A) Ensemble-averaged (mean ±SD, n = 6) joint angular movements. (B) Ensemble-averaged joint torques recorded in three powered Frontiers in Human Neuroscience actuators of the exoskeleton (knee and hip flexion/extension and hip ab/adduction). Note little torques in the hip and knee joints in the zero-torque mode (right panel) due to the absence of assistance. (C) Mean torques and peak-to-peak oscillations of torques. Asterisks denote significant differences. www.frontiersin.org June 2014 | Volume 8 | Article 423 | 4 453 454 455 456 EMG patterns during assisted gait Sylos-Labini et al. 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 exoskeleton. In the first two conditions, the participants were asked to walk along a 8-m walkway and were allowed to place the abducted arms on horizontal handrails located at the side of the walkway (Figure 1B), to provide stability/assistance if needed. A safety harness worn by the wearer was attached to an overhead suspension system moving along with the wearer, which only came into action when the subject fell. Typically, we collected the data from 2–4 trials while walking in the exoskeleton following a short period of training (1–2 trials). On average, 8–15 strides were recorded and analyzed in each experimental condition. The total duration of the experimental session was about 1–2 h. In the EXO-assisted condition, the subjects were instructed to move their CoM forward and toward a side to trigger a contralateral step (for example leaning to the left and forward would trigger a right step). In the EXO-unassisted condition, the subjects were told to just walk at their preferred pace bearing the exoskeleton to reach the end of the walkway. In the latter two conditions, healthy subjects walked without the exoskeleton along a 8-m walkway at slow and self-selected speeds. Gait initiation and gait termination strides were excluded from the analysis. About 10 strides were analyzed in each subject in each condition. The high speeds of normal walking were not recorded because walking in the exoskeleton was rather slow (see Results). One experimental condition in SCI participants was recorded, that is the EXO-assisted condition. A similar protocol as used in healthy individuals (control) was employed, for comparison. In participants with complete lesions, familiarization with MINDWALKER usage was more difficult and required several days of exoskeleton training (2 or 3 times/week) for a total of session ranging between 5 and 8. SCI participants achieved the control of balance holding the handrails located at the side of the walkway. All subjects presented high motivation since the first trial and throughout testing and the comparison between the first and last sessions for the whole group of patients present only minimal changes in the mean walking speed. No clinical changes were observed, between first and last trials, in the clinical scales (the detailed description of behavioral assessment and the physiological cost index are provided elsewhere, Pisotta et al., 2014), indicating that MINDWALKER usage does not affect the functional neurological status, consistent with a limited effectiveness of robot-assisted gait training in severely paralyzed individuals (Swinnen et al., 2010; Roy et al., 2012; Sale et al., 2012). Here we analyzed the stepping pattern in the last session, after familiarization with MINDWALKER usage. 503 504 DATA RECORDING, PROCESSING, AND GAIT EVENT DETECTION 505 In the exoskeleton walking conditions (EXO-assisted and EXOunassisted), joint angles and torques at aforementioned powered DoFs were recorded by the MINDWALKER exoskeleton at 1000 Hz and downsampled at 100 Hz to be used with the muscle activity recordings (Wang et al., 2013). Gait cycle events (touchdown and lift-off) were defined based on the kinematic data of the hip flexion-extension angle: touchdown as the first local minimum following the maximum and lift-off as the first local minimum preceding the maximum (Figure 1C). These kinematic 506 507 508 509 510 511 512 513 Frontiers in Human Neuroscience criteria were verified by comparison with the events detected by inertial signals from the sensor placed on the TA muscle using a similar method as during normal walking (Jasiewicz et al., 2006). In general, the difference between the time events measured from kinematics (Figure 1C) and inertial sensors was less than 4%. We divided the recorded kinematic and kinetic data into gait cycles (touchdown as the beginning of the gait cycle), then interpolated each stride to 200 time points, and finally averaged across gait cycles (individually for each subject). Joint torques were normalized to the total body weight (subject + exoskeleton) prior to averaging across subjects. It is worth noting that these are not the net joint torques of the subjects but the resulting torques exerted by the exoskeleton to move the subject’s limbs and whole body. Electromyographic (EMG) activity was recorded by means of surface electrodes from 11 muscles simultaneously on the right side of each subject. These included vastus medialis (VM), rectus femoris (RF), biceps femoris long head (BF), semitendinosus (ST), tibialis anterior (TA), medial gastrocnemius (MG), and soleus (Sol), anterior deltoid (DELTa), posterior deltoid (DELTp), flexor carpi ulnaris (FCU), extensor carpi ulnaris (ECU). We placed EMG electrodes based on suggestions from SENIAM (seniam.org), the European project on surface EMG, and by palpating to locate the muscle bellies and orienting the electrodes along the main direction of the fibers (Winter, 1991; Kendall et al., 2005). All EMGs were recorded at 2000 Hz using a Delsys Trigno Wireless System (Boston, MA). The EMG sensors of the Delsys Trigno Wireless System also contained 3D accelerometers, and we recorded and filtered (5 Hz low-pass zero-lag 4th order Butterworth) these inertial signals from the sensor placed on the TA muscle in order to define the gait cycle during walking without the exoskeleton (NM slow and NM self-selected walking): based on the method of Jasiewicz et al. (2006), touchdown was identified by minima in the shank x acceleration while lift-off was identified by maxima in the shank y acceleration (Figure 1D). 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 EMG DATA ANALYSIS 551 EMG data were processed using standard filtering and rectifying methods. We applied a 60 Hz high-pass filter, then rectified the EMG signals and applied a 10 Hz low-pass filter (all filters, zerolag 4th order Butterworth). EMG data were time-interpolated over a time base with 200 points for individual gait cycles (i = 1 ÷ 200) and averaged. In addition to computing the ensemble-averaged EMG waveforms (Winter, 1991; Perry, 1992), we calculated for each muscle and each subject the mean and maximum EMG activity and the center-of-activity (CoA) throughout the gait cycle. The CoA during the gait cycle was calculated using circular statistics (“circ_mean.m” function in the CircStat Matlab toolbox, Berens, 2009) and plotted in polar coordinates (polar direction denoted the phase of the gait cycle—with angle θ that varies from 0 to 360◦ corresponding to 0 and 100% cycle, respectively—and radius denoted the mean EMG activity of the muscle). The CoA of the EMG waveform was calculated as the angle of the vector (first trigonometric moment) which points to the CoM of that circular distribution using the following formulas: 552 www.frontiersin.org June 2014 | Volume 8 | Article 423 | 5 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 EMG patterns during assisted gait Sylos-Labini et al. 571 A= 572 200  573 t =1 574 200  B= 575 (cos θt × EMGt ) (sin θt × EMGt ) 576 t =1 577 CoA = tan−1 (B/A) 578 (1) (2) (3) 579 580 581 582 583 584 585 586 The CoA has been used previously to characterize the overall temporal shifts of EMG or motoneuron activity (Yakovenko et al., 2002; Ivanenko et al., 2006; Sylos-Labini et al., 2011) and was chosen because it was impractical to reliably identify a single peak of activity in the majority of muscles. It can be helpful to understand if the distribution of muscular activity remains unaltered across different conditions. movements in the sagittal plane were similar (Figure 2A), however, hip abduction was larger during assisted walking since lateral trunk movements were necessary to trigger the swing phase. EXO-assisted walking in the exoskeleton requires relatively large torques in the hip and knee joints (Figure 2B). For instance, peak-to-peak amplitudes in the knee and hip joints (normalized to the wearer-EXO’s weight) were about 1 Nm/kg (Figure 2C). Nevertheless, the torques that the exoskeleton applied to the subject (in the sagittal plane) were compatible to those exerted by subjects during normal overground walking (Winter, 1991). Note though that these torques were exerted by the exoskeleton (in order to move the subject’s limbs and body) rather than by the subjects themselves. As expected, during unassisted walking these torques were very small (Figure 2B, right panel), which was dictated by the closed-loop torque control performance of the exoskeleton. 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 Descriptive statistics included means ± standard deviation (SD) of the mean. The mean torque and peak-to-peak torque amplitudes were computed and compared across conditions. A repeated measure (RM) ANOVA was used to evaluate the effect of condition (on all parameters except for CoA) in healthy individuals. Post-hoc tests and multiple comparisons analysis were performed by means of the Bonferroni test. Circular statistics on directional data (Batschelet, 1981) were used to characterize the mean CoA for each muscle (see preceding text) and its variability across strides (angular SD). The Watson-William test was used for circular data (CoA) to evaluate the effect of condition in healthy individuals. Unpaired t-test was used to test differences in the exoskeleton torques and mean EMGs between controls and SCI patients. Statistics on Pearson’s correlation coefficients was performed on the normally distributed, Z-transformed values. Reported results are considered significant for p < 0.05. 606 RESULTS 607 Results were presented and briefly discussed in the following manner to make clear comparison: first, comparisons on kinematic and kinetic data were made between EXO-assisted and EXO-unassisted walking conditions in healthy individuals; second, for the same two walking conditions, EMG data in lower limbs were presented; third, EMG data in EXO-assisted and NM slow walking were given; and finally, for the EXO-assisted walking condition, comparisons were made between healthy and SCI subjects based on the kinematic/kinetic and EMG measurements. 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 STATISTICS 605 608 628 EXO-ASSISTED vs. EXO-UNASSISTED: KINEMATICS AND KINETICS IN HEALTHY SUBJECTS In general, as shown in Figure 3A, EXO-unassisted walking was faster than EXO-assisted walking (mean cycle duration was 3.2 ± 0.8 s vs. 6.1 ± 0.8 s). The swing phase durations were similar and the difference were caused by the dead time in stance, since in EXO-assisted walking, the subjects needed to move their trunk and trigger the swing step by step. Figures 2A,B illustrates ensemble-averaged angular movements and joint torques in healthy subjects in these two walking conditions. The amplitude of the knee and hip joint angular Frontiers in Human Neuroscience EXO-ASSISTED vs. EXO-UNASSISTED: EMG PATTERNS IN HEALTHY SUBJECTS 645 Figure 3B illustrates ensemble-averaged EMG patterns of leg muscles in control subjects in different walking conditions. During EXO-assisted walking the exoskeleton provided all necessary torques to support the body and move the legs forward while during unassisted walking the subjects moved their and exoskeleton’s legs together. Accordingly, it was not surprising that during unassisted walking the amplitude of EMG activity was typically larger than that during assisted walking (Figure 3B, left two panels). Specifically, the mean activity was significantly larger for the RF, VM, TA, MG, and Sol muscles while it was comparable for BF and smaller for ST (Figure 4A). It is also worth noting that EMG waveforms differed for BF and ST: in particular, there was no activity in these muscles at the beginning of the stance phase during not-assisted walking (Figure 3B). The correlation analysis confirmed similarities in the EMG waveforms for RF, VM, MG, and Sol muscles and differences for BF, ST, and TA muscles between these two conditions (Table 2). 647 646 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 EXO-ASSISTED vs. NM SLOW WALKING: EMG PATTERNS IN HEALTHY SUBJECTS 665 During normal walking, muscle activity typically increases with increasing walking speed (Ivanenko et al., 2006). Therefore, comparisons of normal and pathological gait are typically performed at similar walking speeds. Assisted walking in the exoskeleton (EXO assisted) was relatively slow compared to normal walking (Figure 3A). The swing phase duration was also longer for the EXO assisted condition (Figure 3A). Since the movements of the limbs were performed by the exoskeleton, one would expect substantially lower muscle activity during assisted walking. Interestingly, contrary to our expectations, assisted walking in the exoskeleton was not accompanied by reduced EMG activity. In fact, the activity of most muscles (RF, VM, TA, MG, Sol) did not change significantly, while the activity of BF and ST even increased during assisted walking despite the slower walking speed in this condition (Figure 4A). EMG waveforms of some leg muscles also differed between assisted gait and normal walking (Figure 3B). For instance, RF and VM activity contained additional bursts during the swing 667 www.frontiersin.org June 2014 | Volume 8 | Article 423 | 6 666 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 EMG patterns during assisted gait Sylos-Labini et al. 685 742 686 743 687 744 688 745 689 746 690 747 691 748 692 749 693 750 694 751 695 752 696 753 697 754 698 755 699 756 700 757 701 758 702 759 703 760 704 761 705 762 706 Q7 707 708 763 FIGURE 3 | EMG patterns in healthy subjects during walking in the exoskeleton and during normal overground walking. (A) Stride and swing durations (mean +SD, n = 6) for each experimental condition. (B) Time course of ensemble-averaged EMG patterns (dark area, gray area corresponds to SD). 764 765 709 766 710 767 711 768 712 769 713 770 714 771 715 772 716 773 717 774 718 775 719 776 720 777 721 778 722 779 723 780 724 781 725 782 726 783 727 784 728 785 729 786 730 787 731 788 732 789 733 790 734 791 735 736 737 738 739 FIGURE 4 | Characteristics of EMG activity during assisted and normal walking in control subjects. (A) Mean and maximum EMG activities (left and right panels, respectively) for each muscle (mean +SD). (B) Polar plots of the center of EMG activity. Polar direction denotes the relative time over the gait cycle (time progresses clockwise), radius of the vector denotes the mean EMG activity of the muscle and the width of the sector denotes angular SD (across subjects). Polar grid with circles was also shown to ease comparisons (the number in the right corner of each plot corresponds to the value of the external circle). Asterisks denote significant differences across conditions. 792 793 794 795 796 740 797 741 798 Frontiers in Human Neuroscience www.frontiersin.org June 2014 | Volume 8 | Article 423 | 7 EMG patterns during assisted gait Sylos-Labini et al. 799 800 801 802 Table 2 | Pearson correlation coefficients (mean ±SD, n = 6) between EMG waveforms for different conditions in control subjects. Muscle Condition RF EXO-assisted 856 NW self-selected 857 EXO-assisted EXO-unassisted NW slow – 0.32 ± 0.29* 0.10 ± 0.21 0.07 ± 0.10 859 858 803 EXO-unassisted 0.32 ± 0.29* – 0.58 ± 0.52* 0.35 ± 0.27* 860 804 NW slow 0.10 ± 0.21 0.58 ± 0.52* – 0.32 ± 0.13* 861 805 NW self-selected 0.07 ± 0.10 0.35 ± 0.27* 0.32 ± 0.13* – 862 – 0.70 ± 0.35* 0.41 ± 0.26* 0.29 ± 0.13* 864 806 807 863 VM EXO-assisted 808 EXO-unassisted 0.70 ± 0.35* – 0.61 ± 0.28* 0.56 ± 0.20* 865 809 NW slow 0.41 ± 0.26* 0.61 ± 0.28* – 0.50 ± 0.24* 866 810 NW self-selected 0.29 ± 0.13* 0.56 ± 0.20* 0.50 ± 0.24* – 867 – 0.01 ± 0.34 0.01 ± 0.34 811 812 BF EXO-unassisted 813 NW slow 814 NW self-selected 815 816 EXO-assisted ST EXO-assisted 868 −0.11 ± 0.29 0.25 ± 0.41 – 0.26 ± 0.34 0.47 ± 0.51 −0.11 ± 0.29 0.26 ± 0.34 – 0.29 ± 0.09* 0.25 ± 0.41 0.47 ± 0.51 0.29 ± 0.09* – 0.20 ± 0.36 0.17 ± 0.17* 873 −0.34 ± 0.10* 874 −0.07 ± 0.33 – 869 870 871 872 817 EXO-unassisted 818 NW slow 0.20 ± 0.36 −0.24 ± 0.13* – 0.38 ± 0.13* 875 819 NW self-selected 0.17 ± 0.17* −0.34 ± 0.10* 0.38 ± 0.13* – 876 – 0.35 ± 0.49 0.14 ± 0.21 0.22 ± 0.07* 878 −0.07 ± 0.33 – −0.24 ± 0.13* 820 821 877 TA EXO-assisted 822 EXO-unassisted 0.35 ± 0.49 – 0.33 ± 0.28* 0.26 ± 0.30 879 823 NW slow 0.14 ± 0.21 0.33 ± 0.28* – 0.72 ± 0.21* 880 824 NW self-selected 0.22 ± 0.07* 0.26 ± 0.30 0.72 ± 0.21* – 881 – 0.65 ± 0.34* 0.60 ± 0.30* 0.73 ± 0.34* EXO-unassisted 0.65 ± 0.34* – 0.74 ± 0.14* 0.78 ± 0.25* NW slow 0.60 ± 0.30* 0.74 ± 0.14* – 0.74 ± 0.14* NW self-selected 0.73 ± 0.34* 0.78 ± 0.25* 0.74 ± 0.14* – – 0.67 ± 0.37* 0.65 ± 0.25* 0.74 ± 0.37* 887 825 826 Sol 827 828 829 830 MG EXO-assisted EXO-assisted 882 883 884 885 886 831 EXO-unassisted 0.67 ± 0.37* – 0.72 ± 0.24* 0.87 ± 0.38* 888 832 NW slow 0.65 ± 0.25* 0.72 ± 0.24* – 0.79 ± 0.25* 889 833 NW self-selected 0.74 ± 0.37* 0.87 ± 0.38* 0.79 ± 0.25* – 890 834 835 891 Asterisks denote correlation coefficients significantly different from zero, t-test. 892 836 837 838 839 840 841 842 843 893 phase (Figure 3B), BF and ST muscles were activated in midstance and early swing during assisted walking (Figure 3B) and the center of activity of the ST muscle differed significantly between normal and assisted walking (Figure 4B). The correlation analysis showed low correlations for most muscles: only VM, Sol, and MG muscles demonstrated significant correlations between these two conditions (Table 2). 844 845 846 847 848 849 850 851 852 853 854 855 SCI vs. HEALTHY SUBJECTS IN EXO-ASSISTED CONDITION: KINEMATICS, KINETICS, AND EMG PATTERNS SCI patients walked slightly slower than the control subjects (Figure 5A, on average, the cycle duration was 6.1 ± 0.8 s in control subjects and 7.6 ± 1.1 s in SCI patients), though the swing phase duration (Figure 5A right panel) and the angular movements (Figure 5B left panel) were similar. The amplitude (peakto-peak) of the exoskeleton torques was also similar (only the knee torque was larger, Figure 5C), suggesting that the exoskeleton provided the main forces for stepping in both control and SCI subjects. Frontiers in Human Neuroscience Despite similarities in the kinematics and dynamics of movements, EMG patterns differed in SCI patients. Overall, they used more upper limb muscles for stepping (DELTp and ECU Figure 5E) though there was also variability in using the arms muscles between subjects (compare, for instance, the control and the SCI subject in Figure 5D). EMG activity in the lower limb muscles was typically minute if any in SCI patients, though one SCI patient demonstrated consistent activity in the BF, ST, RF, and MG muscles during the swing phase and beginning of stance (Figure 5D right panel). 894 895 896 897 898 899 900 901 902 903 904 DISCUSSION 905 We investigated the effect of walking with an exoskeleton on the muscular activation patterns in healthy subjects and SCI patients. Strikingly, despite exoskeleton assistance in both posture and leg movements, the overall muscle activity level in healthy subjects was not reduced at all, as one would expect, further supporting the importance of sensory input and suitability of using robotic exoskeletons for entraining lumbosacral locomotor circuitry. The 906 www.frontiersin.org June 2014 | Volume 8 | Article 423 | 8 907 908 909 910 911 912 EMG patterns during assisted gait Sylos-Labini et al. 913 970 914 971 915 972 916 973 917 974 918 975 919 976 920 977 921 978 922 979 923 980 924 981 925 982 926 983 927 984 928 985 929 986 930 987 931 988 932 989 933 990 934 991 935 992 936 993 937 994 938 995 939 996 940 997 941 998 942 999 943 1000 944 1001 945 1002 946 1003 947 1004 948 Q8 949 950 951 952 953 1005 FIGURE 5 | EMG activity in healthy subjects and SCI patients during walking in the exoskeleton (“EXO-assisted” mode). (A) Joint angles and exoskeleton torques recorded in SCI patients during walking in the exoskeleton. The same format as in Figure 2. (B) Mean torques and peak-to-peak oscillations of torques in control subjects and patients. (C) An example of EMG activity in the upper and lower limb muscles in a healthy subject (left) and SCI patient (right, p3 Table 1) during walking in the exoskeleton. Note EMG activity in the upper limb muscles in both subjects. Note also some EMG activity in the ST, BF, and MG muscles in the patient despite neurologically complete paraplegia. (D) Mean (+ SD) EMG activity of the upper and lower limb muscles. Asterisks denote significant differences. 954 957 958 959 961 EMG PATTERNS IN HEALTHY SUBJECTS 962 To assess similarities in the EMG waveforms across conditions, we used both the correlation analysis and calculated the center of EMG activity in the gait cycle. Both invariant features and significant variations in the EMG waveforms were observed across different walking conditions (Figure 4, Table 2). For instance, the correlation analysis revealed that in EXOassisted and normal slow walking Sol and MG muscles demonstrated significant correlations (Table 2), which could be 964 965 966 967 968 969 1008 1009 1010 1012 results also showed a non-linear reorganization of EMG patterns under different walking conditions (Figures 3–5, Table 2). Below we discuss the findings in the context of adaptability of locomotor patterns and human-machine interactions. 960 963 1007 1011 955 956 1006 Frontiers in Human Neuroscience explained by the fact that the exoskeleton ankles were not powered and in both conditions human ankles were actively contributing to locomotion and to antigravity calf muscle activity during foot loading in the stance phase (Nielsen and Sinkjaer, 2002). The most variable pattern was generally observed in the hamstring muscles (BF and ST). This can be explained by the important contribution of stretch reflexes in this muscle in the context of a “passive” contribution (Duysens et al., 1998), but it can also be interpreted in terms of the more proximal muscles being less dependent on sensory feedback than the distal ones (in the context of “active” contribution from central sources). Another explanation can be related to the fact that the hamstring muscle (in particular, the semimembranosus and semitendinosus muscles) is specifically involved in the locking of the erect posture www.frontiersin.org June 2014 | Volume 8 | Article 423 | 9 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 EMG patterns during assisted gait Sylos-Labini et al. 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 by producing a tonic activation against the action of gravity (Cheron et al., 1997). Indeed, an anticipated inhibition of the hamstring activity worked in conjunction with the phasic activation of the TA and the action of gravity (Cheron et al., 1997). In the present “EXO-assisted” situation, such inhibitory modulation related to normal graviception can be disturbed by the presence of these artificial forces. The context-specific function of the hamstring muscle was also reported in other experimental conditions (Ivanenko et al., 2000; Sylos-Labini et al., 2014). The amplitude of EMG activity varied across conditions. Walking in EXO-unassisted mode in healthy individuals was accompanied by the augmented motor output (Figures 3, 4), likely due to additional inertia and weight of the exoskeleton. Strikingly, however, walking in the EXO-assisted mode was not accompanied by the reduction of leg muscle EMG activity despite limb movement assistance. This can be explained in part by the important contribution of afferent feedback to the pre-programmed motoneuronal drive (Nielsen and Sinkjaer, 2002), different biomechanical demands and the “active” nature of stepping in the exoskeleton (the subject was not fully “relaxed,” needed to maintain the upper trunk posture and provide small lateral trunk displacements to trigger step transitions) even though the limb movements were guided by the exoskeleton. Another possible cause could be the intermittent contact between the exoskeleton and the subject. The brace connections were not tight and had slag (for comforts and to prevent overloading human joints since minor misalignments could not be avoided.). It could be that the subject was ambulating on his own and the exoskeleton was acting as disturbances to the subject due to the intermittent contacts. Adaptive non-linear changes in both amplitude and temporal envelope have been reported in other walking conditions as well (Hidler and Wall, 2005; Israel et al., 2006; Lam et al., 2008; Van Asseldonk et al., 2008; Moreno et al., 2013). For instance, with body weight unloading (Ivanenko et al., 2002), most muscles (e.g., gluteus maximus and distal leg extensors) decrease their activity, while other muscles demonstrate a “paradoxical” increment of activation (e.g., quadriceps) or considerable changes in the activation waveforms (hamstring muscles). Even the amplitude of EMG activity of “anatomical” synergists may diverge remarkably: lateral and medial gastrocnemius muscles at different walking speeds (Huang and Ferris, 2012), soleus and gastrocnemius muscles at different levels of limb loading (McGowan et al., 2010). In addition, muscle activity patterns are shaped by the direction of progression (e.g., forward vs. backward, Grasso et al., 1998, or walking along a curved path, Courtine et al., 2006). In particular, such studies suggest that a comparison of normal and pathological gait should be preferably performed in the same stepping conditions. Taken together, the data support the idea of plasticity and distributed networks for controlling human locomotion (Scivoletto et al., 2007; Ivanenko et al., 2013). Tens of muscles participate in the control of limb and body movements during locomotion, and redundancy in the neuromuscular system is an essential element of gait adaptability (Winter, 1989; Cai et al., 2006; Noble and Prentice, 2006; Molinari, 2009; Duysens et al., Frontiers in Human Neuroscience 2013; Ivanenko et al., 2013). Due to muscle redundancy, various neuromotor strategies may exist to compensate for decreased muscle strength and pathology (Grasso et al., 2004; Goldberg and Neptune, 2007; Huang and Ferris, 2012; Gordon et al., 2013). 1084 1085 1086 1087 1088 1089 EMG PATTERNS IN SCI PATIENTS 1090 Flexibility and adaptability of locomotor patterns are evident from monitoring and analyzing the spatiotemporal spinal segmental output after SCI (Grasso et al., 2004; Scivoletto et al., 2007). For instance, in motor incomplete paraplegics who recovered independent control of their limbs, an additional activation burst, related to abnormal activation of the quadriceps muscle, is often present in the lumbosacral enlargement (Ivanenko et al., 2013). Patients can be trained to step with body weight support unassisted, but they use activity patterns in individual muscles that were often different from healthy individuals (Grasso et al., 2004). In this study we used the reference patterns based on prerecorded trajectories from unimpaired volunteer walking in the device while it is operated in a transparent mode. Other approaches may be based on patient specific patterns by recording the gait trajectory while the patient walks with manual assistance (Aoyagi et al., 2007), but this may be done only in individuals with less severe paresis of the lower limbs. Further investigations are needed regarding the possible effect that the selected reference gait pattern may have on the findings and also regarding possible solutions for reference gait pattern customization for SCI. Patients with severe SCI disorders frequently show EMG patterns different from those of healthy individuals suggesting that human spinal cord can interpret differently loading- or velocity-dependent sensory input during stepping (Beres-Jones and Harkema, 2004). Complete paraplegics also use more their arms and largely rely on proximal and axial muscles to assist the leg movements and balance control (Figures 5D,E, see also Grasso et al., 2004). During assisted walking in the exoskeleton, complete paraplegics typically showed little if any leg muscle activity (Figure 5E). Only one patient (p3, Table 1) demonstrated consistent activity in the BF, ST, RF, and MG muscles during swing and beginning of stance (Figure 5D right panel), suggesting the contribution of stretch- or loading-related afferent inputs to muscle activity (Maegele et al., 2002; Beres-Jones and Harkema, 2004; Grasso et al., 2004). Nevertheless, this reflex-related activity might be beneficial for potential gait rehabilitation since there is a relationship between facilitation of segmental reflexes and the ability to recover gait (Dietz et al., 2009; Thompson and Wolpaw, 2014). Thus, in addition to gait assistive aspects of exoskeleton robotic devices in severely paralyzed individuals, the proposed approach may also be beneficial for gait rehabilitation. We did not test in this study the effect of robot-assisted gait training in persons with SCI. Longer sessions would be required to evaluate the adequate learning paradigm, likely in combination with other central pattern generator-modulating therapies (Roy et al., 2012; Guertin, 2014) and biofeedback that might help the patients to adapt their movement patterns and to improve their motivation (Lünenburger et al., 2007). 1091 www.frontiersin.org June 2014 | Volume 8 | Article 423 | 10 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 Sylos-Labini et al. 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Citation: Sylos-Labini F, La Scaleia V, d’Avella A, Pisotta I, Tamburella F, Scivoletto Effectiveness of robot-assisted gait training in persons with spinal cord injury: a 1274 G, Molinari M, Wang S, Wang L, van Asseldonk E, van der Kooij H, Hoellinger T, systematic review. J. Rehabil. Med. 42, 520–526. doi: 10.2340/16501977-0538 1275 Cheron G, Thorsteinsson F, Ilzkovitz M, Gancet J, Hauffe R, Zanov F, Lacquaniti F Sylos-Labini, F., Ivanenko, Y. P., Cappellini, G., Gravano, S., and Lacquaniti, F. 1276 and Ivanenko YP (2014) EMG patterns during assisted walking in the exoskeleton. (2011). Smooth changes in the EMG patterns during gait transitions under body 1277 Front. Hum. Neurosci. 8:423. doi: 10.3389/fnhum.2014.00423 weight unloading. J. Neurophysiol. 106, 1525–1536. doi: 10.1152/jn.00160.2011 1278 Sylos-Labini, F., Ivanenko, Y. P., Maclellan, M. J., Cappellini, G., Poppele, R. E., This article was submitted to the journal Frontiers in Human Neuroscience. Copyright © 2014 Sylos-Labini, La Scaleia, d’Avella, Pisotta, Tamburella, Scivoletto, and Lacquaniti, F. (2014). Locomotor-like leg movements evoked by rhyth1279 Molinari, Wang, Wang, van Asseldonk, van der Kooij, Hoellinger, Cheron, mic arm movements in humans. PLoS ONE 9:e90775. doi: 10.1371/jour1280 Thorsteinsson, Ilzkovitz, Gancet, Hauffe, Zanov, Lacquaniti and Ivanenko. This is an nal.pone.0090775 1281 open-access article distributed under the terms of the Creative Commons Attribution Thompson, A. K., and Wolpaw, J. R. (2014). Operant conditioning of spinal 1282 License (CC BY). The use, distribution or reproduction in other forums is permitted, reflexes: from basic science to clinical therapy. Front. Integr. Neurosci. 8:25. doi: provided the original author(s) or licensor are credited and that the original publica10.3389/fnint.2014.00025 1283 tion in this journal is cited, in accordance with accepted academic practice. No use, 1284 Van Asseldonk, E. H. F., Veneman, J. F., Ekkelenkamp, R., Buurke, J. H., distribution or reproduction is permitted which does not comply with these terms. Van der Helm, F. C. T., and van der Kooij, H. (2008). The effects on 1312 1256 1313 1285 1342 1286 1343 1287 1344 1288 1345 1289 1346 1290 1347 1291 1348 1292 1349 1293 1350 1294 1351 1295 1352 1296 1353 1297 1354 1298 1355 1299 1356 1300 1357 1301 1358 1302 1359 1303 1360 1304 1361 1305 1362 1306 1363 1307 1364 1308 1365 1309 1366 1310 1367 1311 1368 Frontiers in Human Neuroscience View publication stats www.frontiersin.org June 2014 | Volume 8 | Article 423 | 12 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341