Haptic Error Modulation Outperforms Visual Error Amplification When Learning a Modified Gait Pattern

Abstract

Robotic algorithms that augment movement errors have been proposed as promising training strategies to enhance motor learning and neurorehabilitation. However, most research effort has focused on rehabilitation of upper limbs, probably because large movement errors are especially dangerous during gait training, as they might result in stumbling and falling. Furthermore, systematic large movement errors might limit the participants' motivation during training. In this study, we investigated the effect of training with novel error modulating strategies, which guarantee a safe training environment, on motivation and learning of a modified asymmetric gait pattern. Thirty healthy young participants walked in the exoskeletal robotic system Lokomat while performing a foot target-tracking task, which required an increased hip and knee flexion in the dominant leg. Learning the asymmetric gait pattern with three different strategies was evaluated: (i) No disturbance: no robot disturbance/guidance was applied, (ii) haptic error amplification: unsafe and discouraging large errors were limited with haptic guidance, while haptic error amplification enhanced awareness of small errors relevant for learning, and (iii) visual error amplification: visually observed errors were amplified in a virtual reality environment. We also evaluated whether increasing the movement variability during training by adding randomly varying haptic disturbances on top of the other training strategies further enhances learning. We analyzed participants' motor performance and self-reported intrinsic motivation before, during and after training. We found that training with the novel haptic error amplification strategy did not hamper motor adaptation and enhanced transfer of the practiced asymmetric gait pattern to free walking. Training with visual error amplification, on the other hand, increased errors during training and hampered motor learning. Participants who trained with visual error amplification also reported a reduced perceived competence. Adding haptic disturbance increased the movement variability during training, but did not have a significant effect on motor adaptation, probably because training with haptic disturbance on top of visual and haptic error amplification decreased the participants' feelings of competence. The proposed novel haptic error modulating controller that amplifies small task-relevant errors while limiting large errors outperformed visual error augmentation and might provide a promising framework to improve robotic gait training outcomes in neurological patients.

Keywords: error amplification; force disturbance; haptic guidance; motor adaptation; motor learning; rehabilitation robotics; robotic gait-training; visual feedback.

FIGURE 1

The Lokomat^® (Hocoma AG, Switzerland) is a bilateral gait robotic orthosis that, together with a body-weight support system and a treadmill, controls patient’s hips and knees movements in the sagittal plane (Tsangaridis et al., 2018). The participant in this figure consented to the publication of her image.

FIGURE 2

Example of the ankle trajectories that resulted from applying forward kinematic analysis to a participant’s average hip and knee joints (θ_ave), the Lokomat original (θ_Lok) and adjusted Lokomat references (_Lok), and the final reference trajectory (θ_ref) in the non-dominant (A), and dominant legs (B). The final trajectory is the result of increasing the hip and knee angle ROM of the dominant leg by 20%. The final reference ankle trajectory was shown on the screen (C) together with the actual ankle position and an avatar representation of the legs (with dominant leg on top).

FIGURE 3

(Up) The impedance gain in the error modulating haptic controller (λ) depends on the participants’ ongoing error following a combined sigmoid function. (Bottom) The resulting torque (T_amp) amplifies small errors (e < e_turn) with error amplification (EA), but prevents participants from performing large errors (e > e_turn) with haptic guidance (HG).

FIGURE 4

Experimental protocol. Participants were randomly allocated to one of three training groups (Parallel design: no disturbance [ND/Control], haptic error amplification [HEA], and visual error amplification [VEA]). Within these groups, participants were split into two groups (cross-over design), depending whether they started training with haptic disturbance (HD) added on top of their main training strategy (HD1), or they started training without haptic disturbance (HD2). Motor learning was evaluated at mid-training retention (MTR), short-term retention (STR), and long-term retention (LTR). After baseline, after the first and second training blocks, and after the short- and long-term retention tests, participants responded to six statements (Table 1) selected from the Intrinsic Motivation Inventory (IMI).

FIGURE 5

Effect of the training strategies on performance during training (shadowed trials) and retention tests. (A) Mean spatial error. (B) Tracking errors. Error bars: ±1 SE.

FIGURE 6

(Up) Trajectory of the tracking error reduction from baseline to mid-training retention. Positive values indicate that tracking error was reduced after training. Clouds represent the standard error. (Bottom) SPM{F} statistic for the one-way ANOVA with training groups as effect and tracking error reduction from baseline to mid-training retention as dependent variable. Vertical lines indicate starting and finishing time frames of the significant supra-threshold clusters.

FIGURE 7

Effect of the training strategies on free walking. (A) Knee asymmetry. (B) Hip asymmetry. Error bars: ±1 SE.

FIGURE 8

(Up) Mean trajectory of the knee angle differences during Calibration (solid blue) and free walking 2 (dashed green) from the HEA group. Clouds represent the standard error. (Bottom) SPM{t} statistic for the paired t-test (knee differences between Calibration and free walking 2) in the HEA group. Vertical cyan lines indicate starting and finishing points of the supra-threshold cluster in the test.

FIGURE 9

Effect of the training strategy on changes in responses to IMI subscales statements from baseline to each training block and after short and long retention tests. (A) Changes in responses assessing interest/enjoyment. (B) Changes in perceived competence. (C) Changes in effort/importance. (D) Changes in responses in each IMI subscale from baseline to first training session, for participants trained with haptic disturbance (HD) (dark gray) and without disturbance (light gray). ^∗p < 0.05, °p < 0.1. Error bars: ±1 SE.