Research ArticleNEUROREHABILITATION

A multidirectional gravity-assist algorithm that enhances locomotor control in patients with stroke or spinal cord injury

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Science Translational Medicine  19 Jul 2017:
Vol. 9, Issue 399, eaah3621
DOI: 10.1126/scitranslmed.aah3621
  • Fig. 1. Technological framework of the multidirectional gravity-assist.

    (A) Schematic and image of the robotic support system, including the directions of the actuated and passive (rotation) degrees of freedom. The spatial trajectory and instantaneous speed of the CoM are shown during locomotion within the entire workspace. Images (1 to 4) illustrating the recovery from a fall. (B) Kinematics, electromyogram (EMG) activity, and GRFs are recorded concomitantly. A gait sequence is shown, during which an upward force followed by an increasing forward force are applied to a healthy subject during walking. From top to bottom: Desired forces, measured forces, left and right stance durations, leg joint angles, EMG activity, vertical GRFs, and timing of applied forces.

  • Fig. 2. Interaction between upward and forward forces during standing and walking.

    (A) Schematic of the body, including the CoM, the postural orientation (β), and CoP. The mean position of the CoP with respect to the feet is shown during standing with transparent (Transp) support, upward force only, and both upward and forward forces. A concomitant sequence of EMG activity from ankle extensor (Sol, soleus; MG, medial gastrocnemius) and ankle flexor muscles (TA, tibialis anterior) is displayed. The plot represents the continuous and mean (colored circle) positions of the CoP for each condition. The x axis refers to the axis passing through the malleoli, whereas the y axis corresponds to the midline between the feet. (B) Plots reporting the means ± SEM of β during standing and gait parameters under the conditions shown in (A) and (C). *P < 0.05, Wilcoxon signed-rank tests, n = 5 healthy subjects. n.s., not significant. (C) Stick diagram decomposition (rate, 120 ms) of head, trunk, and leg movements during stance (dark, shading) and swing (light, unshaded). The stick diagram decomposition of trunk and leg movements is shown for sides. The filled and dashed lines differentiate the right and left legs, respectively. The EMG activity of extensor and flexor muscles acting at the ankle and knee (VL, vastus lateralis; BF, biceps femoris) is displayed, together with the CoM trajectory in the sagittal plane and GRFs. (D) Gait kinematics of one subject shown in the space created by PC1 and PC2 (%, explained variance). Each color-coded dot corresponds to a single gait cycle, whereas the black circles indicate the average value for each condition.

  • Fig. 3. Design of the MGA algorithm.

    (A) Stick diagram decomposition of whole-body movements, continuous CoP trajectory, and EMG activity of ankle muscles during standing with upward forces ranging from 25 to 60% of body weight support (5% increments) for a nonambulatory individual with a SCI. (B) Plot showing the relationship between the upward force and the Euclidian distance between the data of the participant shown in (A) and healthy subjects in the space defined by PC1 and PC2 (PCA performed on 15 parameters). The minimal distance (blue dot) was defined as the optimal upward force. (C) The measured variables were fed into an artificial neural network that calculated the correction of body weight support (Δ, upward force in percentage of body weight) to facilitate gait. The plot shows the relationship between the experimentally determined correction and the prediction of the neural network. Each dot corresponds to a given condition of upward force for a subject with SCI or stroke who contributed to the training or test data set. The histogram plot reports the occurrence rate of errors in the prediction of the corrections calculated by the artificial neural network. (D) Three-dimensional plots reporting the relationships between the upward force, the optimal forward force, and the speed. Each data point corresponds to values measured in the simulations and in subjects with SCI or stroke. To include data from subjects with varying biometry, the speed is represented as the Froude number, which takes into account the length of the leg to normalize the speed. A polynomial function was fitted through both simulated and experimental data points.

  • Fig. 4. Validation of MGA algorithm in individuals with SCI or stroke.

    (A) Steps for configuration of the MGA. (B) Stick diagram decomposition of whole-body movements (and walker, blue) for a nonambulatory subject with SCI walking with three ranks of upward force and associated forward force. The position of the feet during stance and their trajectories during swing is indicated (gray) together with the trajectory of the wheels of the walker (blue). A representative sequence of EMG activity recorded from ankle muscles is reported for each condition together with the stance duration of each leg (gray bar). Foot dragging is indicated in brown. The CoM trajectory is displayed at the bottom in blue. (C) Locomotor performance was quantified as the Euclidian distance between gait cycles of each subject in three different conditions of upward assistance versus healthy individuals in the PCA space (fig. S3). Middle blue refers to the gravity-assist condition. Light blue and dark blue correspond to a 10% decrease and increase in the amount of upward force, respectively. *P < 0.05, Friedman test with Tukey-Kramer post hoc tests. (D) Impact of suboptimal force configurations based on locomotor performance. (E) Plots reporting the means of selected gait parameters under the three conditions of upward force. The mean (horizontal orange bar) ± SEM (thickness) measured during locomotion in healthy subjects is represented. *P < 0.05, Wilcoxon signed-rank tests. Data are means per subject, n = 6 pathological subjects.

  • Fig. 5. Performance of the MGA to enable or enhance locomotor control after SCI.

    (A) Subjects with SCI walked overground without and with MGA using the least-assistive device possible. To quantify locomotor performance and identify the most relevant parameters, a PCA-based method has been applied, as described in fig. S3. On the right, the computed parameters that strongly correlated with a given PCA (|loading factors| > 0.5) were regrouped into functional clusters. Numbers refer to table S1. (B) Subjects were segregated into four categories: nonambulatory (wheelchair), walker, crutches, and none. Image and stick diagram decomposition of whole-body movements (and assistive device) are shown for each category and condition. (C) Plots reporting the mean values of locomotor performance and classic gait parameters for each subject during locomotion without and with MGA. The horizontal orange bars report the means ± SEM (thickness) measured during locomotion in healthy subjects. *P < 0.05, Wilcoxon signed-rank tests, n = 15 subjects with SCI.

  • Fig. 6. Performance of the MGA to enable or enhance locomotor control after stroke.

    (A) Subjects with stroke walked overground without and with MGA using the least-assistive device possible. To quantify locomotor performance and identify the most relevant parameters, a PCA-based method has been applied, as described in fig. S3. On the right, the computed parameters that strongly correlated with a given PCA (|loading factors| > 0.5) were regrouped into functional clusters. Numbers refer to table S1. (B) Subjects were segregated into three categories: nonambulatory (wheelchair), crutches, and none. Images and stick diagram decomposition of whole-body movements (and assistive device) are shown for each category and condition. (C) Plots reporting the mean values of locomotor performance and classic gait parameters for each subject during locomotion without and with MGA. In addition, a symmetry index reports the relative symmetry between left and right step lengths for each subject. The mean (horizontal orange bar) ± SEM (thickness) measured during locomotion in healthy subjects is represented. *P < 0.05, Wilcoxon signed-rank tests, n = 12 subjects with stroke.

  • Fig. 7. Gait training session overground with MGA or on a treadmill with upward support.

    (A) Experimental design of the 1-hour training sessions. (B) Plots reporting gait speed and double stance duration for each successive gait cycle of subject SCI_HCU over the course of the entire session. The color coding refers to the experimental design detailed in (A). (C) Stick diagram decomposition of whole-body movements recorded overground without robotic assistance. The blue stick represents the crutch. The plot reports the locomotor performance of n = 5 subjects with SCI before and after training with MGA (light blue), as well as 1 week later (before and after training restricted to a treadmill, dark blue). Locomotor performance was evaluated using the PCA-based method described in fig. S3. The horizontal orange bars report the means ± SEM (thickness) measured during locomotion in healthy subjects. *P < 0.05, ***P < 0.001, Mann-Whitney U test. Data are means per subject.

  • Fig. 8. The MGA allows training of skilled locomotor and postural activities.

    (A) Images and stick diagram decomposition showing a subject with SCI who could position his feet onto the irregularly spaced rungs of a ladder projected onto the floor during locomotion assisted with two crutches. The subject could not walk over the actual ladder in this condition. The MGA enabled the subject to climb up the first staircase and to progress onto the actual ladder without assistive device. (B) Successive positions of the trunk in the coronal plane while a subject with SCI was asked to progress along a curvilinear path projected onto the floor, both without and with MGA. The CoM trajectory and acceleration are also displayed, together with the position of the feet (dashes) during stance and their trajectory during swing. The dot indicates the positions of the crutches onto the floor without MGA. (C) Image sequences showing the behavior of a subject with SCI during the application of postural perturbations during walking. The perturbation is schematized above each sequence. Left: Sudden leftward and then rightward forces. Right: Sustained leftward and then rightward forces. The impact of these perturbations on the position, posture, and trajectory of the trunk and feet are shown.

Supplementary Materials

  • www.sciencetranslationalmedicine.org/cgi/content/full/9/399/eaah3621/DC1

    Materials and Methods

    Fig. S1. Attachment to the robotic support system.

    Fig. S2. Transparent mode of the robotic support system.

    Fig. S3. Representation and processing of kinematic and muscle activity recordings.

    Fig. S4. Impact of upward and forward forces on body kinematics and muscle activity.

    Fig. S5. Interaction between upward and forward forces during locomotion on a treadmill.

    Table S1. List of parameters computed during walking and standing.

    Table S2. Characteristics of the subjects.

    Movie S1. Interaction between upward and forward forces during walking.

    Movie S2. Accuracy of the algorithm to configure the MGA.

    Movie S3. Performance of the MGA to facilitate locomotion after SCI and stroke.

    Movie S4. The MGA improved locomotor performance after a 1-hour training session.

    Movie S5. The MGA enabled skilled locomotion.

    Database S1. Summary of the gait analyses for all the recorded subjects with SCI or stroke, without or with the MGA.

    References (5456)

  • Supplementary Material for:

    A multidirectional gravity-assist algorithm that enhances locomotor control in patients with stroke or spinal cord injury

    Jean-Baptiste Mignardot, Camille G. Le Goff, Rubia van den Brand, Marco Capogrosso, Nicolas Fumeaux, Heike Vallery, Selin Anil, Jessica Lanini, Isabelle Fodor, Grégoire Eberle, Auke Ijspeert, Brigitte Schurch, Armin Curt, Stefano Carda, Jocelyne Bloch, Joachim von Zitzewitz, Grégoire Courtine*

    *Corresponding author. Email: gregoire.courtine{at}epfl.ch

    Published 19 July 2017, Sci. Transl. Med. 9, eaah3621 (2017)
    DOI: 10.1126/scitranslmed.aah3621

    This PDF file includes:

    • Materials and Methods
    • Fig. S1. Attachment to the robotic support system.
    • Fig. S2. Transparent mode of the robotic support system.
    • Fig. S3. Representation and processing of kinematic and muscle activity recordings.
    • Fig. S4. Impact of upward and forward forces on body kinematics and muscle activity.
    • Fig. S5. Interaction between upward and forward forces during locomotion on a treadmill.
    • Table S1. List of parameters computed during walking and standing.
    • Table S2. Characteristics of the subjects.
    • Legends for movies S1 to S5
    • Legend for database S1
    • References (5456)

    [Download PDF]

    Other Supplementary Material for this manuscript includes the following:

    • Movie S1 (.mp4 format). Interaction between upward and forward forces during walking.
    • Movie S2 (.mp4 format). Accuracy of the algorithm to configure the MGA.
    • Movie S3 (.mp4 format). Performance of the MGA to facilitate locomotion after SCI and stroke.
    • Movie S4 (.mp4 format). The MGA improved locomotor performance after a 1-hour training session.
    • Movie S5 (.mp4 format). The MGA enabled skilled locomotion.
    • Database S1 (.pdf format). Summary of the gait analyses for all the recorded subjects with SCI or stroke, without or with the MGA.

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