Gastrocnemius Myoelectric Control of a Robotic Hip Exoskeleton Can Reduce the User's Lower-Limb Muscle Activities at Push Off

Abstract

We present a novel assistive control strategy for a robotic hip exoskeleton for assisting hip flexion/extension, based on a proportional Electromyography (EMG) strategy. The novelty of the proposed controller relies on the use of the Gastrocnemius Medialis (GM) EMG signal instead of a hip flexor muscle, to control the hip flexion torque. This strategy has two main advantages: first, avoiding the placement of the EMG electrodes at the human-robot interface can reduce discomfort issues for the user and motion artifacts of the recorded signals; second, using a powerful signal for control, such as the GM, could improve the reliability of the control system. The control strategy has been tested on eight healthy subjects, walking with the robotic hip exoskeleton on the treadmill. We evaluated the controller performance and the effect of the assistance on muscle activities. The tuning of the assistance timing in the controller was subject dependent and varied across subjects. Two muscles could benefit more from the assistive strategy, namely the Rectus Femoris (directly assisted) and the Tibialis Anterior (indirectly assisted). A significant correlation was found between the timing of the delivered assistance (i.e., synchronism with the biological hip torque), and reduction of the hip flexors muscular activity during walking; instead, no significant correlations were found for peak torque and peak power. Results suggest that the timing of the assistance is the most significant parameter influencing the effectiveness of the control strategy. The findings of this work could be important for future studies aimed at developing assistive strategies for walking assistance exoskeletons.

Keywords: EMG control; exoskeleton; gait; hip orthosis; walking assistance.

Figure 1

Overview of the Active Pelvis Orthosis. (A) Lateral view. (B) Frontal view. (C) During treadmill walking. Instrumented shoes and EMG electrodes are also shown.

Figure 2

(A) Sub-modules of the experimental apparatus. (B) Architecture of the assistive controller. The inputs for the high-level control layer are the left vGRF, the CoP and the raw EMG signals of left and right GM muscles. The Gait Phase Estimator block computes the EGP. The Assistive Torque Generator block computes the LE. Each LE is then multiplied by k and delayed by Δt. The outputs of the high-level control layer are the torque reference control signals for the low-level closed-loop controller. (C) EGP, vGRF, CoP, hip joint angle, and GM EMG signals for a few steps. Blue lines represent data from the left side, green lines represent data from the right side. (D) Parameters extracted.

Figure 3

Comparisons of the APO kinematic and dynamic variables between TM, AM₅₀, AM₁₀₀, and AM₁₅₀ conditions. (A) Hip joint angles, delivered torque and mechanical power profiles for one representative subject; variables are plotted as median values and interquartile range contours of left hip joint. (B) Maximum and minimum hip flexion angle, torque peak, and power peak, averaged over all subjects: mean values ± SE. Asterisks denote p < 0.05.

Figure 4

EMG data from left leg muscles (TA, RF, GM, ST) for the last minute of each tested condition. (A) EMG LE activation profiles for one representative subject. Profiles are shown as median values and interquartile range contours. (B) EMG peaks averaged over all subjects. Mean values ± SE are shown. Data are normalized for the IBL activation peak.

Figure 5

Pearson correlation analysis between Δφ and the percentage variation of RF EMGs for all assistance levels. Pearson correlation coefficient r and the level of significance p, are reported for each condition. Percentage variations are calculated for all AM conditions compared to TM. Colors are different participants. Red regions represent EMG activity increase, green regions represent EMG activity reduction. Orange lines indicate linear fit.