The Role of Multijoint Torques in Standing Balance Recovery

Academic Background

Standing balance is an essential ability in human daily life, especially when facing external perturbations. How to quickly coordinate torques at the hip, knee, and ankle joints to maintain balance has long been a critical topic in motor control and neuroscience research. Traditional views suggest that balance recovery relies on the synergistic action of neural-mediated feedforward and feedback mechanisms. The feedforward mechanism provides instantaneous mechanical feedback through muscle short-range stiffness, while the feedback mechanism activates muscles through sensory input, generating delayed joint torques. However, the specific contributions of feedforward and feedback mechanisms to balance recovery remain unclear. To address this issue, researchers developed a novel Sensorimotor Response Model (SRM) aimed at decomposing the torque responses of the hip, knee, and ankle joints during balance recovery and distinguishing the contributions of feedforward and feedback mechanisms.

Source of the Paper

This paper was co-authored by Kristen L. Jakubowski, Giovanni Martino, Owen N. Beck, Gregory S. Sawicki, and Lena H. Ting. The authors are affiliated with Emory University, University of Padova, University of Texas at Austin, Georgia Institute of Technology, and the Department of Rehabilitation Medicine at Emory University. The study was first published on December 11, 2024, in the Journal of Neurophysiology, titled Center of Mass States Render Multijoint Torques Throughout Standing Balance Recovery.

Research Process

1. Study Design and Participants

The study recruited eight healthy young adults (4 females, 4 males, average age 25 years), all of whom had no history of neurological or musculoskeletal disorders. The primary goal of the study was to simulate standing balance perturbations by translating the support surface backward and analyze the torque responses of the hip, knee, and ankle joints under different perturbation magnitudes.

2. Data Collection

Participants stood on a custom platform capable of performing ramp-and-hold translations. Two independent force plates embedded in the platform were used to collect ground reaction forces. Participants wore 33 markers based on the Vicon Plug-in Gait model for motion capture. Additionally, surface electromyography (EMG) data from the left leg, including the medial gastrocnemius, soleus, tibialis anterior, rectus femoris, vastus medialis, biceps femoris, and gluteus medius, were collected.

3. Perturbation Experiments

To determine each participant’s step threshold, researchers first quantified their balance capacity by translating the platform backward. The step threshold was defined as the maximum translation magnitude at which participants could maintain balance without taking corrective steps or using a safety harness. Participants then completed 40 ramp-and-hold perturbation trials with magnitudes of 12 cm and 75%, 85%, and 95% of their step threshold. To reduce anticipatory responses, forward perturbation “catch trials” were randomly interspersed.

4. Data Processing

Researchers used the inverse dynamics toolbox in OpenSim to calculate torques at the hip, knee, and ankle joints. Center of mass (COM) acceleration, velocity, and displacement were computed using motion capture and force plate data. EMG signals were processed with high-pass filtering, rectification, and low-pass filtering for muscle activation analysis.

5. Sensorimotor Response Model (SRM)

The researchers modified the original EMG-SRM model to develop a torque-SRM model. This model reconstructs joint torques through parallel feedback loops, each with independent delays and gains. The input to the model was COM kinematics, and the output was joint torques. By optimizing the gains and delays of each loop, the researchers were able to decompose the contributions of feedforward and feedback mechanisms to joint torques.

Key Findings

1. Accuracy of the Torque-SRM Model

The torque-SRM model accurately reconstructed the torque responses of the hip, knee, and ankle joints under different perturbation magnitudes. The model’s goodness-of-fit (R²) and variance accounted for (VAF) were both above 0.84, indicating that the model effectively captured the time course and magnitude changes of joint torques.

2. Contributions of Feedforward and Feedback Mechanisms

The study found that the torque responses of the hip and knee joints included both feedforward and feedback components, while the ankle joint’s torque response was driven solely by feedback mechanisms. The feedforward components of the hip and knee joints manifested as instantaneous mechanical feedback, whereas the ankle joint’s feedforward component was significantly attenuated due to the compliance of the Achilles tendon.

3. Effect of Perturbation Magnitude on Feedback Gains

As the perturbation magnitude increased, the feedforward gains of the hip and knee joints significantly increased, while the feedback gains of the ankle joint showed saturation. This suggests that the feedforward mechanisms of the hip and knee joints can adjust according to perturbation magnitude, whereas the feedback mechanisms of the ankle joint may reach their limits under larger perturbations.

Conclusions and Significance

By developing the torque-SRM model, this study successfully decomposed the feedforward and feedback torque responses of the hip, knee, and ankle joints during standing balance recovery for the first time. The findings reveal that while COM kinematics drive the torque responses of all joints, there are significant differences in the feedforward and feedback mechanisms across joints. This not only deepens our understanding of balance control mechanisms but also provides a new framework for assessing balance impairments in older adults, individuals with neuromuscular diseases, or those with injuries.

Furthermore, the model and methods from this study can be applied to robotics and wearable device development. By simulating physiological balance recovery mechanisms, future robots or exoskeletons can better respond to external perturbations, enhancing users’ balance capabilities.

Research Highlights

1. Innovative Model: Researchers developed the torque-SRM model, successfully decomposing the feedforward and feedback components of multijoint torque responses for the first time.
2. Joint-Specific Mechanisms: The study found that feedforward mechanisms are significant in the hip and knee joints, while feedback mechanisms dominate in the ankle joint, revealing the unique roles of different joints in balance control.
3. Application Potential: This research provides new theoretical support for robotics and rehabilitation medicine, with potential applications in developing smarter balance-assistive devices.