Effects of Altered Haptic Feedback Gain on Balance and Its Neural Mechanisms

Background

In daily life, human balance control relies on multiple sensory inputs, including vision, vestibular sensation, and proprioception. Haptic feedback also plays a crucial role in balance control, especially when touching a fixed object, as it significantly reduces postural sway. However, how the gain of haptic feedback affects balance control and how the central nervous system (CNS) processes these feedback signals remain incompletely understood. To explore this issue, researchers designed an experiment to artificially modulate haptic feedback gain, study its impact on balance control, and uncover the underlying neural mechanisms.

This study was conducted by Raymond F. Reynolds, Craig P. Smith, and Lorenz Assländer from the School of Sport, Exercise & Rehabilitation Sciences at the University of Birmingham, UK, and the Human Performance Research Centre at the University of Konstanz, Germany. The findings were published in 2025 in the European Journal of Neuroscience.

Research Process

Experimental Design and Participants

The study recruited 14 healthy volunteers (mean age 23 years, 7 females), all of whom provided informed consent. The experiment was approved by the ethics committee of the University of Birmingham. Participants were asked to stand barefoot with their eyes closed, lightly gripping a robotic manipulandum. The movement of the manipulandum was synchronized with the participants’ body sway, and the researchers systematically altered the gain of the robot’s motion to change the intensity of haptic feedback. The gain ranged from -2 to +2, where 0 indicated a stationary robot, positive gain meant the robot moved in the same direction as body sway, and negative gain meant the opposite direction.

Experimental Setup and Data Collection

Participants stood on a force platform, with the robotic manipulandum positioned 400 mm in front of the ankle, 200 mm lateral to the body midline, and 1100 mm in height. The manipulandum was equipped with a triaxial force sensor to measure the interaction force between the hand and the manipulandum. Body sway was recorded in real-time using a motion tracking device (Fastrak, Polhemus, USA), with data acquired at 120 Hz and used to control the robot’s movement. Each gain condition was repeated five times, with each trial lasting 50 seconds. The first 10 seconds of data were discarded to avoid the initial adaptation phase.

Data Analysis and Model Construction

The researchers analyzed the amplitude and velocity of body sway using power spectral density (PSD) and calculated the cross-correlation between hand force and body position. To explain the experimental results, they constructed a feedback control model to simulate body sway behavior under different haptic feedback gains. The model included two main feedback loops: one based on spatial reference and the other on haptic reference. A “conflict estimator” was also introduced to distinguish between haptic feedback caused by body motion and that caused by external object motion.

Key Findings

Effects of Haptic Feedback Gain on Body Sway

The experimental results showed that haptic feedback gain significantly affected body sway. When the gain was 0 (robot stationary), body sway was significantly reduced by 55%-62% compared to the no-contact condition. At negative gains, body sway remained low, with the smallest sway observed at gains of -0.25 and -0.5. However, at positive gains, the effectiveness of haptic feedback diminished, and at a gain of +2, body sway increased significantly, even exceeding that of the no-contact condition.

Relationship Between Hand Force and Body Position

The researchers found that the cross-correlation between hand force and body position was higher at negative gains, indicating better quality haptic feedback. As the gain increased, the cross-correlation decreased, particularly at gains of +1 and +2, where it dropped significantly. This suggests that the quality of haptic feedback is closely related to the control of body sway.

Model Validation

The feedback control model successfully replicated the observed body sway behavior. The model results showed that body sway was minimal at negative gains and increased at positive gains, consistent with the experimental data, further validating the model’s accuracy.

Conclusions and Significance

This study demonstrates that the CNS can utilize artificially modulated haptic feedback gain to enhance balance control, but only when the gain changes are relatively small. Larger changes in gain can negatively impact balance. The research also highlights the importance of haptic feedback in balance control, particularly when the feedback quality is high, as it effectively reduces body sway.

The scientific value of this study lies in revealing the non-linear effects of haptic feedback gain on balance control and providing a theoretical basis for developing balance-assistive devices based on haptic feedback. In the future, this research may play a significant role in balance rehabilitation for the elderly or patients with neurological disorders.

Research Highlights

1. Innovative Experimental Design: By modulating the motion gain of a robotic manipulandum, the study systematically investigated the effects of haptic feedback gain on balance control.
2. Feedback Control Model: The introduction of a “conflict estimator” in the feedback control model successfully explained the experimental data, revealing how the CNS processes haptic feedback signals.
3. Application Potential: The findings provide theoretical support for developing balance-assistive devices based on haptic feedback, with potential applications in rehabilitation for the elderly and patients with neurological disorders.

Additional Valuable Information

The researchers also discussed the potential impact of haptic feedback delay on the experimental results. Although there was a 60 ms delay in the experiment, its effect on the results was minimal. Future studies could further explore the effects of different delay times on haptic feedback effectiveness.

This study not only deepens our understanding of the role of haptic feedback in balance control but also provides important theoretical foundations for future clinical applications and technological developments.