Problem
Research questions and friction points this paper is trying to address.
Bipedal Locomotion
Ankle Control
Center of Mass
The Strange Attractor Model of Bipedal Locomotion and its Consequences on Motor Control
🤖 AI Summary
Existing bipedal walking models inadequately capture the coordination between ankle control and vertical/lateral center-of-mass (CoM) dynamics during human gait, limiting the efficacy of rehabilitation interventions. To address this, we propose a dynamical systems framework grounded in singular attractor theory. For the first time, we formulate heel-strike and toe-off strategies as closed-form equations embedded within an attractor manifold, replacing conventional joint-space control with task-space planning. Integrating harmonic CoM oscillation modeling, motion-capture-driven virtual subject construction, and biomechanical parameter averaging, our approach enables quantitative analysis of gait self-organization. Validated on experimental data from 12 healthy participants, the model accurately reproduces stance-phase regulation patterns. Crucially, it provides the first dynamical-systems–based empirical validation of both the existence and functional role of attractor mechanisms in human locomotor control.
📝 Abstract
Despite decades of study, many unknowns exist about the mechanisms governing human locomotion. Current models and motor control theories can only partially capture the phenomenon. This may be a major cause of the reduced efficacy of lower limb rehabilitation therapies. Recently, it has been proposed that human locomotion can be planned in the task-space by taking advantage of the gravitational pull acting on the Centre of Mass (CoM) by modelling the attractor dynamics. The model proposed represents the CoM transversal trajectory as a harmonic oscillator propagating on the attractor manifold. However, the vertical trajectory of the CoM, controlled through ankle strategies, has not been accurately captured yet. Research Questions: Is it possible to improve the model accuracy by introducing a mathematical model of the ankle strategies by coordinating the heel-strike and toe-off strategies with the CoM movement? Our solution consists of closed-form equations that plan human-like trajectories for the CoM, the foot swing, and the ankle strategies. We have tested our model by extracting the biomechanics data and postural during locomotion from the motion capture trajectories of 12 healthy subjects at 3 self-selected speeds to generate a virtual subject using our model. Our virtual subject has been based on the average of the collected data. The model output shows our virtual subject has walking trajectories that have their features consistent with our motion capture data. Additionally, it emerged from the data analysis that our model regulates the stance phase of the foot as humans do. The model proves that locomotion can be modelled as an attractor dynamics, proving the existence of a nonlinear map that our nervous system learns. It can support a deeper investigation of locomotion motor control, potentially improving locomotion rehabilitation and assistive technologies.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bipedal Walking Model
Attractor Dynamics
Gait Control
🔎 Similar Papers
No similar papers found.
