Comparison of toe clearance characteristics between simulated obstacle crossing using visual height cues and actual obstacle crossing

Description

Research Hypothesis The study initially hypothesized that a simplified simulated obstacle-crossing environment, utilizing floor-based visual height cues, would demonstrate high biomechanical fidelity by evoking gait patterns and obstacle-negotiation strategies similar to those observed in actual environments. Data Gathering and Methodology Trial-by-trial kinematic data were collected from healthy participants performing obstacle-crossing tasks. Experiment 1 investigated the influence of target heights (3–12 cm) and walking paths (straight vs. curved) on gait patterns. Experiment 2 compared performance between actual obstacle negotiation and a risk-free simulated environment using four height levels relative to the participant's leg length.The primary outcome measures include Minimum Toe Clearance (TCmin), Maximum Toe Clearance (TCmax), Approach Distance (AD), and Quasicoefficient of Variation (QCV). Key Findings and Data Representation Contrary to the initial hypothesis, the dataset reveals a significant dissociation between motor intention and execution precision. While participants successfully scaled their maximum limb elevation (TCmax) in response to obstacle height in the simulated environment, they systematically underestimated their TCmin, leading to reduced safety margins. Additionally, the data show increased motor variability (QCV) in the trail limb during simulated crossing compared to actual crossing. Interpretation and Data Use These results suggest that while simulation can evoke overall motor intention (gross scaling), the absence of physical risk and task-relevant sensory feedback leads the central nervous system to prioritize effort economy over the precise, fine-tuning of foot trajectories. This dataset is valuable for researchers investigating perception–action coupling and for those developing evidence-based virtual training protocols for fall prevention.

Steps to reproduce

1. Setup and Instrumentation System: 3D motion capture (e.g., Vicon) at 100 Hz. Markers: Reflective markers placed on the lower limbs, specifically the hallux. Obstacles: Physical obstacles (Actual) and floor-based visual cues (Simulated) scaled to participants' leg length. 2. Experimental Protocol Exp 1: Obstacle crossing at four heights (3–12 cm) on straight and curved paths. Exp 2: Comparative tasks between Actual and Simulated environments at four relative heights (4–16% leg length). Task: Participants performed multiple trials of obstacle negotiation for each condition. 3. Data Processing Kinematic Parameters: Calculated TCmin (minimum clearance), TCmax (maximum elevation), and AD (approach distance) for each crossing. Variability: Calculated the Quasicoefficient of Variation (QCV) to assess motor variability. 4. Statistical Analysis Software: Analyzed the trial-by-trial dataset using R. Model: Employed Linear Mixed Models (LMMs) to evaluate the effects of height, condition, and walking path on the kinematic outcomes.

Institutions

• Kanagawa Kenritsu Hoken Fukushi Daigaku

  Kanagawa, Yokosuka

Categories

Funders

• Japan Society for the Promotion of Science

  Ministry of Education, Culture, Sports, Science and Technology

  Tokyo, Japan

  Grant ID: 21K111171

Licence