A Motion Capture Dataset on Human Sitting to Walking Transitions

Area of Science:

• Biomechanics
• Human Movement Analysis
• Gerontology

Background:

• Sit-to-walk (STW) is essential for daily living, but existing datasets are limited.
• Understanding age-related changes in STW is crucial for mobility and independence.
• A larger, multi-modal dataset is needed to establish reliable benchmarks.

Purpose of the Study:

• To present a novel, comprehensive dataset of sit-to-walk (STW) biomechanics.
• To capture detailed lower body movement, muscle activity, and functional outcomes.
• To enable cross-sectional analysis of STW across different age groups.

Main Methods:

• Collected data from 65 healthy adults across young, middle, and older age groups.
• Utilized motion capture, ground reaction force, EMG, and IMU sensors.
• Administered the Knee Injury and Osteoarthritis Outcome Score (KOOS) survey.

Main Results:

• Achieved good test-retest reliability (ICC > 0.74) for lower body joint angles.
• STW joint angle trajectories and maximum voluntary contractions align with existing literature.
• Established a dataset comparable to current research standards.

Conclusions:

• This dataset provides a valuable resource for studying age-related STW biomechanics.
• It serves as a benchmark for assessing neuromusculoskeletal impairments.
• Facilitates the design and development of assistive technologies for rehabilitation.