Understanding the Representation of Older Adults in Motion Capture Locomotion Datasets

TL;DR

This paper surveys existing motion capture datasets to evaluate how well they represent older adults' locomotion, revealing a scarcity of accurate older adult data and proposing metrics to assess motion fidelity.

Contribution

It identifies gaps in older adult representation in MoCap datasets and introduces quantitative metrics to evaluate the fidelity of older adult walking motions.

Findings

Older adults are underrepresented in MoCap datasets.

Old-style walking motions often lack age-specific characteristics.

Proposed metrics can assess the quality of older adult motion data.

Abstract

The Internet of Things (IoT) sensors have been widely employed to capture human locomotions to enable applications such as activity recognition, human pose estimation, and fall detection. Motion capture (MoCap) systems are frequently used to generate ground truth annotations for human poses when training models with data from wearable or ambient sensors, and have been shown to be effective to synthesize data in these modalities. However, the representation of older adults, an increasingly important demographic in healthcare, in existing MoCap locomotion datasets has not been thoroughly examined. This work surveyed 41 publicly available datasets, identifying eight that include older adult motions and four that contain motions performed by younger actors annotated as old style. Older adults represent a small portion of participants overall, and few datasets provide full-body motion data for this group. To assess the fidelity of old-style walking motions, quantitative metrics are introduced, defining high fidelity as the ability to capture age-related differences relative to normative walking. Using gait parameters that are age-sensitive, robust to noise, and resilient to data scarcity, we found that old-style walking motions often exhibit overly controlled patterns and fail to faithfully characterize aging. These findings highlight the need for improved representation of older adults in motion datasets and establish a method to quantitatively evaluate the quality of old-style walking motions.