# Understanding the Representation of Older Adults in Motion Capture Locomotion Datasets

arXiv: https://arxiv.org/abs/2511.11713 · HTML: https://mlsanity.com/paper/2511.11713/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 58 references that are in the map.
- First posted on arXiv 12 Nov 2025.

## Builds on (most cited first, 14 of 14)

- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [Robust Motion In-betweening](https://mlsanity.com/paper/2102.04942/) (2021)
- [Unpaired Motion Style Transfer from Video to Animation](https://mlsanity.com/paper/2005.05751/) (2020)
- [Real-Time Style Modelling of Human Locomotion via Feature-Wise Transformations and Local Motion Phases](https://mlsanity.com/paper/2201.04439/) (2022)
- [MoVi: A Large Multipurpose Motion and Video Dataset](https://mlsanity.com/paper/2003.01888/) (2020)
- [HUMOTO: A 4D Dataset of Mocap Human Object Interactions](https://mlsanity.com/paper/2504.10414/) (2025)
- [Explaining the Unique Nature of Individual Gait Patterns with Deep Learning](https://mlsanity.com/paper/1808.04308/) (2018)
- [UnderPressure: Deep Learning for Foot Contact Detection, Ground Reaction Force Estimation and Footskate Cleanup](https://mlsanity.com/paper/2208.04598/) (2022)
- [GroundLink: A Dataset Unifying Human Body Movement and Ground Reaction Dynamics](https://mlsanity.com/paper/2310.03930/) (2023)
- [Polarization Human Shape and Pose Dataset](https://mlsanity.com/paper/2004.14899/) (2020)
- [Lower-limb kinematics and kinetics during continuously varying human locomotion](https://mlsanity.com/paper/2108.12307/) (2021)
- [CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator](https://mlsanity.com/paper/2202.10562/) (2022)
- [SUPER: Seated Upper Body Pose Estimation using mmWave Radars](https://mlsanity.com/paper/2407.02455/) (2024)
- [PA-HOI: A Physics-Aware Human and Object Interaction Dataset](https://mlsanity.com/paper/2508.06205/) (2025)

## Abstract (arXiv)

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.