The Gait Signature of Frailty: Transfer Learning based Deep Gait Models for Scalable Frailty Assessment

TL;DR

This study develops and evaluates deep learning models using gait silhouette data to assess frailty in older adults, demonstrating the importance of transfer learning, model interpretability, and handling class imbalance.

Contribution

It introduces a publicly available gait dataset for frailty assessment and systematically analyzes transfer learning strategies for scalable, non-invasive frailty classification.

Findings

Pretrained gait models can be effectively adapted for frailty classification.

Selective freezing of model layers improves performance and stability.

Model attention aligns with biomechanical markers of frailty.

Abstract

Frailty is a condition in aging medicine characterized by diminished physiological reserve and increased vulnerability to stressors. However, frailty assessment remains subjective, heterogeneous, and difficult to scale in clinical practice. Gait is a sensitive marker of biological aging, capturing multisystem decline before overt disability. Yet the application of modern computer vision to gait-based frailty assessment has been limited by small, imbalanced datasets and a lack of clinically representative benchmarks. In this work, we introduce a publicly available silhouette-based frailty gait dataset collected in a clinically realistic setting, spanning the full frailty spectrum and including older adults who use walking aids. Using this dataset, we evaluate how pretrained gait recognition models can be adapted for frailty classification under limited data conditions. We study both convolutional and hybrid attention-based architectures and show that predictive performance depends primarily on how pretrained representations are transferred rather than architectural complexity alone. Across models, selectively freezing low-level gait representations while allowing higher-level features to adapt yields more stable and generalizable performance than either full fine-tuning or rigid freezing. Conservative handling of class imbalance further improves training stability, and combining complementary learning objectives enhances discrimination between clinically adjacent frailty states. Interpretability analyses reveal consistent model attention to lower-limb and pelvic regions, aligning with established biomechanical correlates of frailty. Together, these findings establish gait-based representation learning as a scalable, non-invasive, and interpretable framework for frailty assessment and support the integration of modern biometric modeling approaches into aging research and clinical practice.