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.
Abstract (translated)
衰弱症是老年医学中的一种状态，其特征是生理储备下降和对压力源的易感性增加。然而，在临床实践中，衰弱评估仍具有主观性、异质性且难以规模化。步态是生物衰老的敏感标志，能在明显失能前捕捉多系统衰退。但现代计算机视觉在基于步态的衰弱评估中的应用，一直受限于数据集规模小、类别不平衡以及缺乏临床代表性基准。在本研究中，我们发布了一个在临床真实场景下收集的、基于轮廓的衰弱步态公开数据集，该数据集覆盖了完整的衰弱谱系，并包含了使用助行器的老年人。利用此数据集，我们评估了预训练步态识别模型如何在数据有限条件下适配于衰弱分类任务。我们研究了卷积架构和混合注意力架构，并证明预测性能主要取决于预训练表征的迁移方式，而非单纯依赖架构复杂度。跨模型分析表明，与全参数微调或僵化冻结相比，选择性冻结低层步态表征同时允许高层特征自适应，能获得更稳定且泛化性更好的性能。对类别不平衡的保守处理进一步提升了训练稳定性，而结合互补学习目标则增强了对临床相邻衰弱状态的判别能力。可解释性分析显示，模型持续关注下肢和骨盆区域，这与已确立的衰弱生物力学相关指标一致。综上，这些发现确立了基于步态的表征学习作为一种可规模化、非侵入且可解释的衰弱评估框架，并支持将现代生物特征建模方法整合到衰老研究和临床实践中。
URL
https://arxiv.org/abs/2603.24434
