超越模拟基准：真实世界数据稀缺下跌倒检测的运动表示评估

中文摘要 AI 辅助

跌倒对老年人而言是重大健康问题，可穿戴传感器已被广泛用于跌倒检测以实现及时干预。然而，真实世界中的跌倒事件极为罕见：收集100例真实跌倒事件估计需要10万天的监测，导致用于训练机器学习模型的标注数据严重不足。因此，许多方法依赖模拟数据集，常报告在实验室环境下的高性能，但真实世界泛化能力有限。本文针对真实世界数据稀缺场景下可穿戴跌倒检测的运动表示开展系统评估。利用加速度计信号，本文比较了基于区间、基于核、符号以及基础模型的表示方法；作为可解释基线，还研究了一种轻量符号表示，该表示将短运动片段转换为符号句子，并补充基于物理的冲击描述符。实验使用的数据集包括模拟跌倒数据集FallAllD，以及经临床验证的真实跌倒数据集FARSEEING。通过交叉验证、可控数据稀缺设置和跨数据集迁移，本文探究了表示选择如何影响实际部署场景下的鲁棒性。结果表明，高参数化的核模型与基础模型在模拟数据上表现优异，但在数据稀缺和领域偏移时性能会严重下降；尽管基于区间的表示在真实世界数据上的绝对性能最强，但用基于物理的冲击描述符增强符号表示后，其在领域偏移下的性能下降幅度最小，且在极端数据稀缺时仍能保持检测灵敏度，仅精度较低。这些发现凸显了超越模拟基准进行评估的重要性，表明在真实世界数据稀缺的情况下，运动表示的选择对可部署跌倒检测至关重要。

英文摘要

Falls are a major health concern for older adults, and wearable sensors have been widely explored for detecting falls and enabling timely intervention. However, real-world falls are extremely rare: collecting 100 of them requires an estimated 100,000 days of monitoring, resulting in severely limited labelled data for training machine learning models. Consequently, many approaches rely on simulated datasets, often reporting high laboratory performance but limited real-world generalisation. We present a systematic evaluation of motion representations for wearable fall detection under real-world data scarcity. Using accelerometer signals, we compare interval-based, kernel-based, symbolic, and foundation model representations. As an interpretable baseline, we additionally investigate a lightweight symbolic representation that converts short motion segments into symbolic sentences augmented with physically-grounded impact descriptors. Experiments use FallAllD, a simulated falls dataset, and FARSEEING, a clinically verified real-world falls dataset. Through cross-validation, controlled data scarcity, and cross-dataset transfer, we examine how representation choices affect robustness under realistic deployment. Our results reveal that highly parameterised kernel and foundation models excel on simulated data but degrade severely under both data scarcity and domain shift. Although the interval-based representation achieves the strongest absolute real-world performance, augmenting a symbolic representation with physically-grounded impact descriptors yields the smallest degradation under domain shift and retains detection sensitivity under extreme scarcity, albeit at lower precision. These findings highlight the importance of evaluating beyond simulated benchmarks and show that representation choice is critical for deployable fall detection given the scarcity of real-world data.

发表机构

• University College Dublin (UCD)（都柏林大学学院）
• University of Bologna（博洛尼亚大学）
• Robert Bosch Hospital（罗伯特博世医院）
• Heidelberg University Hospital（海德堡大学医院）

机构由 AI 辅助整理，请以论文原文为准。