Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200, University Ave. W, Waterloo, Canada.
Department of Computer & Information Sciences, Northumbria University, 2 Ellison Pl, Newcastle upon Tyne, UK.
Gait Posture. 2021 Mar;85:178-190. doi: 10.1016/j.gaitpost.2020.04.010. Epub 2020 May 28.
Despite advances in laboratory-based supervised fall risk assessment methods (FRAs), falls still remain a major public health problem. This can be due to the alteration of behavior in laboratory due to the awareness of being observed (i.e., Hawthorne effect), the multifactorial complex etiology of falls, and our limited understanding of human behaviour in natural environments, or in the' wild'. To address these imitations, a growing body of literature has focused on free-living wearable-sensor-based FRAs. The objective of this narrative literature review is to discuss papers investigating natural data collected by wearable sensors for a duration of at least 24 h to identify fall-prone older adults.
Databases (Scopus, PubMed and Google Scholar) were searched for studies based on a rigorous search strategy.
Twenty-four journal papers were selected, in which inertial sensors were the only wearable system employed for FRA in the wild. Gait was the most-investigated activity; but sitting, standing, lying, transitions and gait events, such as turns and missteps, were also explored. A multitude of free-living fall predictors (FLFPs), e.g., the quantity of daily steps, were extracted from activity bouts and events. FLFPs were further categorized into discrete domains (e.g., pace, complexity) defined by conceptual or data-driven models. Heterogeneity was found within the reviewed studies, which includes variance in: terminology (e.g., quantity vs macro), hyperparameters to define/estimate FLFPs, models and domains, and data processing approaches (e.g., the cut-off thresholds to define an ambulatory bout). These inconsistencies led to different results for similar FLFPs, limiting the ability to interpret and compare the evidence.
Free-living FRA is a promising avenue for fall prevention. Achieving a harmonized model is necessary to systematically address the inconsistencies in the field and identify FLFPs with the highest predictive values for falls to eventually address intervention programs and fall prevention.
尽管基于实验室的监督性跌倒风险评估方法（FRAs）取得了进展，但跌倒仍然是一个主要的公共卫生问题。这可能是由于在实验室中由于被观察的意识而改变了行为（即霍桑效应）、跌倒的多因素复杂病因以及我们对自然环境或“野外”中人类行为的理解有限。为了解决这些限制，越来越多的文献集中在自由生活的基于可穿戴传感器的 FRAs 上。本叙述性文献综述的目的是讨论研究自然数据的论文，这些数据是通过可穿戴传感器收集的，持续时间至少为 24 小时，以识别易跌倒的老年人。
根据严格的搜索策略，在数据库（Scopus、PubMed 和 Google Scholar）中搜索研究。
选择了 24 篇期刊论文，其中仅惯性传感器被用于野外的 FRA。步态是研究最多的活动；但也探索了坐姿、站立、躺卧、转换和步态事件，如转弯和失误。从活动回合和事件中提取了大量的自由生活跌倒预测因子（FLFPs），例如每日步数。FLFPs 进一步分为离散域（例如，步伐、复杂性），由概念或数据驱动模型定义。在综述研究中发现存在异质性，包括术语（例如，数量与宏观）、定义/估计 FLFPs 的超参数、模型和域以及数据处理方法（例如，定义非卧床回合的截止阈值）的差异。这些不一致导致类似的 FLFPs 产生不同的结果，限制了对证据的解释和比较能力。
自由生活 FRA 是预防跌倒的有前途的途径。实现协调一致的模型对于系统地解决该领域的不一致性以及确定对跌倒具有最高预测值的 FLFPs 是必要的，最终可以解决干预计划和跌倒预防问题。
