Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig - Institute of Technology and Hannover Medical School, Muehlenpfordtstrasse 23, D-38106 Braunschweig, Germany.
Inform Health Soc Care. 2009 Dec;34(4):181-8. doi: 10.3109/17538150903356275.
Falls have various causes and are often associated with mobility impairments. Preventive steps to avoid falls may be initiated, if an increasing fall risk could be detected in time. The objective of this article is to identify an automated sensor-based method to determine fall risk of patients based on objectively measured gait parameters. One hundred fifty-one healthy subjects and 90 subjects at risk of falling were measured during a Timed 'Up & Go' test with a single triaxial acceleration sensor worn on a waist belt. The fall risk was assessed using the STRATIFY score. A decision tree induction algorithm was used to distinguish between subjects with high and low risk using the determined gait parameters. The results of the risk classification produce an overall accuracy of 90.4% in relation to STRATIFY score. The sensitivity amount to 89.4%, the specificity to 91.0% and the reliability parameter kappa equals 0.79. The method presented is able to distinguish between subjects with high and low fall risk. It is unobtrusive and therefore may be applied over extended time periods. A subsequent study is needed to confirm the model's suitability for data recorded in patients' everyday lives.
跌倒有多种原因，通常与行动障碍有关。如果能及时发现跌倒风险增加，可以采取预防措施避免跌倒。本文的目的是确定一种基于自动化传感器的方法，根据客观测量的步态参数来确定患者的跌倒风险。使用佩戴在腰带上的单个三轴加速度传感器，对 151 名健康受试者和 90 名跌倒风险受试者进行了定时“站起和行走”测试。使用 STRATIFY 评分评估跌倒风险。使用决策树归纳算法，根据确定的步态参数区分高风险和低风险受试者。风险分类的结果与 STRATIFY 评分相关，总体准确率为 90.4%。灵敏度为 89.4%，特异性为 91.0%，可靠性参数kappa 等于 0.79。所提出的方法能够区分高风险和低风险的受试者。它不引人注意，因此可以在较长时间内使用。需要进一步的研究来确认该模型是否适合记录患者日常生活中的数据。
