Bianchi Federico, Redmond Stephen J, Narayanan Michael R, Cerutti Sergio, Celler Branko G, Lovell Nigel H
Department of Biomedical Engineering, Politecnico di Milano, Milano, Italy.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6111-4. doi: 10.1109/IEMBS.2009.5334922.
A falls detection system, employing a Bluetooth-based wearable device, containing a triaxial accelerometer and a barometric pressure sensor, is described. The aim of this study is to evaluate the use of barometric pressure measurement, as a surrogate measure of altitude, to augment previously reported accelerometry-based falls detection algorithms. The accelerometry and barometric pressure signals obtained from the waist-mounted device are analyzed by a signal processing and classification algorithm to discriminate falls from activities of daily living. This falls detection algorithm has been compared to two existing algorithms which utilize accelerometry signals alone. A set of laboratory-based simulated falls, along with other tasks associated with activities of daily living (16 tests) were performed by 15 healthy volunteers (9 male and 6 female; age: 23.7 +/- 2.9 years; height: 1.74 +/- 0.11 m). The algorithm incorporating pressure information detected falls with the highest sensitivity (97.8%) and the highest specificity (96.7%).
本文描述了一种跌倒检测系统，该系统采用基于蓝牙的可穿戴设备，其中包含一个三轴加速度计和一个气压传感器。本研究的目的是评估将气压测量作为海拔高度的替代测量方法，以增强先前报道的基于加速度计的跌倒检测算法。从腰部佩戴设备获取的加速度计和气压信号通过信号处理和分类算法进行分析，以区分跌倒和日常生活活动。该跌倒检测算法已与另外两种仅利用加速度计信号的现有算法进行了比较。15名健康志愿者（9名男性和6名女性；年龄：23.7±2.9岁；身高：1.74±0.11米）进行了一组基于实验室的模拟跌倒以及与日常生活活动相关的其他任务（16项测试）。结合压力信息的算法检测跌倒的灵敏度最高（97.8%），特异性也最高（96.7%）。
