Bourke A K, Klenk J, Schwickert L, Aminian K, Ihlen E A F, Helbostad J L, Chiari L, Becker C
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:5183-6. doi: 10.1109/EMBC.2015.7319559.
Automatic fall detection will reduce the consequences of falls in the elderly and promote independent living, ensuring people can confidently live safely at home. Inertial sensor technology can distinguish falls from normal activities. However, <;7% of studies have used fall data recorded from elderly people in real life. The FARSEEING project has compiled a database of real life falls from elderly people, to gain new knowledge about fall events. We have extracted temporal and kinematic parameters to further improve the development of fall detection algorithms. A total of 100 real-world falls were analysed. Subjects with a known fall history were recruited, inertial sensors were attached to L5 and a fall report, following a fall, was used to extract the fall signal. This data-set was examined, and variables were extracted that include upper and lower impact peak values, posture angle change during the fall and time of occurrence. These extracted parameters, can be used to inform the design of fall-detection algorithms for real-world falls detection in the elderly.
自动跌倒检测将减少老年人跌倒的后果，并促进独立生活，确保人们能够自信地在家中安全生活。惯性传感器技术可以区分跌倒与正常活动。然而，不到7%的研究使用了从老年人现实生活中记录的跌倒数据。远见项目汇编了一个老年人现实生活跌倒的数据库，以获取有关跌倒事件的新知识。我们提取了时间和运动学参数，以进一步改进跌倒检测算法的开发。总共分析了100次现实世界中的跌倒情况。招募了有已知跌倒史的受试者，将惯性传感器连接到L5，并在跌倒后使用跌倒报告来提取跌倒信号。对该数据集进行了检查，并提取了包括上下冲击峰值、跌倒期间的姿势角度变化和发生时间等变量。这些提取的参数可用于为老年人现实世界跌倒检测的跌倒检测算法设计提供信息。
