Stone Erik E, Skubic Marjorie
Center for Eldercare and Rehabilitation, Technology, Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO 65211, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5106-9. doi: 10.1109/EMBC.2012.6347142.
Results are presented for measuring the gait parameters of walking speed, stride time, and stride length of five older adults continuously, in their homes, over a four month period. The gait parameters were measured passively, using an inexpensive, environmentally mounted depth camera, the Microsoft Kinect. Research has indicated the importance of measuring a person's gait for a variety of purposes from fall risk assessment to early detection of health problems such as cognitive impairment. However, such assessments are often done infrequently and most current technologies are not suitable for continuous, long term use. For this work, a single Microsoft Kinect sensor was deployed in four apartments, containing a total of five residents. A methodology for generating trends in walking speed, stride time, and stride length based on data from identified walking sequences in the home is presented, along with trend estimates for the five participants who were monitored for this work.
研究结果展示了在四个月的时间里,对五名老年人在家中持续测量步行速度、步幅时间和步幅长度等步态参数的情况。步态参数是使用一台价格低廉、安装在环境中的深度摄像头——微软Kinect被动测量的。研究表明,出于从跌倒风险评估到早期发现认知障碍等健康问题等各种目的,测量一个人的步态非常重要。然而,此类评估往往很少进行,并且大多数现有技术都不适合连续、长期使用。在这项研究中,一个微软Kinect传感器被部署在四套公寓中,这些公寓共有五名住户。本文介绍了一种基于家中识别出的步行序列数据生成步行速度、步幅时间和步幅长度趋势的方法,以及对参与这项研究监测的五名参与者的趋势估计。