Liang Jun, Abbott Carmen C, Skubic Marjorie, Keller James
Dartmouth College, Hanover, NH 03755, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6139-42. doi: 10.1109/IEMBS.2009.5334686.
Today, eldercare demands a greater degree of versatility in healthcare. Automatic monitoring devices and sensors are under development to help senior citizens achieve greater autonomy, and, as situations arise, alert healthcare providers. In this paper, we study gait patterns based on extracted silhouettes from image sequences. Three features are investigated through two different image capture perspectives: shoulder level, spinal incline, and silhouette centroid. Through the evaluation of fourteen image sequences representing a range of healthy to frail gait styles, features are extracted and compared to validation results using a Vicon motion capture system. The results obtained show promise for future studies that can increase both the accuracy of feature extraction and pragmatism of machine monitoring for at-risk elders.
如今，老年护理需要医疗保健具备更高的通用性。自动监测设备和传感器正在研发中，以帮助老年人实现更大程度的自主，并在出现情况时提醒医疗保健提供者。在本文中，我们基于从图像序列中提取的轮廓来研究步态模式。通过两种不同的图像捕捉视角研究了三个特征：肩部水平、脊柱倾斜度和轮廓质心。通过对代表一系列从健康到虚弱步态风格的14个图像序列进行评估，提取特征并与使用Vicon运动捕捉系统的验证结果进行比较。所获得的结果为未来的研究带来了希望，这些研究可以提高特征提取的准确性以及对高危老年人进行机器监测的实用性。
