Throughout the world, the proportion of the elders in the total population is increasing dramatically, and home-based care has become the most important form of old-age care. Falling is the most common cause of accidents among the elders at home that poses a huge threat to their health and lives. In order to protect the privacy of the elders an accidental falling detection algorithm for the elders in the home has been proposed in this paper. First, contour-based infrared motion video images are used instead of high-definition cameras to collect the elderly behaviors to protect their privacy. Second, ellipse fitting is performed on the infrared video images of the five behaviors including standing, sitting, squatting, bending and falling. The five geometric characteristic variables of the contour-fitting ellipses including the number of ellipses, centroid positions, ellipsoidal areas, horizontal inclinations and long-short axis ratios of the images, have been extracted. Next, an LSTM model is established using the above variables as inputs for feature extraction and classification. Finally, infrared video images of different types of active behaviors of the elders aged from 50 to 70 years have been selected as IFD database for classification detection. Sixty percent of the IFD images are used as training datasets, and 40% of the IFD images are used as test datasets, and compared with the classification detection of URFD datasets which contains optical RGB HD video images of the different behaviors. The experimental results show the effectiveness of the algorithm proposed in this paper which combines the contour ellipse fitting of the infrared video images and the LSTM feature extraction. The average correct classification rate of the normal and falling down behaviors of the elders is above 95%, which is comparable to the optical RGB datasets. The precision of behavior recognition can effectively protect the privacy of the elders, and provide protection for the accidental falling detection of the elders living alone.

中文翻译：

---

基于老年人行为红外视频图像轮廓椭圆拟合的跌倒检测研究

在世界范围内，老年人在总人口中的比例正在急剧上升，居家养老已成为最重要的养老形式。跌倒是家中长者发生意外的最常见原因，对他们的健康和生命构成巨大威胁。为了保护长者的隐私，本文提出了一种针对家中长者的意外跌倒检测算法。首先，使用基于轮廓的红外运动视频图像代替高清摄像机来收集老年人的行为以保护他们的隐私。其次，对站立、坐、蹲、弯、跌五种行为的红外视频图像进行椭圆拟合。轮廓拟合椭圆的五个几何特征变量，包括椭圆的个数，提取了图像的质心位置、椭球面积、水平倾角和长短轴比。接下来，使用上述变量作为特征提取和分类的输入，建立一个 LSTM 模型。最后选取50~70岁老年人不同类型主动行为的红外视频图像作为IFD数据库进行分类检测。60%的IFD图像作为训练数据集，40%的IFD图像作为测试数据集，并与包含不同行为的光学RGB高清视频图像的URFD数据集进行分类检测。实验结果表明本文提出的将红外视频图像轮廓椭圆拟合与LSTM特征提取相结合的算法的有效性。老年人正常和跌倒行为的平均正确分类率在95%以上，与光学RGB数据集相当。行为识别精准，有效保护长者隐私，为独居长者意外跌倒检测提供保障。