Abstract
Falls have become more frequent in recent years, which has been harmful for senior citizens.Therefore detecting falls have become important and several data sets and machine learning model have been introduced related to fall detection. In this project report, a human fall detection method is proposed using a multi modality approach. We used the UP-FALL detection data set which is collected by dozens of volunteers using different sensors and two cameras. We use wrist sensor with acclerometer data keeping labels to binary classification, namely fall and no fall from the data set.We used fusion of camera and sensor data to increase performance. The experimental results shows that using only wrist data as compared to multi sensor for binary classification did not impact the model prediction performance for fall detection.
Abstract (translated)
Falls近年来变得越来越普遍,这对老年人来说是一种危害。因此,检测跌倒变得非常重要,并引入了多个数据集和机器学习模型与跌倒检测相关。在本报告中,提出了一种使用多模态方法的人跌倒检测方法。我们使用了由数十个志愿者使用多种传感器和两只摄像机收集的UP-Fall检测数据集。我们使用带计步器的手腕传感器将计步器数据作为标签进行二元分类,即跌倒和未跌倒从数据集中选取。我们使用了相机和传感器数据的集成来提高性能。实验结果显示,与使用多个传感器进行二元分类相比,仅使用手腕数据对于跌倒检测模型预测性能没有影响。
URL
https://arxiv.org/abs/2302.00224
