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.

翻译：近年来，跌倒事件频繁发生，对老年人的健康构成严重威胁。因此，跌倒检测的重要性日益凸显，相关领域已涌现出多个数据集和机器学习模型。本研究报告提出了一种基于多模态方法的人类跌倒检测技术。我们采用了由数十名志愿者通过不同传感器和两台摄像机采集的UP-FALL检测数据集。研究中使用腕部传感器采集加速度计数据，并将标签进行二元分类（即"跌倒"与"未跌倒"）。为提升性能，我们融合了摄像头与传感器数据。实验结果表明，在二元分类任务中，仅使用腕部数据与使用多传感器数据相比，对跌倒检测模型的预测性能未产生显著影响。