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Classification of Activities of Daily Living Based on Depth Sequences and Audio

机译：基于深度序列和音频的日常生活活动分类

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In this paper, we propose a non-intrusive daily activity recognition system to monitor elders who are living alone. Our contributions are to collect a dataset of activities of daily living consist of RGB, depth, silhouette, skeleton and audio data streams and to develop a system to recognize 20 different activities using depth image sequences and audio data. We have trained two separate Neural Networks to recognize activities from human silhouette features and Short Time Fourier Transform features extracted from depth images and audio data. Predictions from the two Neural Networks are fed into a fusion model and the final activity classification is done. The proposed activity recognition system can be used in assisted living systems to enhance the quality of the lifestyle of the elders who are living alone.
机译：在本文中，我们提出了一个非侵入式日常活动识别系统，以监测独居的长老。我们的贡献是收集日常生活活动的数据集，包括RGB，深度，剪影，骨架和音频数据流，并开发一个使用深度图像序列和音频数据识别20个不同活动的系统。我们训练了两个单独的神经网络，以识别从人类轮廓的功能和从深度图像和音频数据提取的短时间傅里叶变换功能。从两个神经网络的预测被馈送到融合模型中，完成最终活动分类。建议的活动识别系统可用于辅助生活系统，以提高独自生活的长老生活方式的质量。

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