Recently, egocentric activity recognition has attracted considerable attention in the pattern recognition and artificial intelligence communities because of its widespread applicability to human systems, including the evaluation of dietary and physical activity and the monitoring of patients and older adults. In this paper, we present a knowledge-driven multisource fusion framework for the recognition of egocentric activities in daily living (ADL). This framework employs Dezert-Smarandache theory across three information sources: the wearer's knowledge, images acquired by a wearable camera, and sensor data from wearable inertial measurement units and GPS. A simple likelihood table is designed to provide routine ADL information for each individual. A well-trained convolutional neural network is then used to produce a set of textual tags that, along with routine information and other sensor data, are used to recognize ADLs based on information theory-based statistics and a support vector machine. Our experiments show that the proposed method accurately recognizes 15 predefined ADL classes, including a variety of sedentary activities that have previously been difficult to recognize. When applied to real-life data recorded using a self-constructed wearable device, our method outperforms previous approaches, and an average accuracy of 85.4% is achieved for the 15 ADLs.

中文翻译：

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由用户定义的知识驱动的以自我为中心的活动识别的多源融合框架。

最近，以自我为中心的活动识别由于其在人体系统中的广泛适用性而在模式识别和人工智能领域引起了广泛关注，包括对饮食和身体活动的评估以及对患者和老年人的监控。在本文中，我们提出了一个知识驱动的多源融合框架，用于识别日常生活中的自我中心活动（ADL）。该框架在三个信息源上采用Dezert-Smarandache理论：佩戴者的知识，可穿戴式相机获取的图像以及可穿戴式惯性测量单元和GPS的传感器数据。一个简单的似然表旨在为每个人提供例行的ADL信息。然后，使用训练有素的卷积神经网络来生成一组文本标签，连同常规信息和其他传感器数据一起，用于基于基于信息论的统计信息和支持向量机来识别ADL。我们的实验表明，所提出的方法可以准确识别15种预定义的ADL类，包括以前难以识别的各种久坐活动。当将其应用于使用自行构造的可穿戴设备记录的真实数据时，我们的方法优于以前的方法，并且对于15种ADL而言，其平均准确度达到85.4％。包括以前难以识别的各种久坐活动。当将其应用于使用自行构造的可穿戴设备记录的真实数据时，我们的方法优于以前的方法，并且对于15种ADL而言，其平均准确度达到85.4％。包括以前难以识别的各种久坐活动。当将其应用于使用自行构造的可穿戴设备记录的真实数据时，我们的方法优于以前的方法，并且对于15种ADL而言，其平均准确度达到85.4％。