论文标题
Smart-Badge：带有多模式传感器的可穿戴徽章，用于厨房活动识别
Smart-Badge: A wearable badge with multi-modal sensors for kitchen activity recognition
论文作者
论文摘要
人类健康与他们的日常行为和环境密切相关。但是，保持健康的生活方式对于大多数人来说仍然具有挑战性，因为很难认识到他们的生活行为并确定周围的情况以采取适当的行动。人类活动识别是建立用户行为模型的一种有前途的方法，用户可以通过该模型获得有关其习惯的反馈，并鼓励发展更健康的生活方式。在本文中，我们提供了一个带有六种传感器的智能灯具可穿戴徽章，包括红外阵列传感器MLX90640，可提供隐私，低成本和非侵入性功能，以在现实的未修改厨房环境中识别日常活动。基于数据和特征融合方法的多渠道卷积神经网络（MC-CNN）用于对14种与潜在不健康习惯相关的人类活动进行分类。同时，我们评估了红外阵列传感器对这些活动识别准确性的影响。我们证明了拟议的工作的表现，以检测10名平均准确度为92.44％和F1得分为88.27％的14个活动。
Human health is closely associated with their daily behavior and environment. However, keeping a healthy lifestyle is still challenging for most people as it is difficult to recognize their living behaviors and identify their surrounding situations to take appropriate action. Human activity recognition is a promising approach to building a behavior model of users, by which users can get feedback about their habits and be encouraged to develop a healthier lifestyle. In this paper, we present a smart light wearable badge with six kinds of sensors, including an infrared array sensor MLX90640 offering privacy-preserving, low-cost, and non-invasive features, to recognize daily activities in a realistic unmodified kitchen environment. A multi-channel convolutional neural network (MC-CNN) based on data and feature fusion methods is applied to classify 14 human activities associated with potentially unhealthy habits. Meanwhile, we evaluate the impact of the infrared array sensor on the recognition accuracy of these activities. We demonstrate the performance of the proposed work to detect the 14 activities performed by ten volunteers with an average accuracy of 92.44 % and an F1 score of 88.27 %.
