Smart-Badge: A wearable badge with multi-modal sensors for kitchen activity recognition

• URL: http://arxiv.org/abs/2210.00888v1
• Date: Mon, 3 Oct 2022 12:52:46 GMT
• Title: Smart-Badge: A wearable badge with multi-modal sensors for kitchen activity recognition
• Abstract summary: 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. A multi-channel convolutional neural network is applied to classify 14 human activities associated with potentially unhealthy habits. 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 %.
• Score: 8.377995303174325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 %.

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