ArXiv Preprint
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 %.
Mengxi Liu, Sungho Suh, Bo Zhou, Agnes Gruenerbl, Paul Lukowicz
2022-10-03
