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一种使用人工碳氢网络进行人类活动识别的灵活方法。

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks.

作者信息

Ponce Hiram, Miralles-Pechuán Luis, Martínez-Villaseñor María de Lourdes

机构信息

Faculty of Engineering, Universidad Panamericana, 03920 Mexico City, Mexico.

出版信息

Sensors (Basel). 2016 Oct 25;16(11):1715. doi: 10.3390/s16111715.

DOI:10.3390/s16111715
PMID:27792136
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5134431/
Abstract

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.

摘要

鉴于可穿戴传感器取得的巨大进展，基于传感器的身体活动识别是一个日益受到关注的领域。各个领域的应用都在利用获取数据的便捷性来监测个人活动和行为，以便提供主动和个性化的服务。尽管许多活动识别系统已经开发了二十多年，但仍有一些开放性问题需要用新技术来解决。我们在本文中探讨人类活动识别的主要挑战之一：灵活性。我们在这项工作中的目标是提出人工碳氢网络，作为人类活动识别系统中的一种新颖的灵活方法。为了评估基于人工碳氢网络的分类器的性能，设计了针对独立于用户和依赖于用户的情况的实验。我们的结果表明，人工碳氢网络分类器足够灵活，可用于采用依赖于用户或独立于用户的方法构建人类活动识别系统时。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c449/5134431/ab06b8450319/sensors-16-01715-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c449/5134431/7e09646200b4/sensors-16-01715-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c449/5134431/9727820861ef/sensors-16-01715-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c449/5134431/f98ac3d8a511/sensors-16-01715-g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c449/5134431/b4b2d41331ed/sensors-16-01715-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c449/5134431/6e4737dfe75c/sensors-16-01715-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c449/5134431/ab06b8450319/sensors-16-01715-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c449/5134431/7e09646200b4/sensors-16-01715-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c449/5134431/9727820861ef/sensors-16-01715-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c449/5134431/f98ac3d8a511/sensors-16-01715-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c449/5134431/56d0810701bd/sensors-16-01715-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c449/5134431/c8dc7f652e69/sensors-16-01715-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c449/5134431/b4b2d41331ed/sensors-16-01715-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c449/5134431/6e4737dfe75c/sensors-16-01715-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c449/5134431/ab06b8450319/sensors-16-01715-g008.jpg

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