Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey, NL 64849, Mexico.
Facultad de Matemáticas, Universidad Autónoma de Yucatán, Anillo Periférico Norte, Tablaje Cat. 13615, Colonia Chuburná Hidalgo Inn, Mérida, Yucatan 97110, Mexico.
Sensors (Basel). 2019 Sep 3;19(17):3808. doi: 10.3390/s19173808.
In Ambient Intelligence (AmI), the activity a user is engaged in is an essential part of the context, so its recognition is of paramount importance for applications in areas like sports, medicine, personal safety, and so forth. The concurrent use of multiple sensors for recognition of human activities in AmI is a good practice because the information missed by one sensor can sometimes be provided by the others and many works have shown an accuracy improvement compared to single sensors. However, there are many different ways of integrating the information of each sensor and almost every author reporting sensor fusion for activity recognition uses a different variant or combination of fusion methods, so the need for clear guidelines and generalizations in sensor data integration seems evident. In this survey we review, following a classification, the many fusion methods for information acquired from sensors that have been proposed in the literature for activity recognition; we examine their relative merits, either as they are reported and sometimes even replicated and a comparison of these methods is made, as well as an assessment of the trends in the area.
在 情境智能（AmI） 中，用户正在进行的活动是上下文的重要组成部分，因此其识别对于体育、医学、个人安全等领域的应用至关重要。在 AmI 中同时使用多个传感器来识别人类活动是一种很好的实践，因为一个传感器错过的信息有时可以由其他传感器提供，并且许多工作已经表明与单个传感器相比，准确性有所提高。然而，整合每个传感器信息的方式有很多种，几乎每个报告传感器融合用于活动识别的作者都使用不同的融合方法变体或组合，因此似乎需要明确的准则和传感器数据集成的概括。在这项调查中，我们按照分类，回顾了文献中为活动识别而提出的从传感器获取的信息的许多融合方法；我们检查了它们的相对优点，无论是按照报告的方式，甚至有时还复制和比较这些方法，以及评估该领域的趋势。
