Real-time Human Activity Recognition using a Body Sensor Network

Liang Wang, Tao Gu, Hanhua Chen, Xianping Tao, Jian Lu

Publikation: Bidrag til tidsskriftKonferenceartikelForskningpeer review

Abstrakt

Real-time activity recognition using body sensor
networks is an important and challenging task and it has
many potential applications. In this paper, we propose a realtime,
hierarchical model to recognize both simple gestures and
complex activities using a wireless body sensor network. In
this model, we first use a fast, lightweight template matching
algorithm to detect gestures at the sensor node level, and
then use a discriminative pattern based real-time algorithm
to recognize high-level activities at the portable device level.
We evaluate our algorithms over a real-world dataset. The
results show that the proposed system not only achieves good
performance (an average precision of 94.9%, an average recall
of 82.5%, and an average real-time delay of 5.7 seconds), but
also significantly reduces the network communication cost by
60.2%.
OriginalsprogEngelsk
TidsskriftI E E E Transactions on Computers
Sider (fra-til)43-52
ISSN0018-9340
DOI
StatusUdgivet - 2010

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