.png){kind=icon}
返回
RadioSense: Exploiting Wireless Communication Patterns for Body Sensor Network Activity Recognition
.png){kind=icon}
PRE
AI
DOI：10.1109/RTSS.2012.62.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Automatically recognizing human activities in a body sensor network (BSN) enables many human-centric applications. Many current works recognize human activities through collecting and analyzing sensor readings from on-body sensor nodes. These sensing-based solutions face a dilemma. On one hand, to guarantee data availability and recognition accuracy, sensing-based solutions have to either utilize a high transmission power or involve a packet retransmission mechanism. On the other hand, enhancing the transmission power increases a sensor node's energy overheads and communication range. The enlarged communication range in consequence increases privacy risks. A packet retransmission mechanism complicates on-body sensor nodes' MAC layer and hence increases energy overheads. In contrast to the sensing-based solutions, we build RadioSense, a prototype system that exploits wireless communication patterns for BSN activity recognition. Using RadioSense, we benchmark three system parameters (transmission (TX) power, packet sending rate, and smoothing window size) to design algorithms for system parameter selection. The algorithms aim to balance accuracy, latency, and energy overheads. In addition, we investigate the minimal amount of training data needed for reliable performance. We evaluate our RadioSense system with multiple subjects' data collected over a two-week period and demonstrate that RadioSense achieves reliable performance in terms of accuracy, latency, and battery lifetime.
Keyword:
Body Sensor Network
On-body Sensor Nodes
Communication Patterns
Activity Recognition
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
机构
引用论文
.png){kind=icon}
查看更多内容