.png){kind=icon}
返回
Activity recognition via user-trace segmentation
.png){kind=icon}
OA
AI
DOI：10.1145/1387663.1387665.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A major issue of activity recognition in sensor networks is automatically recognizing a user's high-level goals accurately from low-level sensor data. Traditionally, solutions to this problem involve the use of a location-based sensor model that predicts the physical locations of a user from the sensor data. This sensor model is often trained offline, incurring a large amount of calibration effort. In this article, we address the problem using a goal-based segmentation approach, in which we automatically segment the low-level user traces that are obtained cheaply by collecting the signal sequences as a user moves in wireless environments. From the traces we discover primitive signal segments that can be used for building a probabilistic activity model to recognize goals directly. A major advantage of our algorithm is that it can reduce a significant amount of human effort in calibrating the sensor data while still achieving comparable recognition accuracy. We present our theoretical framework for activity recognition, and demonstrate the effectiveness of our new approach using the data collected in an indoor wireless environment.
Keyword:
algorithms
performance
activity recognition
segmentation
motion patterns
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
IF:
4.7
论文数:
1.0K
被引数:
2.0K
机构
引用论文
Interleukin 1β, Tumor Necrosis Factor Alpha, and Interleukin 8 in Bronchoalveolar Lavage Fluid of Patients with Diffuse Panbronchiolitis: A Potential Mechanism of Macrolide Therapy.png)
Respiration
RespirationIF0
PREAI
分享收藏分享收藏Four year immunogenicity of the RTS,S/AS02A malaria vaccine in Mozambican children during a phase IIb trialVaccine
VaccineIF0
PREAI
分享收藏
查看更多内容