.png){kind=icon}
返回
Energy-Efficient Context Classification With Dynamic Sensor Control
.png){kind=icon}
OA
AI
DOI：10.1109/TBCAS.2011.2166073.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Energy efficiency has been a longstanding design challenge for wearable sensor systems. It is especially crucial in continuous subject state monitoring due to the ongoing need for compact sizes and better sensors. This paper presents an energy-efficient classification algorithm, based on partially observable Markov decision process (POMDP). In every time step, POMDP dynamically selects sensors for classification via a sensor selection policy. The sensor selection problem is formalized as an optimization problem, where the objective is to minimize misclassification cost given some energy budget. State transitions are modeled as a hidden Markov model (HMM), and the corresponding sensor selection policy is represented using a finite-state controller (FSC). To evaluate this framework, sensor data were collected from multiple subjects in their free-living conditions. Relative accuracies and energy reductions from the proposed method are compared against naive Bayes (always-on) and simple random strategies to validate the relative performance of the algorithm. When the objective is to maintain the same classification accuracy, significant energy reduction is achieved.
Keyword:
Classification
energy efficiency
hidden Markov model
optimization
partially observable Markov decision process
sensor selection
wearable platform
期刊
IF:
5.2
论文数:
9.7K
被引数:
2.2W
机构
引用论文
A TUTORIAL ON HIDDEN MARKOV-MODELS AND SELECTED APPLICATIONS IN SPEECH RECOGNITION关于语音识别中的隐马尔可夫模型和选定应用的教程.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏分享收藏分享收藏The Nonnucleoside Reverse Transcriptase Inhibitors Efavirenz and Nevirapine in the Treatment of HIV
OAAI
OAAI分享收藏Multi-agent activity recognition using observation decomposed hidden Markov models基于观测分解隐马尔可夫模型的多智能体活动识别
PREAI
分享收藏
查看更多内容