Classification of Steps on Road Surface Using Acceleration Signals
Journal Title: EAI Endorsed Transactions on Cognitive Communications - Year 2015, Vol 1, Issue 5
Abstract
In order to reduce a road monitoring cost, we propose a system to monitor extensively road condition by cyclists with a smartphone. In this paper, we propose two methods towards road monitoring. First is to classify road signals to four road conditions. Second is to extract road signal from a smartphone's accelerometer in three positions: pants' side pocket, chest pocket and a bag in a front basket. In pants' side pocket, road signal is extracted by Independent Component Analysis. In chest pocket and bag in a front basket, road signal is extracted by selecting 1-axis affected from gravitational acceleration. In the experiment of the classification method, overall accuracy was 75%. The experimental results of the extraction methods with correlation coefficient showed the overall accuracy were more than 0.7 in pants' side pocket and chest pocket, the overall accuracy was less than 0.3 in bag in a front basket.
Authors and Affiliations
Junji Takahashi, Yusuke Kobana, Yoshito Tobe, Gullaume Lopez
Keywords
Related Articles
- EP ID EP45636
- DOI http://dx.doi.org/10.4108/eai.22-7-2015.2260293
- Views 238
- Downloads 0
Quantitative Safety and Security Analysis from a Communication Perspective
This paper introduces and exemplifies a trade-off analysis of safety and security properties in distributed systems. The aim is to support analysis for real-time communication and authentication building blocks in a wire...
Low-Complex Reliable Communications between Wireless Network-Nodes
We present a low-complex blind interference reduction scheme embedded in the receiver to enhance correlative data detection. The key element is a statistically controlled adaptive nonlinearity prior to correlation. This...
Hypernetworks based Radio Spectrum Profiling in Cognitive Radio Networks
This paper presents a novel concept of active radio spectrum profiling f or Cognitive Radio (CR) networks using evolutionary hypernetworks. Spectrum profiling enables cognitive radio nodes to abstract and predict usable...
Predictive channel selection: Practical implementation and a social-aware vision for spectrum use
This paper demonstrates a predictive channel selection method by implementing it in software-defined radio (SDR) platforms and measuring the performance using over-the-air video transmissions. The method uses both long t...
Detecting Multi-ChannelWireless Microphone User Emulation Attacks in White Space with Noise
Cognitive radio networks (CRNs) are susceptible to primary user emulation (PUE) attacks. Conventional PUE attack detection approaches consider television broadcasting as the primary user. In this work, however, we study...