Biological Sensor Fusion Using Sensor Reliability Evaluation for Situation Assesment

Yoshitaka Sakurai, K. Takada, Takashi Kawabe, S. Tsuruta
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Abstract

In Web-based CSCW (Computer-Supported Cooperative Work), remote members communicate their intentions in cyberspace. However, different from face-to-face communication, partners' situations including their interest, concentration, boredom, and tiredness cannot be easily transmitted. Oversight and mishearing of remote partners is often overlooked. Besides, it is further difficult to understand their real intentions sufficiently. To overcome these problems, biological sensor fusion for dependable Web-based CSCW Systems is proposed. This assesses or estimates situations of remote users through fusing information of multiple biological sensors and the related general contexts. By transmitting and using information of estimated usersf situations, the system augments the cyberspace through stressing or providing warnings by multimedia. This paper clarifies the mechanism of sensor fusion engine that uses probabilistic or statistical data to increase estimation reliability and can learn from log data of feature vectors or symptoms to decrease knowledge acquisition bottleneck. The proposed method aims at improving reliability of situation estimation by evaluating the reliability and feed-backing the evaluation results by information requests. The reliability of the output on each process layer is evaluated based on the past log and related information. Based on the evaluated reliability, estimated situations are improved by information request to related sensors.
基于传感器可靠性评估的生物传感器融合态势评估
在基于网络的CSCW(计算机支持的协同工作)中,远程成员在网络空间中交流他们的意图。然而,与面对面的交流不同,伴侣的情况,包括他们的兴趣,注意力,无聊,疲倦,不容易传递。对远程合作伙伴的疏忽和误解往往被忽视。此外,很难充分理解他们的真实意图。为了克服这些问题,提出了基于web的可靠CSCW系统的生物传感器融合。它通过融合多个生物传感器的信息和相关的一般环境来评估或估计远程用户的情况。该系统通过传送和使用估计用户的情况信息,通过多媒体强调或提供警告来增强网络空间。本文阐述了传感器融合引擎的机制,利用概率或统计数据来提高估计可靠性,并可以从特征向量或症状的日志数据中学习,以减少知识获取瓶颈。该方法通过对态势估计的可靠性进行评估,并根据信息请求反馈评估结果,从而提高态势估计的可靠性。根据过去的日志和相关信息评估每个过程层输出的可靠性。在评估可靠性的基础上,通过对相关传感器的信息请求来改进估计情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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