基于多维度体感信息的在线考试异常行为监测

计算机工程与科学

基于多维度体感信息的在线考试异常行为监测

范子健，徐晶，刘威

（华中科技大学电子信息与通信学院,湖北武汉 430074）

收稿日期:2016-05-16 修回日期:2016-10-25 出版日期:2018-02-25 发布日期:2018-02-25
基金资助:
国家科技支撑计划(2015BAH33F04-05)

Somatosensory information based misbehavior

detection in online examinations

FAN Zi-jian，XU Jing，LIU Wei

(School of Electronic Information and Communications,Huazhong University of Science and Technology,Wuhan 430074,China)

Received:2016-05-16 Revised:2016-10-25 Online:2018-02-25 Published:2018-02-25

摘要/Abstract

摘要：

考试者监测是在线考试面临的主要难题之一。传统监测方案主要集中在考试者的身份识别方面，缺乏对考试者异常行为的有效识别。面向在线考试异常行为监测，提出通过体感数据采集仪Kinect获得考试者的骨骼关节点位置以及头部偏转角度等姿态数据，判别在线考试过程中考试者的异常行为。同时，针对单一的动作事件判别方法存在的虚警率高的问题，提出利用多维度体感信息识别考试者行为的新思路。通过分析一个时间窗口内异常事件发生的频次以及持续时间等信息，判断考试者当前的行为是否异常。实验表明，所提方案可以有效地监测考试者在考试过程中出现的异常行为。

关键词: 在线考试, 行为监测, 体感信息

Abstract:

Examination surveillance is one of main challenges in online examination. Traditional approaches mainly focus on the identification of examinees and lack of flexible and scalable solutions to detect the misbehavior of the examinees in online examinations. We provide a new solution to monitor the examinees’ behavior based on somatosensory information. Meanwhile, to reduce false alarm rate, a two-dimensional gesture detection scheme is proposed, in which both the duration and frequency of the detected gesture events are adopted to describe the target misbehavior. Examinees’ states are discriminated by analyzing the duration and frequency of the events happened within a time window. Experiments demonstrate that our proposed solution can effectively distinguish the examinees’ misbehavior from their normal actions.

Key words: online examination, misbehavior detection, somatosensory information

范子健，徐晶，刘威. 基于多维度体感信息的在线考试异常行为监测[J]. 计算机工程与科学.

FAN Zi-jian，XU Jing，LIU Wei.

Somatosensory information based misbehavior

detection in online examinations

[J]. Computer Engineering & Science.

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