Analysis of the Usefulness of Mobile Eyetracker for the Recognition of Physical Activities
Analysis of the Usefulness of Mobile Eyetracker for the Recognition of Physical Activities
复制标题
移动眼动仪识别身体活动的实用性分析
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
P. Lukowicz
中科院分区：
文献类型：
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作者：
Peter Hevesi;Jamie A. Ward;Orkhan Amiraslanov;Gerald Pirkl;P. Lukowicz
我们调查了来自一个
可穿戴式眼动仪，用于检测装配期间的身体活动
和建设任务。大型体力活动，如搬运
重物和行走，与更精确的，
手工工具活动，如使用螺丝刀。统计分析
基于眼睛的特征，如注视长度和注视频率
显示了精确活动的显著相关性。使用此
发现，我们选择了10，校准免费的眼睛功能，以训练一个
分类器可识别多达6种不同的活动。逐帧
和基于事件的结果使用来自
一个包含超过600个活动事件的8人数据集。我们
还评估当注视特征
与来自可穿戴加速计的数据相结合，
麦克风。我们的初步结果显示，
精确度和召回率高达0.69和0.84，独立训练
使用凝视对精确活动进行识别。这表明
凝视适合于发现微妙的精确活动，
更复杂的分类器融合的有用来源。
We investigate the usefulness of information from a
wearable eyetracker to detect physical activities during assembly
and construction tasks. Large physical activities, like carrying
heavy items and walking, are analysed alongside more precise,
hand-tool activities like using a screwdriver. Statistical analysis of
eye based features like fixation length and frequency of fixations
show significant correlations for precise activities. Using this
finding, we selected 10, calibration-free eye features to train a
classifier for recognising up to 6 different activities. Frame-byframe
and event based results are presented using data from
an 8-person dataset containing over 600 activity events. We
also evaluate the recognition performance when gaze features
are combined with data from wearable accelerometers and
microphones. Our initial results show a duration-weighted event
precision and recall of up to 0.69 & 0.84 for independently trained
recognition on precise activities using gaze. This indicates that
gaze is suitable for spotting subtle precise activities and can be
a useful source for more sophisticated classifier fusion.
影响因子：
16.6
作者：
Bulling, Andreas;Blanke, Ulf;Schiele, Bernt
通讯作者：
Schiele, Bernt