Humans are arguably innately prepared to comprehend others' emotional
expressions from subtle body movements. If robots or computers can be empowered
with this capability, a number of robotic applications become possible.
Automatically recognizing human bodily expression in unconstrained situations,
however, is daunting given the incomplete understanding of the relationship
between emotional expressions and body movements. The current research, as a
multidisciplinary effort among computer and information sciences, psychology,
and statistics, proposes a scalable and reliable crowdsourcing approach for
collecting in-the-wild perceived emotion data for computers to learn to
recognize body languages of humans. To accomplish this task, a large and
growing annotated dataset with 9,876 video clips of body movements and 13,239
human characters, named BoLD (Body Language Dataset), has been created.
Comprehensive statistical analysis of the dataset revealed many interesting
insights. A system to model the emotional expressions based on bodily
movements, named ARBEE (Automated Recognition of Bodily Expression of Emotion),
has also been developed and evaluated. Our analysis shows the effectiveness of
Laban Movement Analysis (LMA) features in characterizing arousal, and our
experiments using LMA features further demonstrate computability of bodily
expression. We report and compare results of several other baseline methods
which were developed for action recognition based on two different modalities,
body skeleton, and raw image. The dataset and findings presented in this work
will likely serve as a launchpad for future discoveries in body language
understanding that will enable future robots to interact and collaborate more
effectively with humans.

本研究提出了一种可扩展可靠的众包方法，用于收集自然状态下的人类身体语言数据，建立了一个大型数据集并通过统计分析揭示了许多有趣的想法。通过使用 Laban Movement Analysis 等特征，我们开发并评估了一个名为 ARBEE 的系统，该系统能够基于身体动作识别情感表达。