Annotation Generation from IMU-Based Human Whole-Body Motions in Daily Life Behavior
Annotation Generation from IMU-Based Human Whole-Body Motions in Daily Life Behavior
复制标题
日常生活行为中基于 IMU 的人体全身运动注释生成
DOI：
10.1109/sii46433.2020.9026240
复制
发表时间：
2020
影响因子：
3.6
通讯作者：
Wataru Takano
中科院分区：
文献类型：
--
作者：
Wataru Takano
本文提出了一种概率方法来整合人体全身运动与自然语言。惯性测量单元（IMU）记录了日常生活中人体的全身运动，并随后将其编码为运动符号。句子被手动地附加到人类运动基元以用于它们的注释。语义和句法两个方面的概率图模型表示。一个概率模型训练运动符号到单词的链接，另一个模型将句子结构表示为单词序列。这两个模型对于将人类全身运动转化为描述是有用的，其中多个单词通过第一模型从人类运动中关联，并且第二模型搜索由关联单词组成的句法一致的句子。所提出的方法进行了测试，在一个大型的数据集的人体全身运动和句子来注释这些运动。人类动作与自然语言的联系使机器人能够将人类行为的观察理解为句子。
This paper presents a probabilistic approach to-ward integrating human whole-body motions with natural language. Human whole-body motions in daily life are recorded by inertial measurement units (IMU) and subsequently encoded into motion symbols. Sentences are manually attached to the human motion primitives for their annotation. Two aspects of semantics and syntactics are represented by probabilistic graphical models. One probabilistic model trains the linking of motion symbols to words, and the other model represents sentence structure as word sequences. These two models are useful toward translating human whole-body motions into descriptions, where multiple words are associated from the human motions by the first model, and the second model searches for syntactically consistent sentences consisting of the associated words. The proposed approach was tested on a large dataset of human whole-body motions and sentences to annotate these motions. The linking of human motions to natural language enables robots to understand observations of human behavior as sentences.