AugToAct: scaling complex human activity recognition with few labels
AugToAct: scaling complex human activity recognition with few labels
复制标题
DOI：
10.1145/3360774.3360831
复制
发表时间：
2019-11
期刊：
影响因子：
--
通讯作者：
A. Faridee;Md Abdullah Al Hafiz Khan;Nilavra Pathak;Nirmalya Roy
中科院分区：
文献类型：
--
作者：
A. Faridee;Md Abdullah Al Hafiz Khan;Nilavra Pathak;Nirmalya Roy
来自可穿戴传感器数据的人类活动识别（HAR）最近在许多领域中获得了广泛采用。然而，识别复杂的人类活动，姿势和有节奏的身体运动（例如舞蹈，体育）是具有挑战性的，由于缺乏特定领域的标签信息，由于年龄，性别，灵巧性和专业训练水平的人类运动运动学特征的永久变化。在本文中，我们提出了一种深度活动识别模型，用于处理有限的标记数据，无论是简单的还是复杂的人类活动。为了减轻内部和用户间的时空变化的运动，我们提出了新的数据增强和域归一化技术。我们描述了一种半监督技术，该技术从稀疏标记的数据中学习噪声和变换不变特征表示，以适应人体运动运动学的个人和用户间变化。我们还假设了一种迁移学习方法，通过最小化源域和目标域之间的特征分布距离来学习域不变特征表示。我们展示了我们提出的框架，AugToAct，使用公共HAR数据集的性能改进。我们还设计了自己的数据收集，注释和实验设置复杂的舞蹈活动识别步骤和运动学运动，我们实现了更高的性能指标与有限的标签数据相比，简单的活动识别任务。
Human activity recognition (HAR) from wearable sensor data has recently gained widespread adoption in a number of fields. However, recognizing complex human activities, postural and rhythmic body movements (e.g. dance, sports) is challenging due to the lack of domain-specific labeling information, the perpetual variability in human movement kinematics profiles due to age, sex, dexterity and the level of professional training. In this paper, we propose a deep activity recognition model to work with limited labeled data, both for simple and complex human activities. To mitigate the intra and inter-user spatio-temporal variability of movements, we posit novel data augmentation and domain normalization techniques. We depict a semi-supervised technique that learns noise and transformation invariant feature representation from sparsely labeled data to accommodate intra-personal and inter-user variations of human movement kinematics. We also postulate a transfer learning approach to learn domain invariant feature representations by minimizing the feature distribution distance between the source and target domains. We showcase the improved performance of our proposed framework, AugToAct, using a public HAR dataset. We also design our own data collection, annotation and experimental setup on complex dance activity recognition steps and kinematics movements where we achieved higher performance metrics with limited label data compared to simple activity recognition tasks.