论文标题
基于传感器的人类活动识别的不变特征学习
Invariant Feature Learning for Sensor-based Human Activity Recognition
论文作者
论文摘要
多年来，基于可穿戴传感器的人类活动识别（HAR）一直是无处不在和移动计算领域的研究重点。近年来，许多深层模型已用于HAR问题。但是，深度学习方法通常需要大量数据才能很好地概括。由不同的参与者或不同传感器设备引起的显着差异限制了预训练模型的直接应用到以前从未见过的主题或设备上。为了解决这些问题，我们提出了一个不变的功能学习框架（IFLF），该框架提取了跨主题和设备共享的常见信息。 IFLF结合了两个学习范式：1）元学习以捕获跨看到域的稳健特征，并适应具有基于相似性数据选择的未见域； 2）多任务学习来处理数据短缺并通过不同主题之间的知识共享来提高整体绩效。实验表明，IFLF可以有效地处理受欢迎的开放数据集和内部数据集的主题和设备转移。它的表现优于测试准确性高达40％的基线模型。
Wearable sensor-based human activity recognition (HAR) has been a research focus in the field of ubiquitous and mobile computing for years. In recent years, many deep models have been applied to HAR problems. However, deep learning methods typically require a large amount of data for models to generalize well. Significant variances caused by different participants or diverse sensor devices limit the direct application of a pre-trained model to a subject or device that has not been seen before. To address these problems, we present an invariant feature learning framework (IFLF) that extracts common information shared across subjects and devices. IFLF incorporates two learning paradigms: 1) meta-learning to capture robust features across seen domains and adapt to an unseen one with similarity-based data selection; 2) multi-task learning to deal with data shortage and enhance overall performance via knowledge sharing among different subjects. Experiments demonstrated that IFLF is effective in handling both subject and device diversion across popular open datasets and an in-house dataset. It outperforms a baseline model of up to 40% in test accuracy.
