Sensor-based human activity recognition (HAR) requires to predict the action of a person based on sensor-generated time series data. HAR has attracted major interest in the past few years, thanks to the large number of applications enabled by modern ubiquitous computing devices. While several techniques based on hand-crafted feature engineering have been proposed, the current state-of-the-art is represented by deep learning architectures that automatically obtain high level representations and that use recurrent neural networks (RNNs) to extract temporal dependencies in the input. RNNs have several limitations, in particular in dealing with long-term dependencies. We propose a novel deep learning framework, \algname, based on a purely attention-based mechanism, that overcomes the limitations of the state-of-the-art. We show that our proposed attention-based architecture is considerably more powerful than previous approaches, with an average increment, of more than $7\%$ on the F1 score over the previous best performing model. Furthermore, we consider the problem of personalizing HAR deep learning models, which is of great importance in several applications. We propose a simple and effective transfer-learning based strategy to adapt a model to a specific user, providing an average increment of $6\%$ on the F1 score on the predictions for that user. Our extensive experimental evaluation proves the significantly superior capabilities of our proposed framework over the current state-of-the-art and the effectiveness of our user adaptation technique.
翻译:以感官为基础的人类活动识别(HAR)要求预测一个人根据传感器生成的时间序列数据采取的行动。HAR在过去几年中引起了极大的兴趣,因为现代无所不在的计算机装置能够提供大量应用;虽然提出了基于手工艺特征工程的若干技术,但目前最先进的建筑是由各种深层次的学习结构所代表,这些结构自动获得高层次的表示力,并且利用经常性神经网络(RNN)来提取输入中的时间依赖性。RNN具有若干局限性,特别是在处理长期依赖性方面。我们提出了一个新的深层次学习框架,即基于纯粹基于关注的机制的algname,克服了最新工艺的局限性。我们表明,我们拟议的基于关注的建筑比以往方法要强大得多,平均比先前最佳执行模式的F1分高出7美元以上。此外,我们考虑了使HAR深层次学习模式个性化的问题,这在若干应用中都非常重要。我们提出了一个新的深层次学习框架,即基于纯粹基于关注的基于关注性的机制,即基于我们用户平均和高层次的升级的预测能力,我们的一个简单和高层次的用户水平的升级战略,根据我们的用户对目前具体递增率的系统进行系统的升级。