论文标题
基于接近的流媒体数据积极学习：个性化的饮食时刻识别
Proximity-Based Active Learning on Streaming Data: A Personalized Eating Moment Recognition
论文作者
论文摘要
检测何时饮食是朝着自动饮食监测，药物依从性评估和与饮食相关的健康干预措施迈出的重要一步。可穿戴技术通过利用机器学习算法来设计饮食习惯，从而在时间序列传感器数据上使用以检测饮食时刻，从而在设计饮食不足的饮食监测解决方案中起着核心作用。尽管已经为开发活动识别和饮食时刻检测算法进行了许多研究，但是当新用户使用培训的模型时，检测算法的性能大大下降。为了促进个性化模型的开发，我们提出了PALS，基于接近性的流媒体数据的积极学习，这是一种基于新型的基于接近性的模型，用于识别饮食手势，目的是显着减少与新用户对标签数据的需求。特别是，我们提出了一个优化问题，通过利用未标记的数据来在有限的查询预算下进行主动学习。我们对在受控和不受控制的设置中收集的数据的广泛分析表明，PLA的F评分范围为22％至39％，预算从10到60个查询不等。此外，与最先进的方法相比，离线朋友平均而言，在检测饮食手势方面，召回率高至40％，F-％f-SCORE提高了12 \％。
Detecting when eating occurs is an essential step toward automatic dietary monitoring, medication adherence assessment, and diet-related health interventions. Wearable technologies play a central role in designing unubtrusive diet monitoring solutions by leveraging machine learning algorithms that work on time-series sensor data to detect eating moments. While much research has been done on developing activity recognition and eating moment detection algorithms, the performance of the detection algorithms drops substantially when the model trained with one user is utilized by a new user. To facilitate development of personalized models, we propose PALS, Proximity-based Active Learning on Streaming data, a novel proximity-based model for recognizing eating gestures with the goal of significantly decreasing the need for labeled data with new users. Particularly, we propose an optimization problem to perform active learning under limited query budget by leveraging unlabeled data. Our extensive analysis on data collected in both controlled and uncontrolled settings indicates that the F-score of PLAS ranges from 22% to 39% for a budget that varies from 10 to 60 query. Furthermore, compared to the state-of-the-art approaches, off-line PALS, on average, achieves to 40% higher recall and 12\% higher f-score in detecting eating gestures.
