Proximity-based active learning for eating moment recognition in wearable systems
Proximity-based active learning for eating moment recognition in wearable systems
复制标题
基于接近度的主动学习，用于可穿戴系统中的进食时刻识别
DOI：
10.1145/3396870.3400011
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Ghasemzadeh, Hassan
中科院分区：
文献类型：
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作者：
Nourollahi, Marjan;Rokni, Seyed Ali;Alinia, Parastoo;Ghasemzadeh, Hassan
检测进食时间是实现自动饮食监测、药物依从性评估和饮食相关健康干预的重要一步。可穿戴技术在设计不引人注目的饮食监测解决方案方面发挥着核心作用，它利用机器学习算法对时间序列传感器数据进行处理，以检测饮食时刻。虽然在开发活动识别和进食力矩检测算法方面已经做了很多研究，但当新用户使用该模型时，检测算法的性能会大幅下降。为了促进个性化模型的发展，我们提出了PALS，即基于邻近度的流数据主动学习，这是一种新的基于邻近度的进食手势识别模型，可以显著减少新用户对标记数据的需求。我们对受控和非受控设置的广泛分析表明，对于10到60次查询的预算，PALS的F-score范围从22%到39%。此外，与最先进的方法相比，离线PALS在检测进食手势方面的召回率提高了40%，f值提高了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 unobtrusive 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 is utilized by a new user. To facilitate the development of personalized models, we propose PALS, Proximity-based Active Learning on Streaming data, a novel proximity-based model for recognizing eating gestures to significantly decrease the need for labeled data with new users. Our extensive analysis in both controlled and uncontrolled settings indicates F-score of PALS ranges from 22% to 39% for a budget that varies from 10 to 60 queries. Furthermore, compared to the state-of-the-art approaches, off-line PALS achieves up to 40% higher recall and 12% higher F-score in detecting eating gestures.
观点论文：说服性技术的健康生活：框架、问题和挑战
DOI：
--
发表时间：
2009
期刊：
J. Am. Medical Informatics Assoc.
影响因子：
--
作者：
S. Chatterjee;Alan Price
通讯作者：
Alan Price