PDF(Pubmed)
Abstract：
Monitoring activities of daily living (ADLs) plays an important role in measuring and responding to a person\'s ability to manage their basic physical needs. Effective recognition systems for monitoring ADLs must successfully recognize naturalistic activities that also realistically occur at infrequent intervals. However, existing systems primarily focus on either recognizing more separable, controlled activity types or are trained on balanced datasets where activities occur more frequently. In our work, we investigate the challenges associated with applying machine learning to an imbalanced dataset collected from a fully in-the-wild environment. This analysis shows that the combination of preprocessing techniques to increase recall and postprocessing techniques to increase precision can result in more desirable models for tasks such as ADL monitoring. In a user-independent evaluation using in-the-wild data, these techniques resulted in a model that achieved an event-based F1-score of over 0.9 for brushing teeth, combing hair, walking, and washing hands. This work tackles fundamental challenges in machine learning that will need to be addressed in order for these systems to be deployed and reliably work in the real world.
摘要：
监测日常生活活动（ADL）在衡量和响应一个人管理其基本身体需求的能力方面起着重要作用。用于监视ADL的有效识别系统必须成功地识别自然活动，这些活动也以不频繁的间隔实际发生。然而,现有的系统主要侧重于识别更可分离的，受控活动类型或在活动发生更频繁的平衡数据集上进行训练。在我们的工作中,我们调查了将机器学习应用于从完全野外环境中收集的不平衡数据集的相关挑战.此分析表明，将提高召回率的预处理技术与提高精度的后处理技术相结合，可以为ADL监控等任务提供更理想的模型。在使用野外数据的独立于用户的评估中，这些技术产生了一个模型，该模型实现了基于事件的F1评分超过0.9的刷牙，梳理头发,走路,洗手。这项工作解决了机器学习中的基本挑战，这些挑战需要解决，以便这些系统能够被部署并在现实世界中可靠地工作。
