摘要
由一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-关于人工智能的最新研究结果已经发表。根据NewsRx记者来自德克萨斯州Col Lege Station的新闻报道，研究表明，"监测日常生活活动(ADLs)在衡量和响应一个人管理基本身体需求的能力方面发挥着重要作用。"这项研究的财政支持者包括国家科学基金会。我们的新闻记者从德克萨斯农工大学的研究中获得了一句话：“有效的ADL识别系统必须成功地识别自然主义活动，这些活动实际上也是以不频繁的间隔发生的。然而，现有的系统主要集中在识别更可分离的受控活动类型上，或者在活动更频繁发生的平衡数据集上进行培训。在我们的工作中，我们研究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挑战。这一分析表明，将提高召回率的预处理技术和提高精度的后处理技术相结合，可以产生更理想的ADL监控任务模型。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on artificial in telligence have been published. According to news reporting originating from Col lege Station, Texas, by NewsRx correspondents, research stated, “Monitoring acti vities of daily living (ADLs) plays an important role in measuring and respondin g to a person’s ability to manage their basic physical needs.” Financial supporters for this research include National Science Foundation. Our news reporters obtained a quote from the research from Texas A& M University: “Effective recognition systems for monitoring ADLs must successful ly 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 ass ociated with applying machine learning to an imbalanced dataset collected from a fully in-the-wild environment. This analysis shows that the combination of prep rocessing techniques to increase recall and postprocessing techniques to increas e precision can result in more desirable models for tasks such as ADL monitoring .”
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