Reducing the intrusion of user-trained activity recognition systems

William Duffy, K. Curran, D. Kelly, T. Lunney
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引用次数: 2

Abstract

Many supervised activity recognition systems require a fully labelled time-series for accurate classification. However, gathering these labels is a difficult and often unrealistic task, especially over long-time frames or outside of laboratory conditions. A potential solution is through diary studies, allowing for a user-trained activity recognition system. Requests will be presented on the user’s smart device and while this approach will be significantly less intrusive than current methods, frequent or inappropriately timed requests could reduce user acceptance. This paper proposes to further reduce user intrusion by making a prediction about the next user request and analyzing the classifiers confidence in this prediction. Two methods are presented, and with careful selection of the confidence threshold, they resulted in up to a 35% reduction in user requests with a minimal reduction in accuracy.
减少用户训练的活动识别系统的入侵
许多监督活动识别系统需要一个完全标记的时间序列来进行准确的分类。然而,收集这些标签是一项困难且往往不切实际的任务,特别是在长时间框架或实验室条件外。一个潜在的解决方案是通过日记研究,允许用户训练的活动识别系统。请求将呈现在用户的智能设备上,虽然这种方法比目前的方法侵入性要小得多,但频繁或不合时宜的请求可能会降低用户的接受度。本文提出通过对下一个用户请求进行预测并分析分类器在该预测中的置信度来进一步减少用户入侵。提出了两种方法,通过仔细选择置信度阈值,它们导致用户请求减少35%,准确性降低最小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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