Thomaz Edison, Zhang Cheng, Essa Irfan, Abowd Gregory D
IUI. 2015 Mar-Apr;2015:427-431. doi: 10.1145/2678025.2701405.
Dietary self-monitoring has been shown to be an effective method for weight-loss, but it remains an onerous task despite recent advances in food journaling systems. Semi-automated food journaling can reduce the effort of logging, but often requires that eating activities be detected automatically. In this work we describe results from a feasibility study conducted in-the-wild where eating activities were inferred from ambient sounds captured with a wrist-mounted device; twenty participants wore the device during one day for an average of 5 hours while performing normal everyday activities. Our system was able to identify meal eating with an F-score of 79.8% in a person-dependent evaluation, and with 86.6% accuracy in a person-independent evaluation. Our approach is intended to be practical, leveraging off-the-shelf devices with audio sensing capabilities in contrast to systems for automated dietary assessment based on specialized sensors.
饮食自我监测已被证明是一种有效的减肥方法，但尽管食品记录系统最近有所进步，它仍然是一项艰巨的任务。半自动食品记录可以减少记录的工作量，但通常需要自动检测进食活动。在这项工作中，我们描述了一项在自然环境中进行的可行性研究的结果，其中进食活动是从佩戴在手腕上的设备捕获的环境声音中推断出来的；20名参与者在一天中佩戴该设备，平均佩戴5小时，同时进行正常的日常活动。在依赖个人的评估中，我们的系统能够以79.8%的F值识别用餐，在独立于个人的评估中，准确率为86.6%。我们的方法旨在实用，与基于专门传感器的自动饮食评估系统相比，利用具有音频传感能力的现成设备。
