Detecting Eating Episodes From Wrist Motion Using Daily Pattern Analysis.
Detecting Eating Episodes From Wrist Motion Using Daily Pattern Analysis.
复制标题
使用日常模式分析从手腕运动检测饮食片段。
DOI：
10.1109/jbhi.2023.3341077
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发表时间：
2024
影响因子：
7.7
通讯作者：
Hoover,Adam
中科院分区：
文献类型：
--
作者：
Tang,Zeyu;Patyk,Adam;Jolly,James;Goldstein,StephanieP;Thomas,JGraham;Hoover,Adam
本文提出了新的方法来检测手腕运动吃。我们的主要新奇在于，我们将一整天的手腕运动数据作为单个样本进行分析，以便检测进食事件可以受益于昼夜背景。我们开发了一个两阶段的框架，以促进可行的全天分析。第一阶段模型计算数据窗口内进食的局部概率，第二阶段模型通过将一天内的所有数据视为一个样本来计算进食的增强概率。该框架还采用了增强技术，其中包括迭代再培训的第一阶段模型。这使我们能够从有限大小的数据集中生成足够数量的日长样本。我们在公开的克莱姆森全天（CAD）数据集和FreeFIC数据集上测试了我们的方法，发现包含日长分析大大提高了检测进食事件的准确性。我们还将我们的结果与几种最先进的方法进行了比较。我们的方法实现了89%的进食事件真阳性率（TPR），每个真阳性（FP/TP）有1.4个假阳性，时间加权准确率为84%，这是CAD数据集上报告的最高准确率。我们的研究结果表明，日常模式分类器大大提高了膳食检测，特别是减少了瞬态错误检测时，往往会发生依赖于较短的窗口来寻找个人摄入或消费事件。
This paper presents new methods to detect eating from wrist motion. Our main novelty is that we analyze a full day of wrist motion data as a single sample so that the detection of eating occurrences can benefit from diurnal context. We develop a two-stage framework to facilitate a feasible full-day analysis. The first-stage model calculates local probabilities of eatingwithin windows of data, and the second-stage model calculates enhanced probabilities of eatingby treating allwithin a single day as one sample. The framework also incorporates an augmentation technique, which involves the iterative retraining of the first-stage model. This allows us to generate a sufficient number of day-length samples from datasets of limited size. We test our methods on the publicly available Clemson All-Day (CAD) dataset and FreeFIC dataset, and find that the inclusion of day-length analysis substantially improves accuracy in detecting eating episodes. We also benchmark our results against several state-of-the-art methods. Our approach achieved an eating episode true positive rate (TPR) of 89% with 1.4 false positives per true positive (FP/TP), and a time weighted accuracy of 84%, which are the highest accuracies reported on the CAD dataset. Our results show that the daily pattern classifier substantially improves meal detections and in particular reduces transient false detections that tend to occur when relying on shorter windows to look for individual ingestion or consumption events.