Analysing Cooking Behaviour in Home Settings: Towards Health Monitoring †
Analysing Cooking Behaviour in Home Settings: Towards Health Monitoring †
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DOI：
10.3390/s19030646
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发表时间：
2019-02
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影响因子：
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通讯作者：
Kristina Yordanova;S. Lüdtke;Samuel Whitehouse;Frank Krüger;A. Paiement;M. Mirmehdi;I. Craddock;T. Kirste
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作者：
Kristina Yordanova;S. Lüdtke;Samuel Whitehouse;Frank Krüger;A. Paiement;M. Mirmehdi;I. Craddock;T. Kirste
健康往往受到与健康有关的条件的影响。其中包括与营养有关的健康状况，这可能会显著降低生活质量。我们设想一个系统，可以监控病人的厨房活动，并根据检测到的饮食行为，为临床医生提供改善病人健康的指标。为了取得成功，这种系统必须对人的行为和目标进行推理。为了解决这个问题，我们引入了一种符号行为识别方法，称为计算因果行为模型（CCBM）。CCBM将人的行为的象征性表示与概率推理结合起来，对一个人的行为、所准备的膳食类型及其潜在的健康影响进行推理。为了评估这种方法，我们使用了一个无脚本厨房活动的烹饪数据集，其中包含来自真实厨房中各种传感器的数据。结果表明，该方法能够对人的烹饪行为进行推理。它还能够识别目标的类型准备餐和它是否健康。此外，我们将CCBM与最先进的方法，如隐马尔可夫模型（HMM）和决策树（DT）进行了比较。结果表明，我们的方法在活动识别方面的表现与HMM和DT相当。当应用HMM时，它在目标识别方面的表现优于HMM，中位精度为1，而中位精度为0.12。我们的方法在识别一顿饭是否健康方面也优于HMM，准确率中位数为1，而HMM的准确率中位数为0.5。
Wellbeing is often affected by health-related conditions. Among them are nutrition-related health conditions, which can significantly decrease the quality of life. We envision a system that monitors the kitchen activities of patients and that based on the detected eating behaviour could provide clinicians with indicators for improving a patient’s health. To be successful, such system has to reason about the person’s actions and goals. To address this problem, we introduce a symbolic behaviour recognition approach, called Computational Causal Behaviour Models (CCBM). CCBM combines symbolic representation of person’s behaviour with probabilistic inference to reason about one’s actions, the type of meal being prepared, and its potential health impact. To evaluate the approach, we use a cooking dataset of unscripted kitchen activities, which contains data from various sensors in a real kitchen. The results show that the approach is able to reason about the person’s cooking actions. It is also able to recognise the goal in terms of type of prepared meal and whether it is healthy. Furthermore, we compare CCBM to state-of-the-art approaches such as Hidden Markov Models (HMM) and decision trees (DT). The results show that our approach performs comparable to the HMM and DT when used for activity recognition. It outperformed the HMM for goal recognition of the type of meal with median accuracy of 1 compared to median accuracy of 0.12 when applying the HMM. Our approach also outperformed the HMM for recognising whether a meal is healthy with a median accuracy of 1 compared to median accuracy of 0.5 with the HMM.