Department of Computer Science, University of Rostock, 18051 Rostock, Germany.
Department of Electrical and Electronic Engineering, University of Bristol, Bristol BS8 1UB, UK.
Sensors (Basel). 2019 Feb 4;19(3):646. doi: 10.3390/s19030646.
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.
幸福感常常受到与健康相关的条件的影响。其中包括与营养相关的健康状况，这些状况会显著降低生活质量。我们设想了一个系统，该系统可以监测患者的厨房活动，并根据检测到的饮食习惯为临床医生提供改善患者健康的指标。为了取得成功，该系统必须推理出人的行为和目标。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 将人的行为的符号表示与概率推理相结合，以推理人的行为、正在准备的餐食类型及其潜在的健康影响。为了评估该方法，我们使用了未脚本的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，该方法能够推理人的烹饪行为。它还能够根据准备的餐食类型和健康状况来识别目标。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，当用于活动识别时，我们的方法与 HMM 性能相当，与 HMM 相比，其识别餐食类型的目标的准确率中位数为 1，而 HMM 的准确率中位数为 0.12。对于识别餐食是否健康，我们的方法也优于 HMM，准确率中位数为 1，而 HMM 的准确率中位数为 0.5。
