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Recognition of unscripted kitchen activities and eating behaviour for health monitoring

机译：认识未定名厨房活动和健康监测的饮食行为

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Nutrition related health conditions such as diabetes and obesity can seriously impact quality of life for those who are affected by them. A system able to monitor kitchen activities and patients' eating behaviours could provide clinicians with important information helping them to improve patients' treatments. We propose a symbolic model able to describe unscripted kitchen activities and eating habits of people in home settings. This model consists of an ontology which describes the problem domain, and a Computational State Space Model (CSSM) which is able to reason in a probabilistic manner about a subject's actions, goals, and causes of any problems during task execution. To validate our model we recorded 15 unscripted kitchen activities involving 9 subjects, with the video data being annotated according to the proposed ontology schemata. We then evaluated the model's ability to recognise activities and potential goals from action sequences by simulating noisy observations from the annotations. The results showed that our model is able to recognise kitchen activities with an average accuracy of 80% when using specialised models, and with an average accuracy of 40% when using the general model.
机译：营养相关的健康状况，如糖尿病和肥胖，可能会严重影响那些受其影响的人的生活质量。能够监控厨房活动和患者的饮食行为的系统可以提供临床医生，并有重要信息，帮助他们改善患者治疗。我们提出了一个象征性的模型，能够描述家庭环境中未知的厨房活动和人们的饮食习惯。该模型由描述问题域的本体和计算状态空间模型（CSSM）组成，该计算状态空间模型（CSSM）能够以概率的方式推理关于主题的动作，目标和任务在任务执行期间的任何问题的原因。为了验证我们的型号，我们录制了15名未学式厨房活动，涉及9个受试者，视频数据根据所提出的本体模式注释。然后，我们通过模拟来自注释的噪声观测来评估模型识别活动和潜在目标的能力。结果表明，当使用专业型号时，我们的模型能够识别平均精度为80％的厨房活动，并且使用一般模型时的平均精度为40％。

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