Performing Activity Recognition on Unscripted Culinary Activities in a Living Lab

烹饪活动，包括食物的准备和消费，有可能为个体的心理和身体健康提供有价值的见解。通过监测这些过程而明显的行为习惯或变化可能是糖尿病、肥胖症、抑郁症和痴呆症等疾病的症状。因此，自主监控和理解厨房中发生的活动是一个重要的目标，如果实现这一目标，可以改善许多人在自己家里的舒适生活。自主家庭监控系统的一个关键部分是活动识别，因为这使得机器能够理解人类的情况和行动。这个项目的重点是基于知识的活动识别，因为这对于较小的数据集有更大的灵活性。用于创建和测试我们的模型的序列将被记录在布里斯托尔球馆中，那里已经安装了各种相机和传感器。序列将是无脚本的，允许广泛的有趣和具有挑战性的行为。计算状态空间模型将形成识别系统的基础，并使用边缘过滤来确定每个时刻最可能的状态。在项目过程中，将加强或升级这方面的要素，以提高模型的性能。动作识别将通过卷积神经网络集成视觉数据来改进，并将在分类速度和准确率方面与现有的基于特征的方法进行比较。此外，还将改进识别，以确保分类不随不同地点和系统配置而变化。将对活动识别进行升级，使该模型能够处理在相同环境中由多个人执行的活动。还将进行额外的改进，通过简单的逻辑更改来减少总体状态空间，从而提高效率。在数据允许的情况下，数据驱动的活动识别方法也将进行试验，并与以前的方法进行比较。这个项目的影响比它在厨房中的使用更广泛。在其他环境或情况下的应用可以在许多不同的部门中有广泛的不同用途。主要来说，该项目还建立在SPHERE项目正在进行的工作的基础上并加以加强。

Culinary activities, including the preparation and consumption of sustenance, have thepotential to provide valuable insights into the mental and physical health of individuals.Behavioural habits or variances which may become evident through monitoring of theseprocesses can be symptomatic of conditions such as diabetes, obesity, depression anddementia. Therefore, autonomous monitoring and understanding of activities that take placein the kitchen is an important goal that if achieved could improve the lives of many people inthe comfort of their own homes.A key part of autonomous home monitoring systems is activity recognition, as this allows formachine understanding of human situations and actions. This project focuses onknowledge-based activity recognition, since this has more flexibility for smaller data sets.The sequences used to create and test our model will be recorded in the Bristol SPHEREhouse, which already has an assortment of cameras and sensor installed. Sequences willbe unscripted, allowing for a wide range of interesting and challenging behaviours.A Computational State Space Model will form the foundation of the recognition system, withmarginal filtering used to determine the most likely state at each moment in time. During thecourse of the project, elements of this will be enhanced or upgraded in order to improvemodel performance. Action recognition will be improved through the integration of visualdata through Convolutional Neural Networks, which will be compared to current featurebased approaches in terms of speed and accuracy of classification. In addition, recognitionwill be improved to ensure that classification is invariant to different locations and systemconfigurations. Activity recognition will be upgraded to allow the model to process activitiesperformed by multiple individuals in the same environments. Additional improvements willbe made to improve efficiency by reducing the overall state space through simple logicalchanges. Data permitting, data-driven approaches to activity recognition will also be trialledand compared to previous methods.The implications of this project are wider reaching than it's use in the kitchen. Applications toother environments or situations could have a wide range of different uses in many differentsectors. Mainly, this project also builds upon and enhances the work being done by theSPHERE project.