Context-Adaptive Heterogeneous Models for Human Activity Recognition

在广泛的实际应用的驱动下，最近已经做出了重大努力来探索利用AP适配器收集的信息的基于数据的人类活动识别模型。这些数据通常是从日常环境中收集的，其中包含了许多阻碍人类活动识别的特定环境因素。另一方面，数据分布在具有不同计算能力和电池寿命的收集设备上。这两个挑战促使研究设计上下文自适应异构模型，这些模型在理论上和经验上对多场景中的人类活动识别有效，并且可以以最小的努力推广到新的上下文。可持续能源需求对模型的可扩展性引入了另一个限制，即轻量级。所提出的模型将使用widar3.0等开放数据集和实测数据进行验证。

Driven by a wide range of real-world applications, significant efforts have recently been made to explore the data-based human activity recognition model that utilizes the information collected by the AP adaptor. The data is generally collected from the daily environment, which contains a lot of environment-specific factors hampering the recognition of human activities. On the other hand, the data is distributed at collection devices with different computing capabilities and battery life. These two challenges motivate the research to design context-adaptive heterogeneous models that are theoretically and empirically effective for human activity recognition in multiple scenes and can generalize to a new context with minimal effort. The sustainable energy requirement introduces another constraint on the scalability of models to be lightweight. The proposed models will be verified using open datasets such as widar3.0 and the measured data.