Subject Recognition (SR) refers to the task of identifying persons performing activities in a smart environment using the data captured by the sensors installed in it. The existing literature mainly concentrates on supervised SR using the sensor data captured through multiple modalities. However, majority of the real-life sensor datasets are not annotated with the subjects performing the activities, which creates a scarcity of labeled data samples for this task. Issues of privacy and high manual annotation costs further complicate the problem of less labeled data. In addition to this problem, most of the datasets are of low modalities. Hence, the challenge lies in developing semi-supervised frameworks that are suitable for low-modal sensor data with sparse or no labels. Towards this, we initially perform benchmark experiments to analyze the factors of modality and amount of labeled data in the context of SR. Then, we propose semi-supervised frameworks for SR on the data collected by low-modal ubiquitous and visual sensors. In particular, we propose a clustering-based pseudo label generation algorithm to facilitate the training process in a semi-supervised domain for ubiquitous data. On the other hand, we propose Transfer Learning and Data Augmentation (TLDA) framework to perform SR on visual data in semi-supervised domain. To validate our proposed frameworks, we perform experiments on three real-world datasets, namely Smartphone, OPPORTUNITY, and UTD-MHAD dataset to achieve an accuracy of around 77%, 98%, and 91% respectively. Finally, we also provide an analysis on the aspect of merging modalities to propose a new research dimension for SR.

主题识别（SR）是指使用安装在智能环境中的传感器捕获的数据来识别在智能环境中从事活动的人员的任务。现有文献主要使用通过多种方式捕获的传感器数据来研究有监督的SR。但是，大多数现实生活中的传感器数据集都没有进行活动的对象注释，这导致该任务的标签数据样本稀缺。隐私问题和较高的手动注释成本进一步使标签数据较少的问题变得复杂。除此问题外，大多数数据集的模态都较低。因此，挑战在于开发适用于具有稀疏标签或无标签的低模态传感器数据的半监督框架。为此，我们最初会进行基准实验，以分析SR情境下标记数据的形式和数量的因素。然后，我们针对低模态无处不在的视觉传感器收集的数据，提出了SR的半监督框架。特别是，我们提出了一种基于聚类的伪标签生成算法，以促进遍历数据在半监督域中的训练过程。另一方面，我们提出了转移学习和数据增强（TLDA）框架来对半监督域中的可视数据执行SR。为了验证我们提出的框架，我们对三个真实世界的数据集（即智能手机，机会和UTD-MHAD数据集）进行了实验，以分别达到约77％，98％和91％的准确性。最后，