# Can People Really Do Nothing? Handling Annotation Gaps in ADL Sensor Data
## 1 Introduction
Thus, such facts definitely burden the deployment of semi-supervised learning in high-reliability real applications, which require more accurate performance compared with the existing supervised techniques. Therefore, it is crucial to build a safe semi-supervised classifier using unlabeled data without reducing the performance significantly [6]. The word safe means that the overall performance is not statistically considered worse than those using only labeled data. Many trials must be conducted, since the performance might be worse by exploiting more labeled data. The reason for the cruel effect of the unlabeled data is discussed in [19]. They inferred that the incorrect model assumptions are the main cause of the performance worsening since the learning process will be deceived by the wrong model assumptions. However, it is hard to create accurate model assumptions without applicable domain knowledge. Instead, methods that disagree with this belief usually use various learners to provide pseudo-labels of unlabeled data to enrich the whole data set. However, wrong pseudo-labels may confuse the learning process, but usually, data editing techniques are used to overcome this problem particularly on dense data, since these techniques trust on the data adjacent information [20]. This paper attacks the problem of studying and recognizing human activities of daily living (ADL), which is an important research issue in building a universal and smart environment. Consequently, it is crucial to build semantically rich models permitting knowledge in real time of which activity is performed by an occupant living in a smart home, from semantically poor sequences of observed binary events. Moreover, the main objective of this work is to find the best way to handle annotation gaps in the raw input human activity data. Therefore, we investigate the problem of unlabeled data from different perspectives using proposed annotation gaps handling paradigms. We look at the annotation gaps as unknown data that need to be used in a way that assists performance improvement. First, in paradigm #1, these gaps are treated as a source of confusion and removed. In the second paradigm, all gaps are classified as a unique _\"Unknown\"_ class. The third paradigm characterizesthe gaps by their encapsulating classes. This paradigm is inspired by observed input data pattern repetition depending on preceding and consequent activities. Finally, the first and the third paradigms are combined in a preprocessing stage to exploit some prior knowledge about the physical nature of the ground truth activities for further performance improvement. Experimentally based on a real smart home dataset, the proposed three paradigms are applied to evaluate the effect of each of them on different activity recognition metrics. The rest of this paper is organized as follows: in Section 2, an overview of the used classifier, Hidden Markov Model, is presented. In Section 3, the problem of annotation gaps with the reasons behind are discussed. The proposed annotation gap handling paradigms are detailed in Section 4. The experimental setup is explained, and the evaluation results are discussed in Sections 5 and 6, respectively. Finally, the paper is concluded in Section 7.