# Can People Really Do Nothing? Handling Annotation Gaps in ADL Sensor Data
## 6 Results and Discussion  
Handling the annotation gaps (unlabeled data) in the raw input human activity data is a challenging task. We proposed three paradigms from different perspectives to investigate the best way to handle this unlabeled data. To evaluate and quantify the performance of the proposed paradigms, we used the aforementioned four metrics with the evaluation dataset for every single activity. Figure 6 shows the recall values. As for paradigm #1, removing all annotation gaps, it shows high recall values in three activities: _Leaving-Home_, _Sleeping_, and _Meal-Preparation_. For the _Meal-preparation_ and _Sleeping_ activities, the differences between the recall values of the three paradigms are not so big. However, for the _Leaving-Home_ activity, the superiority of paradigm #1 is obvious. In fact, this is a logical behavior. In particular, if we think about the core operation of this paradigm, we see that the interactivity annotation after the _Leaving-Home_ activity is usually removed. This is because the human annotator does not have an 'out-of-home'-like label. Therefore, he/she usually leaves all input readings between the _Leaving-Home_ and the consequent activity, which is _Entering-Home_ blank. Paradigm #1 by nature omits these gaps resulting in a 'certain', yet logical, sorted order correlation between the _Leaving-Home_ and the _Entering-Home_ activities. In contrast to the other two paradigms, they give some label to the in-between gaps separating these two activities. Since the used dataset is for a single household, ideally there should be no readings between these two activities except sensors noise. The given label for this gap, either it is an _Unknown_ label as in paradigm #2 or _Leaving-Entering_ as in paradigm #3, easily gets confused with other 'idle-similar' activities. This argument is supported by investigating the top matches to the _Leaving-Home_ activity under the operation of the two paradigms. The top classification of the _Leaning-Home_ activity under paradigms #2 and #3 is _Sleeping_. The _Sleeping_  
activity relatively conforms with the gap after _Leaving-Home_ property of being idle till the beginning of the following activity. To overcome this drawback, we suggest to extend the annotation of such kinds of activities to the successive one; or to create a new dedicated label for these specific gaps, e.g., _Out-of-Home_, as shown later in this section. Paradigm #2 does not have a bold improvement except for activities which have low repeatability of their successor activities, e.g., _Wash-Dishes_. The precision values show similar behavior to recall's with slight differences, as shown in Figure 7. Therefore, the same preceding discussion applies to this measure. For the accuracy and specificity metrics shown in Figures 8 and 9 respectively, all paradigms show high performance in slight superiority of paradigm #3. The only case in which paradigm #3 achieves noticeable improvement is with the _Sleeping_ activity. This is because of the repeatability nature of the successor activity, which is the _Bed-to-Toilette_ activity. Giving a unique label to the intermediate input between these two activities supports correct recognition decision. Figure 6: Recall values of the proposed three paradigms. Figure 7: Precision evaluation of the proposed paradigms. We developed an auxiliary preprocessing procedure to aid the model estimation and classification of HMM. Some erroneously missing annotations can be obviously restored. For example, as mentioned earlier, the annotation gap between activities such as _Leaving_Home_ and _Entering_Home_ is not logic, since it is obviously known that any recorded sensor readings between these two activities is erroneous. Therefore, the two activities should be adjacently connected. It is expected that eliminating this gap by extending the preceding _Leaving_Home_ activity is hypothetically better for recognition performance.