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Can People Really Do Nothing? Handling Annotation Gaps in ADL Sensor Data

by Alaa E. Abdel Hakim 1,2,* and Wael Deabes 1,3
1
Department of Computer Science in Jamoum, Umm Al-Qura University, Makkah 25371, Saudi Arabia
2
Electrical Engineering Department, Assiut University, Assiut 71516, Egypt
3
Department of Computers and Systems Engineering, Mansoura University, Mansoura 35516, Egypt
*
Author to whom correspondence should be addressed.
Algorithms 2019, 12(10), 217; https://doi.org/10.3390/a12100217
Received: 6 September 2019 / Revised: 9 October 2019 / Accepted: 12 October 2019 / Published: 17 October 2019
In supervised Activities of Daily Living (ADL) recognition systems, annotating collected sensor readings is an essential, yet exhaustive, task. Readings are collected from activity-monitoring sensors in a 24/7 manner. The size of the produced dataset is so huge that it is almost impossible for a human annotator to give a certain label to every single instance in the dataset. This results in annotation gaps in the input data to the adopting learning system. The performance of the recognition system is negatively affected by these gaps. In this work, we propose and investigate three different paradigms to handle these gaps. In the first paradigm, the gaps are taken out by dropping all unlabeled readings. A single “Unknown” or “Do-Nothing” label is given to the unlabeled readings within the operation of the second paradigm. The last paradigm handles these gaps by giving every set of them a unique label identifying the encapsulating certain labels. Also, we propose a semantic preprocessing method of annotation gaps by constructing a hybrid combination of some of these paradigms for further performance improvement. The performance of the proposed three paradigms and their hybrid combination is evaluated using an ADL benchmark dataset containing more than 2.5 × 10 6 sensor readings that had been collected over more than nine months. The evaluation results emphasize the performance contrast under the operation of each paradigm and support a specific gap handling approach for better performance. View Full-Text
Keywords: activity recognition; annotated and unannotated data; smart environments; HMM activity recognition; annotated and unannotated data; smart environments; HMM
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Abdel Hakim, A.E.; Deabes, W. Can People Really Do Nothing? Handling Annotation Gaps in ADL Sensor Data. Algorithms 2019, 12, 217.

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