rWISDM: Repaired WISDM, a Public Dataset for Human Activity Recognition
This work addresses dataset reliability for researchers in Human Activity Recognition, but it is incremental as it focuses on fixing an existing dataset rather than introducing new methods.
The authors tackled the problem of hidden issues in the WISDM dataset for Human Activity Recognition, which reduced classifier performance and trust; by identifying and repairing these issues, they increased classifier performance.
Human Activity Recognition (HAR) has become a spotlight in recent scientific research because of its applications in various domains such as healthcare, athletic competitions, smart cities, and smart home. While researchers focus on the methodology of processing data, users wonder if the Artificial Intelligence (AI) methods used for HAR can be trusted. Trust depends mainly on the reliability or robustness of the system. To investigate the robustness of HAR systems, we analyzed several suitable current public datasets and selected WISDM for our investigation of Deep Learning approaches. While the published specification of WISDM matched our fundamental requirements (e.g., large, balanced, multi-hardware), several hidden issues were found in the course of our analysis. These issues reduce the performance and the overall trust of the classifier. By identifying the problems and repairing the dataset, the performance of the classifier was increased. This paper presents the methods by which other researchers may identify and correct similar problems in public datasets. By fixing the issues dataset veracity is improved, which increases the overall trust in the trained HAR system.