Open Datasets in Human Activity Recognition Research—Issues and Challenges: A Review

Huge amounts of data are generated with the emergence of new sensor technologies. Human activity recognition (HAR) datasets are generated from cameras, such as video or still images, capturing human behavior through sensors such as gyroscopes, Bluetooth, sound sensors, and accelerometers. These generated data sources are collected by the researchers and formed into open datasets. However, these datasets often show issues during dataset construction, sharing, and searching, which could produce further challenges for the reuse of the data by others. The main objective of this research is to explore the current issues and challenges faced by researchers in the HAR domain. A detail literature review was conducted to extract information from the published literature. Similarly, a questionnaire survey was sent to selected researchers having expertise in the HAR domain, who work with open datasets. The main issues and challenges were identified and classified into a hierarchical structure. This research will help HAR researchers to be aware of the current issues and challenges in the field of HAR open datasets. It will help to promote important attributes applicable to many open datasets, such as privacy, anonymity, platform maintenance, datasets’ descriptions, metadata, environmental conditions, resources, and training, while constructing and sharing new datasets.