A hybrid tuple selection pipeline for smartphone based Human Activity Recognition

With the increasing use of sensors in almost every device and application worldwide, the volume of data increases exponentially. Instance selection is an important data preprocessing step that can be applied to many machine learning models. It is even crucial for executing machine learning tasks in constrained environments as demanded by many Internet of Things (IoT) enabled applications today such as the Human Activity Recognition (HAR) domain of applications. In this paper, we have proposed a hybrid selection and training pipeline methodology that combines the nearest neighbor concept with evolutionary computing to address the instance selection problem of smartphone sensing-based HAR. A clustering algorithm has been proposed first followed by a Genetic Algorithm based instance selection approach. It is so far the first of its kind in the aforementioned domain of HAR based on smartphone sensing data. The proposed instance selection has been tested on two of the popular benchmark HAR datasets - UCI-HAR and WISDM. The experimented outputs report that the proposed method has effectively reduced the dataset size by around 40% for the benchmark datasets while retaining the recognition accuracy above 94%. This is a clear depiction of the removal of the outliers from the instance set. The proposed approach has also been compared with other state-of-the-art under-sampling approaches and the results show that our approach has performed better.