UPIC: user and position independent classical approach for locomotion and transportation modes recognition

The Sussex-Huawei Locomotion-Transportation (SHL) Challenge 2020 was an open competition of recognizing eight different activities that had been performed by three individual users and participants of this competition were tasked to classify these eight different activities with modes of locomotion and transportation. This year's data was recorded with a smartphone which was located in four different body positions. The primary challenge was to make a user-invariant as well as position-invariant classification model. The train set consisted of data from only user-1 with all positions whereas the test set consisted of data from user 2 and 3 with unspeicified sensor position. Moreover, a small validation with the same charecteristics of the test set was given to validate the classifier. In this paper, we have described our (Team Red Circle) approach in which we have used previous year's challenge data as well as this year's provided data to make our training dataset and validation set that have helped us to make our model generative. In our approach, we have extracted various types of features to make our model user independent and position invariant, we have applied Random Forest classifier which is a classical machine learning algorithm and achieved 92.69% accuracy on our customized train set and 77.04% accuracy on our customized validation set.