Effects of Time-Series Data Pre-processing on the Transformer-based Classification of Activities from Smart Glasses

Time-series classification is gaining significance in pattern recognition as time-series data becomes more abundant along with the increasing digitization of daily life and the rise of the Internet of Things (IoT). One of the biggest challenges lies in the ordered nature of time-series attributes, making traditional machine learning (ML) algorithms designed for static data unsuitable for processing temporal data. The Transformer architecture was introduced as a novel approach in natural language processing for machine translation tasks, relying solely on attention mechanisms without the need for convolution or recurrence. Since machine translation is similar to time-series data, where order is an important factor, it is also worth considering the Transformer for time-series classification. Pre-processing the data is a crucial step in the ML process and can influence the data and impact the effectiveness of the ML models. In this paper, we aim to address the effects of time-series pre-processing and data representation in combination with the Transformer model for Human Activity Recognition (HAR) using IMU data from smart glasses as input. We analyze the results based on established evaluation metrics such as the F1-score and the area under the curve (AUC).