A Positionally Encoded Transformer for Monitoring Health Contexts of Hajj Pilgrims from Wearable Sensor Data

Monitoring the health of individuals during physically demanding tasks, such as the Hajj pilgrimage, requires robust methods for real-time detection of health-relevant contexts, including physical tiredness, emotional mood, and activity type. This paper introduces a positionally encoded Transformer model designed to detect these contexts from time-series data collected via wearable sensors. The model leverages Long Short-Term Memory (LSTM) for feature extraction and Transformer layers for context classification, utilizing positional encoding to capture the sequential dependencies within the sensor data. Our experiments, using data from 19 participants, show that the proposed model achieves high classification accuracy across multiple health-relevant contexts, significantly improving real-time health monitoring.