Smart mobile application for real-time monitoring and prediction of pilgrim crowd activities in Hajj and Umrah using wearable sensors

Performing Hajj or Umrah is an Islamic pilgrim tradition and the fifth important Islam pillar. Preparing to implement Hajj or Umrah is very important, and in the middle of travelling to Mecca, learning all the rules, rituals, and obligatory. The recent mobile applications open to the public that are associated with Hajj or Umrah are less communicative, very easy, have boundaries, do not provide smart features to help the user, and do not encounter the users’ targeted needs. Even though the useful information offered by prior studies in crowd behavioural management and the growth of many crowd monitoring technologies, a significant gap remains in the knowledge of crowd dynamics, particularly in unique scenarios like the Hajj pilgrimage. This is primarily because of the absence of complete, multimodal data that can be responsible for the composite interplay between emotional levels, individual cognition, crowd behaviour, and physiological conditions. This study presents a novel Smart Mobile Application for Monitoring and Classification of Pilgrim Activities in Hajj and Umrah Using Wearable Sensors (SMAMCPA-HUWS) method. The main objective of the SMAMCPA-HUWS method is to classify pilgrim activities based on fatigue levels and emotional status using wearable sensors for real-time monitoring. The SMAMCPA-HUWS method performs data preprocessing using min-max normalization to standardize input data, ensuring consistent analysis across varied sensor readings. The waterwheel plant algorithm (WWPA) model is utilized for feature subset selection to identify the most relevant features by reducing data dimensionality and enhancing processing efficiency. In addition, an ensemble of three classifiers such as bidirectional long short-term memory (BiLSTM), bidirectional temporal convolutional network (BiTCN), and conditional variational autoencoder (CVAE), is employed for classifying pilgrim activities. Finally, the multi-strategy improved crested porcupine optimizer (MICPO) method performs hyperparameter tuning for the three classifiers. The experimental study of the SMAMCPA-HUWS technique is performed by utilizing a benchmark dataset from the Kaggle repository. The performance validation of the SMAMCPA-HUWS technique portrayed a superior accuracy value of 97.78 % over existing approaches.