Machine Learning-Integrated Usability Evaluation and Monitoring of Human Activities for Individuals With Special Needs During Hajj and Umrah - Ghadah Naif Alwakid (IEEE Access)
Human activity recognition (HAR) is an important aspect of the safety and accessibility of individuals with disabilities. This is especially essential during large-scale events like Hajj and Umrah, which attract millions of participants each year. These gatherings pose significant challenges for people with disabilities and affect both their mobility and security. To address these issues, this study introduces a new approach to improve usability and tracking for disabled pilgrims performing the two holy pilgrimages: Hajj and Umrah, which are performed annually by millions of Muslims. These acts of worship are still very challenging for the mobility, security, and accessibility of people with special needs. Using clustering, anomaly detection, and predictive modeling, it was intended to enhance the safety and security of sensitive participants. Using the K-Means algorithm and the Elbow Method as the initial indicators to classify the clusters, we reveal four clusters that relate to different human activities that are further visualized through principal component analysis (PCA). Activities are clustered based on the behavioral patterns observed among participants, followed by the performance of anomaly detection. The analysis reveals that the string ‘WALKING_DOWNSTAIRS’ represents a sensitive and rare event in the training data set with a count of 36, which indicates possible walking disabilities that human beings may experience. The proposed study used two machine learning models, i.e., random forest and sequential neural networks, both with 93% accuracy. The performance was fairly accurate, with RF scoring 1 for ‘LAYING’ and SNN scoring 0.99 for ‘WALKING_UPSTAIRS’. The usefulness of this study is to improve the safety of the disabled participants in performing Hajj and Umrah. Besides extending the knowledge base and technical development of HAR and machine learning, this work has implications for specific real-life problems of accessibility and security in large-scale religious gatherings.