Real-Time Pilgrims Management Using Wearable Physiological Sensors, Mobile Technology and Artificial Intelligence

Abstract

Annually, a huge number of pilgrims visit Mecca to perform Al Hajj ritual. Crowd management is critical in this occasion in order to avoid crowd disasters (e.g., stampede and suffocation). Recent studies stated that various factors, such as the environment, fatigue level, health condition and emotional status have a significant effect on crowded events. This calls for a need for an automated data analytics system that feeds event organizers with information about those factors on real-time, at least from a generalizable sample of crowd subjects, in which proactive crowd management decisions are made to reduce overall risks. This paper develops a novel methodology that fuses mobile GPS and physiological data of Hajj pilgrims collected through wearable sensors to train three classification models: (a) current performed Hajj activity, (b) fatigue level, and (c) emotional level. In a pilot experiment conducted against two subjects, promising results of a minimum of 75% accuracy levels were achieved for the activity recognition and fatigue level classifiers, whereas the emotional level classifier still requires further refinements.

Artificial intelligence, self-efficacy and engagement in religious tourism: evidence from Arbaeen pilgrimage

“…Our findings also appear to complement the findings of several recent studies conducted in the narrower context of religious tourism. For instance, Al-Shaery et al . (2022) devised an innovative approach that integrated wearable sensors and mobile GPS technology to amalgamate physiological data gathered from Hajj pilgrims, which allowed for the development of three distinct classification models: (1) for discerning current Hajj activities, (2) for assessing fatigue levels, and (3) for evaluating emotional states.…”

“…Our findings also appear to complement the findings of several recent studies conducted in the narrower context of religious tourism. For instance, Al-Shaery et al (2022)…”

Open Dataset for Predicting Pilgrim Activities for Crowd Management During Hajj Using Wearable Sensors

“…Location data, annotation data, subjects' demographic information and peripheral physiological signals were recorded using a custom built mobile application. For readers interested in getting more details about the collected data, the custom-built application and its development process, and sensors selection, we recommend reading our previously published work [37]. This study significantly expands upon our preliminary work [37], which primarily proposed a framework and included data from only two participants to test the feasibility of our approach.…”

“…For readers interested in getting more details about the collected data, the custom-built application and its development process, and sensors selection, we recommend reading our previously published work [37]. This study significantly expands upon our preliminary work [37], which primarily proposed a framework and included data from only two participants to test the feasibility of our approach. The current paper extends this by collecting a comprehensive dataset from 64 participants during the Umrah season.…”

Opportunities and Challenges Surrounding the Use of Wearable Sensor Bracelets for Infectious Disease Detection During Hajj: A Qualitative Interview Study (Preprint)

“…Various terms are used to refer to wearable sensor devices that capture and analyse real-time physical data in a non-intrusive manner. These terms encompass smartwatches [11,12]smart sensors [13], wearable activity trackers [14], wearable sensors [15], wearable physiological sensors [16], integrated biosensors [17], and sensor bracelets [18]. In the context of this research, the phrase "sensor bracelet" will be employed.…”