Real-time running workouts monitoring using Cloud–Edge computing

• Published in: Neural computing & applications Vol. 35; no. 19; pp. 13803 - 13822
• Main Authors: Avram, Maria-Ruxandra, Pop, Florin
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2023; Springer Nature B.V
• Subjects: Artificial Intelligence; Cloud computing; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Edge computing; Feedback; Haptics; Heart rate; Image Processing and Computer Vision; Physical fitness; Probability and Statistics in Computer Science; Real time; S.I.: IoT-based Health Monitoring System; Smartwatches; Social distancing; Special Issue on Neural Computing for IOT based Intelligent Healthcare Systems; Wearable computers; Wrist; Scalability; IoT-based monitoring; Cloud technologies; Edge devices; Running coach; Running training; Remote monitoring
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-021-06675-3

In a world of ever-growing technology where smartwatches are becoming more and more widespread, we introduced an application that can attach a personal running coach on one’s wrist. We developed a highly scalable model that takes input from real coaches, conveys it into a running training on a watch, analyzes the running performance, and gives real-time text and haptic feedback based on it. Using cloud technologies, we came up with a solution that provides end-to-end connectivity between a smartwatch and a coach or user. We empower people to track down their workouts and physical profiles easily and to connect with their trainers using a standard, interactive dashboard. The solution is presented in this paper.