A Mixed Cloud-and-Embedded-based Approach with Machine Learning Towards the Development of a Fall Monitoring System
Document Type
Conference Proceeding
Publication Date
1-1-2023
Abstract
The ability to monitor falls, especially for the elderly, deems to be a crucial task to provide quality and timely healthcare response. However, there have been minimal efforts in centralizing such activity for efficient hospital management. This paper presents the development of a full-stack fall monitoring system with edge computing and machine learning technologies. Using a 3-axis accelerometer of a smartphone, motion data is collected and directly sent to an edge computing platform wherein a shallow neural network is directly trained to classify the motion data into positional states: stable, falling sidewards, falling flat, and standing up. A confusion matrix is presented to evaluate the performance of the neural network model, both in training and in real time. A cloud-based approach using ReactJS for front-end integration and Firebase's Cloud Firestore with NodeJS embedded capabilities for real-time data storage and embedded classification is implemented.
Recommended Citation
N. J. P. Limbaga, K. L. T. Mallari, N. R. O. Yeung and C. M. Oppus, "A Mixed Cloud-and-Embedded-based Approach with Machine Learning Towards the Development of a Fall Monitoring System," 2023 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), Phuket, Thailand, 2023, pp. 203-208, doi: 10.1109/ECTIDAMTNCON57770.2023.10139738.
