Edge Computing Transformers for Fall Detection in Older Adults

Int J Neural Syst. 2024 Mar 16:2450026. doi: 10.1142/S0129065724500266. Online ahead of print.ABSTRACTThe global trend of increasing life expectancy introduces new challenges with far-reaching implications. Among these, the risk of falls among older adults is particularly significant, affecting individual health and the quality of life, and placing an additional burden on healthcare systems. Existing fall detection systems often have limitations, including delays due to continuous server communication, high false-positive rates, low adoption rates due to wearability and comfort issues, and high costs. In response to these challenges, this work presents a reliable, wearable, and cost-effective fall detection system. The proposed system consists of a fit-for-purpose device, with an embedded algorithm and an Inertial Measurement Unit (IMU), enabling real-time fall detection. The algorithm combines a Threshold-Based Algorithm (TBA) and a neural network with low number of parameters based on a Transformer architecture. This system demonstrates notable performance with 95.29% accuracy, 93.68% specificity, and 96.66% sensitivity, while only using a 0.38% of the trainable parameters used by the other approach.PMID:38490957 | DOI:10.1142/S0129065724500266

Source: International Journal of Neural Systems - March 15, 2024 Category: Neurology Authors: Jes ús Fernandez-Bermejo Jes ús Martinez-Del-Rincon Javier Dorado Xavier Del Toro Mar ía J Santofimia Juan C Lopez Source Type: research