Data Acquisition System for a Wearable-Based Fall Prevention

Abstract

: Reliable ways to treat and monitor patients remotely have been researched and proposed by numerous people. Many of these propositions are under the wearable category due to it usually not requiring deep knowledge to be handled and its durability. Among the many applicable ways, fall monitoring has gained importance as the world population ages and countries aim to increase the quality of life. For it to be possible, there are many ways such as analyzing muscle response, body position, or brain activities, but for most of them, the result ends up being expensive and or inaccurate. With this in mind, this paper brings the development of an acquisition system for electromyography, electrocardiography, body position and temperature. The acquired data is transmitted to the smartphone through Bluetooth Low Energy (BLE) and then sent to a secure cloud to be provided to the physician. In future works, artificial intelligence codes will analyze the data patterns to predict fall occurrences and establish functional electrical stimulation (FES) routines to prevent falls and or treat the patients according to their necessities.