Wearable sensor data fusion for remote health assessment and fall detection
Won-Jae Yi, Oishee Sarkar, Sivisa Mathavan, Jafar Saniie · IEEE International Conference on Electro Information Technology · 2014
In this paper, we present the system architecture and design flow for remote user physiological data and movement detection using wearable sensor data fusion. Our design utilizes an Android smartphone to integrate and process multiple body sensor data to enhance the reliability for remote health diagnosis. Various sensor data such as body temperature, current geographical location, electrocardiography, body posture and fall detection data are collected using concurrent Bluetooth connections to the Android smartphone. Our Android application software is designed to handle real-time analysis of collected sensor data to determine current user status, such as instant heart beat rate, body orientation and possible fall recognition. With the help of the Internet connection on the Android smartphone, the system communicates with a remote server and a designated contact person to log sensor data, and to notify authorized professionals in case of an emergency situation. Our system is highly valuable for remote and mobile patient monitoring and diagnosis. This design flow can be extended to condition assessments in various environments and it is not limited to body temperature, current geographical location, electrocardiography, body posture and fall detection.