Adam Renner, Robert L. Williams, M. McCartney, Brandon Harmon, Lucas Boswell, Subhashini Ganapathy, Kushal Abhyankar, J. West, N. Weiner, Nathan Weinle
{"title":"RIPPLE: Scalable medical telemetry system for supporting combat rescue","authors":"Adam Renner, Robert L. Williams, M. McCartney, Brandon Harmon, Lucas Boswell, Subhashini Ganapathy, Kushal Abhyankar, J. West, N. Weiner, Nathan Weinle","doi":"10.1109/NAECON.2014.7045807","DOIUrl":null,"url":null,"abstract":"Emergency response operations would universally benefit by extending telemedicine to the most difficult and challenging environments. For example, the Air Force Pararescue Jumpers (PJ) and Combat Rescue Officers (CRO) perform rescue and life-saving measure in austere environments. Currently, Bluetooth® aided pen-and-paper systems are employed to collect and store medical data, from the time it is sensed to its dissemination. This is proving to be tedious and non-scalable, especially when the number of casualties is larger than the number of responders in a given mission. Pararescue Jumpers, Combat Rescue Officers and similar medical rescue agencies are seeking medical vital sign sensors and telemetry solutions for mass casualty responses in which a small team of medical rescuers must be able to rescue and sustain the life of multiple casualties in critical condition. Project Ripple, to be described in this paper, is meant to create a Medical Body Area Network (MBAN) of sensors to assist in triage and general physiological data collection in a disaster scenario. The system is demonstrates an improved alternative to existing Bluetooth® and pen-and-paper systems by streamlining the processes of data collection, storage, transfer, and visualization. Low-power, wireless devices that utilized open standards makeup the sensor network while custom mobile applications were used for the visualization of the sensor data. Also, flexible and generic sensor fusion architecture is being explored.","PeriodicalId":318539,"journal":{"name":"NAECON 2014 - IEEE National Aerospace and Electronics Conference","volume":"130 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NAECON 2014 - IEEE National Aerospace and Electronics Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAECON.2014.7045807","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
Emergency response operations would universally benefit by extending telemedicine to the most difficult and challenging environments. For example, the Air Force Pararescue Jumpers (PJ) and Combat Rescue Officers (CRO) perform rescue and life-saving measure in austere environments. Currently, Bluetooth® aided pen-and-paper systems are employed to collect and store medical data, from the time it is sensed to its dissemination. This is proving to be tedious and non-scalable, especially when the number of casualties is larger than the number of responders in a given mission. Pararescue Jumpers, Combat Rescue Officers and similar medical rescue agencies are seeking medical vital sign sensors and telemetry solutions for mass casualty responses in which a small team of medical rescuers must be able to rescue and sustain the life of multiple casualties in critical condition. Project Ripple, to be described in this paper, is meant to create a Medical Body Area Network (MBAN) of sensors to assist in triage and general physiological data collection in a disaster scenario. The system is demonstrates an improved alternative to existing Bluetooth® and pen-and-paper systems by streamlining the processes of data collection, storage, transfer, and visualization. Low-power, wireless devices that utilized open standards makeup the sensor network while custom mobile applications were used for the visualization of the sensor data. Also, flexible and generic sensor fusion architecture is being explored.