View source for Robotic First aid response

{{StudentProjectTemplate
|Summary=EMS Robot: assessing health state
|Keywords=robot first aid, injury localization, anomalous breathing recognition, bleeding recognition
|TimeFrame=2015/1/16-2015/6/30
|References=(first aid teleoperated robots)
http://www.uasvision.com/2014/10/29/ambulance-drone-with-integrated-defibrillator/
http://www.technologyreview.com/news/411865/a-robomedic-for-the-battlefield/
(fall detection example) Simin Wang, Salim Zabir, Bastian Leibe. Lying Pose Recognition for Elderly Fall Detection
(breathing recognition) Phil Corbishley and Esther Rodriguez-Villegas. 2008. Breathing Detection: Towards a Miniaturized, Wearable, Battery-Operated Monitoring System. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 55, NO. 1, JANUARY 2008
|Prerequisites=Requirement: some ability to work with software (installing libraries and writing code), and interest in robots, healthcare, or recognition
|Supervisor=Martin Cooney, Anita Sant'Anna
|Examiner=Antanas Verikas
|Author=Tianyi Zhang and Yuwei Zhao
|Level=Master
|Status=Ongoing
}}
Goal: The capability for a robot in a home or facility to be able to care for people in the event of a health emergency, by possessing some critical first aid skills to allow assessment of a person's state.

Motivation: 
Robots need to be useful.
One of the most useful things a robot can do is look after people's health and safety.
A quick and meaningful assessment of a person's state during first response to a possible emergency could help save lives and prevent anguish.

Challenge: the first thing which should be done is to assess a victim's state, but this is very difficult; 
e.g., for a person who has fallen and unresponsive:
  1) where did they hurt themselves?
  2) are they breathing normally?
  3) are they bleeding? 
Approach: the student will perform three steps
  1) obtain kinect data (skeleton and depth) of a human-shaped dummy falling in different ways
     create a recognition system (possibly using LIBSVM) to classify if the head has been hurt 
  2) record sound samples based on videos of "agonal" respiration, tachypnea (fast breathing), 
      and regular breathing from YouTube
    calculate mfcc features with htk
    create a recognition system (possibly using LIBSVM) to classify kind of breathing 
  3) use a robot (possibly Turtlebot) to drag a white glove over a dummy (red ink will symbolize 
       blood at some areas) to detect the presence/location of deadly bleeding 
Evaluation of system: accuracy or similar metric for how often the system detects head trauma, breathing, bleeding

Requirement: some ability to work with software (installing libraries and writing code), and interest in robots, healthcare, or recognition

Deliverable: an intelligent robot system which can assess a victim's state (thesis/report, code, video)