TY - GEN
T1 - Poster Abstract
T2 - 22nd ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2023
AU - Rahman, M. Arif
AU - Weerasinghe, Keshara
AU - Wijayasingha, Lahiru
AU - Alemzadeh, Homa
AU - Williams, Ronald D.
AU - Stankovic, John
N1 - Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/5/9
Y1 - 2023/5/9
N2 - Emergency Medical Services (EMS) providers use their hands extensively for the rescue operation and providing care to the patients in an EMS scene. Using smartwatch based sensor data, i.e., accelerometer, gyroscope, and magnetometer, we are developing SenseEMS, a system for hand operated EMS intervention detection and real-time monitoring. SenseEMS will use a hybrid deep neural network with appropriate real-time algorithms on the sensor data to detect multiple hand operated activities, i.e. CPR compressions, attaching defibrillation pads and breathing bags, and to provide quality assessment on different metrics of the activity, i.e., the rate and depth of CPR compressions. Our initial results for this ongoing research show promising accuracy. Preliminary survey with 31 anonymous EMS responders suggests that this automated system will be highly beneficial for real-scene application and EMS training.
AB - Emergency Medical Services (EMS) providers use their hands extensively for the rescue operation and providing care to the patients in an EMS scene. Using smartwatch based sensor data, i.e., accelerometer, gyroscope, and magnetometer, we are developing SenseEMS, a system for hand operated EMS intervention detection and real-time monitoring. SenseEMS will use a hybrid deep neural network with appropriate real-time algorithms on the sensor data to detect multiple hand operated activities, i.e. CPR compressions, attaching defibrillation pads and breathing bags, and to provide quality assessment on different metrics of the activity, i.e., the rate and depth of CPR compressions. Our initial results for this ongoing research show promising accuracy. Preliminary survey with 31 anonymous EMS responders suggests that this automated system will be highly beneficial for real-scene application and EMS training.
KW - Emergency Medical Services
KW - Hand Activity Detection
KW - Sensor Networks
UR - http://www.scopus.com/inward/record.url?scp=85160028040&partnerID=8YFLogxK
U2 - 10.1145/3583120.3589823
DO - 10.1145/3583120.3589823
M3 - Conference article
AN - SCOPUS:85160028040
T3 - IPSN 2023 - Proceedings of the 2023 22nd International Conference on Information Processing in Sensor Networks
SP - 310
EP - 311
BT - IPSN 2023 - Proceedings of the 2023 22nd International Conference on Information Processing in Sensor Networks
PB - Association for Computing Machinery, Inc
Y2 - 9 May 2023 through 12 May 2023
ER -