TY - GEN
T1 - Postural transition detection using a wireless sensor activity monitoring system
AU - LeMay, Richelle
AU - Choi, Sangil
AU - Youn, Jong Hoon
AU - Newstorm, Jay
PY - 2013
Y1 - 2013
N2 - Mobility health is an important aspect of the overall health status of a person. Many tests exist that determine the mobility health of a subject, but there are several issues associated with these tests, such as human error. Much work is being done to develop a mobility classification system which consolidates these tests, and circumvents the associated issues. Even so, many of these systems in development are complicated and lack the calculation of important postural transition measurements. The goal of this project was to remove the errors associated with current mobility tests, and to make the system as simple and energy-efficient as possible. In addition, we wanted this system to be able to detect with accuracy of over 90% six mobility states in addition to postural transition information. These goals were accomplished by using a waist-mounted triaxial accelerometer that processed data on-board using a well-developed classification algorithm.
AB - Mobility health is an important aspect of the overall health status of a person. Many tests exist that determine the mobility health of a subject, but there are several issues associated with these tests, such as human error. Much work is being done to develop a mobility classification system which consolidates these tests, and circumvents the associated issues. Even so, many of these systems in development are complicated and lack the calculation of important postural transition measurements. The goal of this project was to remove the errors associated with current mobility tests, and to make the system as simple and energy-efficient as possible. In addition, we wanted this system to be able to detect with accuracy of over 90% six mobility states in addition to postural transition information. These goals were accomplished by using a waist-mounted triaxial accelerometer that processed data on-board using a well-developed classification algorithm.
KW - activity classification
KW - mobility monitoring
KW - sensor networks
UR - http://www.scopus.com/inward/record.url?scp=84883387809&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883387809&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-38027-3_42
DO - 10.1007/978-3-642-38027-3_42
M3 - Conference contribution
AN - SCOPUS:84883387809
SN - 9783642380266
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 393
EP - 402
BT - Grid and Pervasive Computing - 8th International Conference, GPC 2013 and Colocated Workshops, Proceedings
T2 - 8th International Conference on Grid and Pervasive Computing, GPC 2013
Y2 - 9 May 2013 through 11 May 2013
ER -