TY - GEN
T1 - Wearable sensor system to measure velocity adaptive variability for continuous human mobility monitoring
AU - Youn, Ik Hyun
AU - Youn, Jong Hoon
AU - Patlolla, Abhilash
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/28
Y1 - 2015/9/28
N2 - Variability of human mobility has become an important identifier for the assessment of human motor performance. For example, abnormally increased variability during movement has shown to correlate with higher falling risk. Various gait parameters, such as step length, stride time, and joint angle velocity have been studied to reveal the link between variability and movement impairment under the hospital or laboratory environments. Although the accuracy of the measurements with the laboratory equipment is relatively high and reliable, spatiotemporal limitation and lack of representativeness of ordinary mobility characteristics of a subject have been major challenges of previous approaches. This study proposes the velocity adaptive variability parameter to overcome the listed limitations. Among several major factors that affect level of variability, such as kinematic, pathological, and physiological changes, the parameter specifically absorbs the impact of varied walking speeds to get an instinct variability signature from the same subject regardless of walking speed. Since we utilize a single inertial sensor to measure variability of the subject, the approach will enable us to continuously monitor mobility-related problems in a free-living environment. The proof of concept experiment has shown practical advantages of our approach, and we also expect that the adaptive variability can be applied to future continuous mobility monitoring research.
AB - Variability of human mobility has become an important identifier for the assessment of human motor performance. For example, abnormally increased variability during movement has shown to correlate with higher falling risk. Various gait parameters, such as step length, stride time, and joint angle velocity have been studied to reveal the link between variability and movement impairment under the hospital or laboratory environments. Although the accuracy of the measurements with the laboratory equipment is relatively high and reliable, spatiotemporal limitation and lack of representativeness of ordinary mobility characteristics of a subject have been major challenges of previous approaches. This study proposes the velocity adaptive variability parameter to overcome the listed limitations. Among several major factors that affect level of variability, such as kinematic, pathological, and physiological changes, the parameter specifically absorbs the impact of varied walking speeds to get an instinct variability signature from the same subject regardless of walking speed. Since we utilize a single inertial sensor to measure variability of the subject, the approach will enable us to continuously monitor mobility-related problems in a free-living environment. The proof of concept experiment has shown practical advantages of our approach, and we also expect that the adaptive variability can be applied to future continuous mobility monitoring research.
KW - Continuous monitoring
KW - Human mobility
KW - Variability
KW - Wearable sensors
UR - http://www.scopus.com/inward/record.url?scp=84946897118&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84946897118&partnerID=8YFLogxK
U2 - 10.1109/CSNT.2015.289
DO - 10.1109/CSNT.2015.289
M3 - Conference contribution
AN - SCOPUS:84946897118
T3 - Proceedings - 2015 5th International Conference on Communication Systems and Network Technologies, CSNT 2015
SP - 303
EP - 307
BT - Proceedings - 2015 5th International Conference on Communication Systems and Network Technologies, CSNT 2015
A2 - Tomar, Geetam Singh
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Communication Systems and Network Technologies, CSNT 2015
Y2 - 4 April 2015 through 6 April 2015
ER -