TY - GEN
T1 - A video/IMU hybrid system for movement estimation in infants
AU - Machireddy, Archana
AU - Van Santen, Jan
AU - Wilson, Jenny L.
AU - Myers, Julianne
AU - Hadders-Algra, Mijna
AU - Song, Xubo
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/13
Y1 - 2017/9/13
N2 - Cerebral palsy is a non-progressive neurological disorder occurring in early childhood affecting body movement and muscle control. Early identification can help improve outcome through therapy-based interventions. Absence of so-called 'fidgety movements' is a strong predictor of cerebral palsy. Currently, infant limb movements captured through either video cameras or accelerometers are analyzed to identify fidgety movements. However both modalities have their limitations. Video cameras do not have the high temporal resolution needed to capture subtle movements. Accelerometers have low spatial resolution and capture only relative movement. In order to overcome these limitations, we have developed a system to combine measurements from both camera and sensors to estimate the true underlying motion using extended Kalman filter. The estimated motion achieved 84% classification accuracy in identifying fidgety movements using Support Vector Machine.
AB - Cerebral palsy is a non-progressive neurological disorder occurring in early childhood affecting body movement and muscle control. Early identification can help improve outcome through therapy-based interventions. Absence of so-called 'fidgety movements' is a strong predictor of cerebral palsy. Currently, infant limb movements captured through either video cameras or accelerometers are analyzed to identify fidgety movements. However both modalities have their limitations. Video cameras do not have the high temporal resolution needed to capture subtle movements. Accelerometers have low spatial resolution and capture only relative movement. In order to overcome these limitations, we have developed a system to combine measurements from both camera and sensors to estimate the true underlying motion using extended Kalman filter. The estimated motion achieved 84% classification accuracy in identifying fidgety movements using Support Vector Machine.
UR - http://www.scopus.com/inward/record.url?scp=85032189054&partnerID=8YFLogxK
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U2 - 10.1109/EMBC.2017.8036928
DO - 10.1109/EMBC.2017.8036928
M3 - Conference contribution
C2 - 29059976
AN - SCOPUS:85032189054
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 730
EP - 733
BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Y2 - 11 July 2017 through 15 July 2017
ER -