TY - GEN
T1 - Deep vision system for clinical gait analysis in and out of hospital
AU - Yu, Hosang
AU - Kang, Kyunghun
AU - Jeong, Sungmoon
AU - Park, Jaechan
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - To follow-up Parkinson’s disease (PD) progress, clinical gait analysis is performed with the precise measuring equipments (e.g. IMU, electric walkway, etc.). However, the existing gait analysis methods have a limitation such that patients must visit a certain space in hospital for the checkup. For clinical gait analysis in and out of hospital, we propose a baseline model of ‘deepvision’ system, which can estimate 15 clinical gait parameters measured from electric walkway named GAITRite. We constructed 3D convolution layers which have skip connections to grasp spatio-temporal characteristics of the walking behavior with an effective manner. Afterwards, we validated the method with scripted walking videos, and achieved the following results: error range of temporal and spatial parameters as 32–71 ms, 1.6–6.7 cm respectively, and error for cadence, velocity and functional ambulation profile as 7.0 steps/min, 4.1 cm/min, and 4.9 points respectively.
AB - To follow-up Parkinson’s disease (PD) progress, clinical gait analysis is performed with the precise measuring equipments (e.g. IMU, electric walkway, etc.). However, the existing gait analysis methods have a limitation such that patients must visit a certain space in hospital for the checkup. For clinical gait analysis in and out of hospital, we propose a baseline model of ‘deepvision’ system, which can estimate 15 clinical gait parameters measured from electric walkway named GAITRite. We constructed 3D convolution layers which have skip connections to grasp spatio-temporal characteristics of the walking behavior with an effective manner. Afterwards, we validated the method with scripted walking videos, and achieved the following results: error range of temporal and spatial parameters as 32–71 ms, 1.6–6.7 cm respectively, and error for cadence, velocity and functional ambulation profile as 7.0 steps/min, 4.1 cm/min, and 4.9 points respectively.
KW - Clinical gait analysis
KW - Contactless visual monitoring
KW - Parkinson’s disease
UR - http://www.scopus.com/inward/record.url?scp=85089612102&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-36808-1_69
DO - 10.1007/978-3-030-36808-1_69
M3 - Conference contribution
AN - SCOPUS:85089612102
SN - 9783030368074
T3 - Communications in Computer and Information Science
SP - 633
EP - 642
BT - Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings
A2 - Gedeon, Tom
A2 - Wong, Kok Wai
A2 - Lee, Minho
PB - Springer
T2 - 26th International Conference on Neural Information Processing, ICONIP 2019
Y2 - 12 December 2019 through 15 December 2019
ER -