Alternation of walking pattern decreases quality of life and may result in falls and injuries. Freezing of gait (FOG) in Parkinson's disease (PD) patients occurs occasionally and intermittently, appearing in a random, inexplicable manner. In order to detect typical disturbances during walking, we designed an expert system for automatic classification of various gait patterns. The proposedmethod is based on processing of data obtained from an inertial sensor mounted on shank. The algorithm separates normal from abnormal gait using Pearson's correlation and describes each stride by duration, shank displacement, and spectral components. A rule-based data processing classifies strides as normal, short short or very short short strides, FOG with tremor FOG or FOG with complete motor block FOG. The algorithm also distinguishes between straight and turning strides. In 12 PD patients, FOG and FOG were identified correctly in 100% of strides, while normal strides were recognized in 95% of cases.... Short and short strides were identified in about 84% and 78%. Turning strides were correctly identified in 88% of cases. The proposed method may be used as an expert system for detailed stride classification, providing warning for severe FOG episodes and near-fall situations.
TY - JOUR
AU - Đurić-Jovičić, Milica
AU - Jovičić, Nenad S.
AU - Radovanović, Saša M.
AU - Stanković, Iva
AU - Popović, Mirjana B.
AU - Kostić, Vladimir S.
PY - 2014
UR - http://rimi.imi.bg.ac.rs/handle/123456789/549
AB - Alternation of walking pattern decreases quality of life and may result in falls and injuries. Freezing of gait (FOG) in Parkinson's disease (PD) patients occurs occasionally and intermittently, appearing in a random, inexplicable manner. In order to detect typical disturbances during walking, we designed an expert system for automatic classification of various gait patterns. The proposedmethod is based on processing of data obtained from an inertial sensor mounted on shank. The algorithm separates normal from abnormal gait using Pearson's correlation and describes each stride by duration, shank displacement, and spectral components. A rule-based data processing classifies strides as normal, short short or very short short strides, FOG with tremor FOG or FOG with complete motor block FOG. The algorithm also distinguishes between straight and turning strides. In 12 PD patients, FOG and FOG were identified correctly in 100% of strides, while normal strides were recognized in 95% of cases. Short and short strides were identified in about 84% and 78%. Turning strides were correctly identified in 88% of cases. The proposed method may be used as an expert system for detailed stride classification, providing warning for severe FOG episodes and near-fall situations.
PB - IEEE-Inst Electrical Electronics Engineers Inc, Piscataway
T2 - IEEE Transactions on Neural Systems & Rehabilitation Engineering
T1 - Automatic Identification and Classification of Freezing of Gait Episodes in Parkinson's Disease Patients
EP - 694
IS - 3
SP - 685
VL - 22
DO - 10.1109/TNSRE.2013.2287241
UR - conv_3330
ER -
@article{
author = "Đurić-Jovičić, Milica and Jovičić, Nenad S. and Radovanović, Saša M. and Stanković, Iva and Popović, Mirjana B. and Kostić, Vladimir S.",
year = "2014",
abstract = "Alternation of walking pattern decreases quality of life and may result in falls and injuries. Freezing of gait (FOG) in Parkinson's disease (PD) patients occurs occasionally and intermittently, appearing in a random, inexplicable manner. In order to detect typical disturbances during walking, we designed an expert system for automatic classification of various gait patterns. The proposedmethod is based on processing of data obtained from an inertial sensor mounted on shank. The algorithm separates normal from abnormal gait using Pearson's correlation and describes each stride by duration, shank displacement, and spectral components. A rule-based data processing classifies strides as normal, short short or very short short strides, FOG with tremor FOG or FOG with complete motor block FOG. The algorithm also distinguishes between straight and turning strides. In 12 PD patients, FOG and FOG were identified correctly in 100% of strides, while normal strides were recognized in 95% of cases. Short and short strides were identified in about 84% and 78%. Turning strides were correctly identified in 88% of cases. The proposed method may be used as an expert system for detailed stride classification, providing warning for severe FOG episodes and near-fall situations.",
publisher = "IEEE-Inst Electrical Electronics Engineers Inc, Piscataway",
journal = "IEEE Transactions on Neural Systems & Rehabilitation Engineering",
title = "Automatic Identification and Classification of Freezing of Gait Episodes in Parkinson's Disease Patients",
pages = "694-685",
number = "3",
volume = "22",
doi = "10.1109/TNSRE.2013.2287241",
url = "conv_3330"
}
Đurić-Jovičić, M., Jovičić, N. S., Radovanović, S. M., Stanković, I., Popović, M. B.,& Kostić, V. S.. (2014). Automatic Identification and Classification of Freezing of Gait Episodes in Parkinson's Disease Patients. in IEEE Transactions on Neural Systems & Rehabilitation Engineering
IEEE-Inst Electrical Electronics Engineers Inc, Piscataway., 22(3), 685-694.
https://doi.org/10.1109/TNSRE.2013.2287241
conv_3330
Đurić-Jovičić M, Jovičić NS, Radovanović SM, Stanković I, Popović MB, Kostić VS. Automatic Identification and Classification of Freezing of Gait Episodes in Parkinson's Disease Patients. in IEEE Transactions on Neural Systems & Rehabilitation Engineering. 2014;22(3):685-694.
doi:10.1109/TNSRE.2013.2287241
conv_3330 .
Đurić-Jovičić, Milica, Jovičić, Nenad S., Radovanović, Saša M., Stanković, Iva, Popović, Mirjana B., Kostić, Vladimir S., "Automatic Identification and Classification of Freezing of Gait Episodes in Parkinson's Disease Patients" in IEEE Transactions on Neural Systems & Rehabilitation Engineering, 22, no. 3 (2014):685-694,
https://doi.org/10.1109/TNSRE.2013.2287241 .,
conv_3330 .
