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Detection of Freezing of Gait and Feature Extraction of Human Gait with Linear Discriminant Analysis

Sjögren, Emma LU (2019) In Master's Theses in Mathematical Sciences FMSM01 20191
Mathematical Statistics
Abstract
Linear discriminant analysis (LDA) is performed in order to detect freezing of gait (FOG) in gait acceleration data. The data is collected from 10 healthy test persons, imitating Parkinson's disease (PD) gait, with the cellphone application Medoclinic. Feature spaces are constructed and reduced with principal component analysis (PCA), on which LDA is performed. The best performing model constructed with the LDA technique has a precision and recall of 0.6071 and 0.6800, respectively. This model projects its feature space on the five first principal components.

Extended versions of the algorithm written by Moore-Bächlin and later developed by Capecci et al., are constructed in order to detect the FOG phenomenon in gait acceleration.
The... (More)
Linear discriminant analysis (LDA) is performed in order to detect freezing of gait (FOG) in gait acceleration data. The data is collected from 10 healthy test persons, imitating Parkinson's disease (PD) gait, with the cellphone application Medoclinic. Feature spaces are constructed and reduced with principal component analysis (PCA), on which LDA is performed. The best performing model constructed with the LDA technique has a precision and recall of 0.6071 and 0.6800, respectively. This model projects its feature space on the five first principal components.

Extended versions of the algorithm written by Moore-Bächlin and later developed by Capecci et al., are constructed in order to detect the FOG phenomenon in gait acceleration.
The best performing model is a simplified version of the algorithm written by Moore et al., with both a precision and recall of 0.6154.
More characteristics of the gait data can be captured with LDA than what the algorithms of Moore-Bächlin and Capecci allow. (Less)
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author
Sjögren, Emma LU
supervisor
organization
course
FMSM01 20191
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Classification, Linear discriminant analysis, Freezing of gait, Parkinson's disease
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMS-3378-2019
ISSN
1404-6342
other publication id
2019:E47
language
English
id
8988907
date added to LUP
2019-10-08 14:02:10
date last changed
2019-10-08 14:02:10
@misc{8988907,
  abstract     = {{Linear discriminant analysis (LDA) is performed in order to detect freezing of gait (FOG) in gait acceleration data. The data is collected from 10 healthy test persons, imitating Parkinson's disease (PD) gait, with the cellphone application Medoclinic. Feature spaces are constructed and reduced with principal component analysis (PCA), on which LDA is performed. The best performing model constructed with the LDA technique has a precision and recall of 0.6071 and 0.6800, respectively. This model projects its feature space on the five first principal components.

Extended versions of the algorithm written by Moore-Bächlin and later developed by Capecci et al., are constructed in order to detect the FOG phenomenon in gait acceleration. 
The best performing model is a simplified version of the algorithm written by Moore et al., with both a precision and recall of 0.6154. 
More characteristics of the gait data can be captured with LDA than what the algorithms of Moore-Bächlin and Capecci allow.}},
  author       = {{Sjögren, Emma}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Detection of Freezing of Gait and Feature Extraction of Human Gait with Linear Discriminant Analysis}},
  year         = {{2019}},
}