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Hany Hazfiza Manap
Universiti Teknologi MARA
Machine learningGaitGait analysisPattern recognitionNaive Bayes classifier
8Publications
5H-index
119Citations
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Publications 8
Newest
#1Hany Hazfiza Manap (UiTM: Universiti Teknologi MARA)H-Index: 5
#2Nooritawati Md Tahir (UiTM: Universiti Teknologi MARA)H-Index: 12
Last. Ramli Bin AbdullahH-Index: 10
view all 3 authors...
In this study the potential of decision tree (DT)specifically Classification and Regression Tree (CART) isinvestigated in classifying Parkinsononian gait pattern due to motor impairment. Firstly, gait features extracted duringwalking experiments namely basic spatiotemporal parameters,kinetic parameters and kinematic parameters by twelve PDpatients and twenty control group are acquired as database.Next, CART is chosen as classifier for identifying the distinct features between these two groups. I...
2 CitationsSource
#1Hany Hazfiza Manap (UiTM: Universiti Teknologi MARA)H-Index: 5
#2Nooritawati Md Tahir (UiTM: Universiti Teknologi MARA)H-Index: 12
This paper aims to identify gait patterns between normal healthy adults and PD patients based on three vertical ground reaction force gait features namely Maximum vertical heel contact (FZ1), Vertical minimum midstance force (FZ2) and FZ3 known as Maximum vertical push off force. Based on the gait data extracted, it was found that all three vertical GRF features are higher for normal subjects as compared to PD group. Conversely, longer time is taken by PD group to complete a stance phase. Moreov...
2 CitationsSource
#1Hany Hazfiza Manap (UiTM: Universiti Teknologi MARA)H-Index: 5
#2Nooritawati Md Tahir (UiTM: Universiti Teknologi MARA)H-Index: 12
Last. Ramli Bin AbdullahH-Index: 10
view all 3 authors...
The aim of this paper is to explore the potential of Principal Component Analysis (PCA) as feature selection in identifying gait pattern between Parkinsonian and healthy adult. Original gait database which consist of four basic spatiotemporal gait features, five kinetic gait features and twelve kinematic gait features are acquired from prior walking experiments of both Parkinson Disease (PD) and normal subjects. These features undergo normalization based on mean and standard deviation values fol...
5 CitationsSource
#1Hany Hazfiza Manap (UiTM: Universiti Teknologi MARA)H-Index: 5
#2Nooritawati Md Tahir (UiTM: Universiti Teknologi MARA)H-Index: 12
Last. Ramli Bin Abdullah (UKM: National University of Malaysia)H-Index: 10
view all 3 authors...
The aim of this study is to investigate the potential of Naive Bayes classifier as abnormal gait pattern detection specifically due to Parkinson Disease since it is vital to identify the best classifier that can perform competitively prior to implementation of a gait identification system. Moreover, the significant of SFS short for ‘sequential feature selection’ is experimental explored along with Naive Bayes capability as classifier. Initial findings showed that classification task based on Nai...
3 CitationsSource
#2Hany Hazfiza Manap (UiTM: Universiti Teknologi MARA)H-Index: 5
53 CitationsSource
Dec 1, 2011 in ISSPIT (International Symposium on Signal Processing and Information Technology)
#1Hany Hazfiza Manap (UiTM: Universiti Teknologi MARA)H-Index: 5
#2Nooritawati Md Tahir (UiTM: Universiti Teknologi MARA)H-Index: 12
Last. Ahmad Ihsan Mohd Yassin (UiTM: Universiti Teknologi MARA)H-Index: 7
view all 3 authors...
The aim of this study is to investigate the parameters that could be used to identify abnormal gait pattern in Parkinson's disease subjects during normal walking. Hence, three types of gait parameters namely basic, kinematic and kinetic are evaluated. Initial findings showed that the average mean of cadence, step length and walking speed for Parkinson's disease patients are lower than normal subjects, while the mean of stride time for Parkinson's disease patients are higher. Further, for kinemat...
32 CitationsSource
#1Hany Hazfiza Manap (UiTM: Universiti Teknologi MARA)H-Index: 5
#2Nooritawati Md Tahir (UiTM: Universiti Teknologi MARA)H-Index: 12
Last. Ahmad Ihsan Mohd Yassin (UiTM: Universiti Teknologi MARA)H-Index: 7
view all 3 authors...
Support Vector Machine is amongst the popular machine classifier due to its rigorous theory background and remarkable generalization performance. Hence, in this paper, the performance of SVM is evaluated to classify gait abnormalities due to Parkinson disease based on three kernels namely radial basis function, polynomial as well as linear. In addition, two types of normalization is applied to these gait data namely intra group norm and inter group norm. Initial findings showed that basic spatio...
8 CitationsSource
Jun 1, 2011 in ICSET (International Conference on System Engineering and Technology)
#1Hany Hazfiza Manap (UiTM: Universiti Teknologi MARA)H-Index: 5
#2Nooritawati Md Tahir (UiTM: Universiti Teknologi MARA)H-Index: 12
Last. Ramli Bin Abdullah (UKM: National University of Malaysia)H-Index: 10
view all 4 authors...
The aim of this study is to investigate the potential of Artificial Neural Network (ANN) as classifier for distinguishing gait pattern between normal healthy subjects and Parkinson Disease (PD) patients. Since it has been proven by various researchers that PD patients owned significant gait deviation compared to normal adults, hence this study are conducted and will mainly focused on the basic, kinetic and kinematic measurements of human gait. Initial findings attained confirm that the ANN class...
14 CitationsSource
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