Analysis of Gait Features between Loaded and Normal Gait

Samudin, Norshuhada; Mohd-Isa, Wan-Noorshahida; Maul, Tomás; Lai, Weng Kin

doi:10.1109/sitis.2009.37

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…By tracking each person's lower limbs from a sideway's point of view, the displacement values of the lower limbs with respect to his/her body's vertical axis throughout the video have been chosen as that person's gait feature, as illustrated in Figure 1(a). This follows successful similar research on unique individuals [12][13][14][15]. The forward movement of the lower limbs gives positive degree values and the reverse is a negative degree.…”

Section: Data Collection and Feature Extractionsupporting

confidence: 80%

“…Due to the small sample size, only the linear discriminant (LDA) and k-nearest neighbor (k-NN) classifiers can be used. These classifiers are standard classifiers and have been performing well for small sample problem in previous gait recognition research [12,14,15]. The LDA finds discriminant functions that can give the maximum class separability via an eigen equation formed on a ratio of between class to within class scatter data.…”

Section: Matched-pair Classification With Jackknifingmentioning

confidence: 99%

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Matched-Pair Classification Analysis on Siblings' Gait Biometric Data

Mohd-Isa¹,

Abdullah²,

Eswaran³

et al. 2014

JCSCM

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This paper presents a supervised classification task on gait biometric of siblings' data sets. This task, which we refer to as matched-pair classification, evaluates the within pair differences in terms of the data set via jackknifing. A misclassification rate (MCR), which measures the percentage of misclassification of one sib compared to the other, gives an estimate on the potential uniqueness of gait for a person, particularly in twins. By this approach, the MCR values are mostly in the range of 90% for a data set of twins and in the range of 80% for a data set of non-twin siblings. When compared to the standard Leave-One-Out (LOO) classification, the MCR values of the proposed approach are higher than the LOO classification, which may suggest its potential use in machine learning with regard to biometric-based systems.

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Section: Data Collection and Feature Extractionsupporting

confidence: 80%

Section: Matched-pair Classification With Jackknifingmentioning

confidence: 99%

Matched-Pair Classification Analysis on Siblings' Gait Biometric Data

Mohd-Isa¹,

Abdullah²,

Eswaran³

et al. 2014

JCSCM

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show abstract

“…Gait patterns get affected by many factors such as illness [1], fatigue [2], emotions [3], cognitive and motor tasks [4]. In addition, gait is also prone to influence from external factors such as clothing, wearing shoes or carrying load [5].…”

Section: Introductionmentioning

confidence: 99%

Gait Activity Classification Using Multi-Modality Sensor Fusion: A Deep Learning Approach

Yunas

Ozanyan

2021

IEEE Sensors J.

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Floor Sensors (FS) are used to capture information from the force induced on the contact surface by feet during gait. On the other hand, the Ambulatory Inertial Sensors (AIS) are used to capture the velocity, acceleration and orientation of the body during different activities. In this paper, fusion of the stated modalities is performed to overcome the challenge of gait classification from wearable sensors on the lower portion of human body not in contact with ground as in FS. Deep learning models are utilized for the automatic feature extraction of the ground reaction force obtained from a set of 116 FS and body movements from AIS attached at 3 different locations of lower body, which is novel. Spatio-temporal information of disproportionate inputs obtained from the two modalities is balanced and fused within deep learning network layers whilst reserving the categorical content for each gait activity. Our approach of fusion compensates the degradation in spatio-temporal accuracies in individual modalities and makes the overall classification outcomes more accurate. Further assessment of multi-modality based results show significant improvements in f-scores using different deep learning models i.e., LSTM (99.90%), 2D-CNN (88.73%), 1D-CNN (94.97%) and ANN (89.33%) respectively.

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“…However gait patterns get affected by many factors such as illness, fatigue, emotions, cognitive and motor tasks [4]. In addition, gait is also prone to influence from external factors such as clothing, shoes or carrying load [5].…”

Section: Introductionmentioning

confidence: 99%

Multi-modality sensor fusion for gait classification using deep learning

Yunas

Alharthi

Ozanyan

2020

2020 IEEE Sensors Applications Symposium (SAS)

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Human gait has been acquired and studied through modalities such as video cameras, inertial sensors and floor sensors etc. Due to many environmental constraints such as illumination, noise, drifts over extended periods or restricted environment, the classification f-score of gait classifications is highly dependent on the usage scenario. This is addressed in this work by proposing sensor fusion of data obtained from 1) ambulatory inertial sensors (AIS) and 2) plastic optical fiber-based floor sensors (FS). Four gait activities are executed by 11 subjects on FS whilst wearing AIS. The proposed sensor fusion method achieves classification fscores of 88% using artificial neural network (ANN) and 91% using convolutional neural network (CNN) by learning the best data representations from both modalities.

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Analysis of Gait Features between Loaded and Normal Gait

Cited by 3 publications

References 12 publications

Matched-Pair Classification Analysis on Siblings' Gait Biometric Data

Matched-Pair Classification Analysis on Siblings' Gait Biometric Data

Gait Activity Classification Using Multi-Modality Sensor Fusion: A Deep Learning Approach

Multi-modality sensor fusion for gait classification using deep learning

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