Gait signal classification tool utilizing Hilbert transform based feature extraction and logistic regression based classification

Vipani, Raj; Hore, Sambit; Basak, Souryadeep; Dutta, Saibal

doi:10.1109/icrcicn.2017.8234481

Cited by 1 publication

(2 citation statements)

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“…Many researchers applied machine learning algorithms such as logistic regression [11], support vector machine (SVM) [12], hidden markov model (HMM) [10] and clustering [9] to detect abnormal gait patterns with kinect skeletal data. These methods were related to model-based approach for human gait analysis.…”

Section: Related Workmentioning

confidence: 99%

“…Kinect (v2) offers five different data streams [19] out of which skeletal data stream are used extensively in clinical purposes [20,21] for its simplicity to track joint positions directly. Vipani et al [22] also used logistic regression for classification of healthy and pathological subjects. Kozlowska et al [23] used MARS model to investigate the trends in spatial and temporal gait parameters during treadmill walking.…”

Section: Related Workmentioning

confidence: 99%

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Human Gait Abnormality Detection Using Low Cost Sensor Technology

Jain

Nandy

2021

Communications in Computer and Information Science

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Detection of gait abnormality is becoming a growing concern in different neurological and musculoskeletal patients group including geriatric population. This paper addresses a method of detecting abnormal gait pattern using deep learning algorithms on depth Images. A low cost Microsoft Kinect v2 sensor is used for capturing the depth images of different subject's gait sequences. A histogram-based technique is applied on depth images to identify the range of depth values for the subject. This method generates segmented depth images and subsequently median filter is used on them to reduce unwanted information. Multiple 2D convolutional neural network (CNN) models are trained on segmented images for pathological gait detection. But these CNN models are only restricted to spatial features. Therefore, we consider 3D-CNN model to include both spatial and temporal features by stacking all the images from a single gait cycle. A statistical technique based on autocorrelation is applied on entire gait sequences for finding the gait period. We achieve a significant detection accuracy of 95% using 3D-CNN model. Performance evaluation of the proposed model is evaluated through standard statistical metrics.

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Section: Related Workmentioning

confidence: 99%