A new paradigm of human gait analysis with Kinect

Nandy, Anup; Chakraborty, Pavan

doi:10.1109/ic3.2015.7346722

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…In another study in [ 118 ], Nandy and Chakraborty proposed a new approach of human gait analysis to find an intrinsic gait posture using the Kinect Xbox device. They used an NB classifier for classification and minimized segmentation errors using the automated background subtraction technique.…”

Section: Survey On State-of-the-artmentioning

confidence: 99%

In-Home Older Adults’ Activity Pattern Monitoring Using Depth Sensors: A Review

Momin

Sufian

Barman

et al. 2022

Sensors

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The global population is aging due to many factors, including longer life expectancy through better healthcare, changing diet, physical activity, etc. We are also witnessing various frequent epidemics as well as pandemics. The existing healthcare system has failed to deliver the care and support needed to our older adults (seniors) during these frequent outbreaks. Sophisticated sensor-based in-home care systems may offer an effective solution to this global crisis. The monitoring system is the key component of any in-home care system. The evidence indicates that they are more useful when implemented in a non-intrusive manner through different visual and audio sensors. Artificial Intelligence (AI) and Computer Vision (CV) techniques may be ideal for this purpose. Since the RGB imagery-based CV technique may compromise privacy, people often hesitate to utilize in-home care systems which use this technology. Depth, thermal, and audio-based CV techniques could be meaningful substitutes here. Due to the need to monitor larger areas, this review article presents a systematic discussion on the state-of-the-art using depth sensors as primary data-capturing techniques. We mainly focused on fall detection and other health-related physical patterns. As gait parameters may help to detect these activities, we also considered depth sensor-based gait parameters separately. The article provides discussions on the topic in relation to the terminology, reviews, a survey of popular datasets, and future scopes.

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Section: Survey On State-of-the-artmentioning

confidence: 99%

In-Home Older Adults’ Activity Pattern Monitoring Using Depth Sensors: A Review

Momin

Sufian

Barman

et al. 2022

Sensors

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show abstract

“…In addition, kinematic features were proven as less significant for human recognition. Other dynamic features for lower limb was evaluated as discussed in [43]. The angles of the joint of both right and left hip, left and right knee were extracted as feature vectors.…”

Section: B Human Gait Recognition With Kinectmentioning

confidence: 99%

“…Note that very few researchers explored feature selection and optimisation methods for identifying significant gait features except for work done by [26] and [33] using principal component analysis (PCA) with lower accuracy. A different feature selection method was used in the recognition of eight subjects, as reported in [43]. Features of joint angle of both right and left hip as well as right and left knee were extracted and used Fisher Discriminant Ratio (FDR) as feature selection.…”

Section: B Human Gait Recognition With Kinectmentioning

confidence: 99%

Human Gait Recognition Using Skeleton Joint Coordinates with Orthogonal Least Square and Locally Linear Embedded Techniques

Sahak

Tahir

Yassin

et al. 2019

International Journal of Simulation: Systems, Science &Amp; Technology

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This study investigated the skeleton points in the three dimensional (3D) space using Kinect sensor to be utilised as recognition of human based on their walking gait in frontal view. Firstly, walking gait of 30 subjects is captured using Kinect sensor. Next, all the twenty skeleton joints in the 3D space within one full gait cycle of each subjects are extracted as features. Further, the significant features amongst the skeleton joints are extracted using Orthogonal Least Square (OLS) technique followed by Locally Linear Embedded (LLE) as feature selection. The effectiveness of OLS as feature extraction along with LLE as feature selection is evaluated using Support Vector Machine as the machine classifier. Classification results attained for human gait recognition are 98.67% and 97% based on RBF and polynomial kernel respectively This proven that the significant gait features identified using OLS and LLE techniques are indeed suitable for gait recognition purpose.

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“…Initially, recognition of human gait using Kinect has been explored specifically in lateral view. For instance, nine subjects were required to walk from right to left, in front of the Kinect for eight times [7,8]. Then, 11 sets of static features consisting of height, length of legs, torso, both lower legs, both thighs, both upper arms and both forearms, and two sets of dynamic features; the length of step and the speed were extracted.…”

Section: Introductionmentioning

confidence: 99%

Human gait recognition using orthogonal least square as feature selection

Sahak

Tahir

Yassin

et al. 2020

IJEECS

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<span>This study investigates the potential gait features that are related to human recognition using orthogonal least square (OLS). Firstly, video of 30 subjects walking in oblique view was recorded using Kinect. Next, all 20 skeleton joints in 3D space were extracted and further selected using OLS. Additionally, SVM with linear, polynomial and radial basis function (RBF) kernel was used to classify the selected features. As consequences, OLS was proven to be able to identify the significant features using all three kernels of SVM since all recognition accuracy attained is higher as compared to the original gait features. Results attained showed that the highest recognition accuracy was 90.67% using 48 skeleton joint points for SVM with linear as kernel, followed by 46 skeleton joint points for SVM with RBF kernel namely 88.33% and accuracy of 86.33% for 38 skeleton joint points using polynomial kernel.</span>

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A new paradigm of human gait analysis with Kinect

Cited by 8 publications

References 7 publications

In-Home Older Adults’ Activity Pattern Monitoring Using Depth Sensors: A Review

In-Home Older Adults’ Activity Pattern Monitoring Using Depth Sensors: A Review

Human Gait Recognition Using Skeleton Joint Coordinates with Orthogonal Least Square and Locally Linear Embedded Techniques

Human gait recognition using orthogonal least square as feature selection

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