Gait analysis and identification based on joint information using RGB-depth camera

İnce, Ömer Faruk; Ince, İbrahim Furkan; Park, Jang-Sik; Song, Jong Kwan

doi:10.1109/ecticon.2017.8096299

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…The depth camera extended the RGB camera. Thus the gait could be analyzed with new methods [5] . The disadvantage of camera systems is that they are stationary.…”

Section: Sensorsmentioning

confidence: 99%

Analyzing gait symmetry with automatically synchronized wearable sensors in daily life

Steinmetzer¹,

Wilberg²,

Bönninger³

et al. 2020

Microprocessors and Microsystems

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Gait deviations such as asymmetry are one of the characteristic symptoms of motor dysfunctions that contribute to the risk of falls. Our objective is to measure gait abnormalities such as asymmetry of the lower limbs in order to evaluate the diagnosis more objectively. For the measurement we use inertial measurement unit (IMU) sensors and force sensors, which are integrated in wristbands and insoles. To extend the battery life of wearable devices, we only save data of the activity gait within the wearables. Therefore we perform activity recognition with a smartphone. Using convolutional neural network (CNN) we achieved an accuracy of 94.7 % of the activity gait recognition. Before recording we synchronize the wearable sensors and reach a maximum latencies of 3 ms. Before the analysis of the symmetry we detect the strides by using a CNN with an accuracy of 98.8 %. For the symmetry evaluation we used dynamic time warping (DTW). The DTW enables us to calculate symmetry of the complete time series of human gait.

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“…The depth camera extended the RGB camera. Thus the gait could be analyzed with new methods [5] . The disadvantage of camera systems is that they are stationary.…”

Section: Sensorsmentioning

confidence: 99%

Analyzing gait symmetry with automatically synchronized wearable sensors in daily life

Steinmetzer¹,

Wilberg²,

Bönninger³

et al. 2020

Microprocessors and Microsystems

View full text Add to dashboard Cite

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“…Note that the vast majority of studies that use spatial-temporal gait features (for PD detection or elsewhere) employ Kinect systems [7][8][9][10], but other motion caption detectors also exist. For example, in [11], an 8-camera video motion analysis system measured reflective marker positions, while GRFs (ground reaction forces) were recorded simultaneously using two instrumented force platforms.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Detect Parkinson’s Disease by Looking at Gait Alterations

et al. 2022

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Parkinson’s disease (PD) is often detected only in later stages, when about 50% of nigrostriatal dopaminergic projections have already been lost. Thus, there is a need for biomarkers to monitor the earliest phases, especially for those that are at higher risk. In this work, we explore the use of machine learning methods to diagnose PD by analyzing gait alterations via an inertial sensors system that participants in the study wear while walking down a 15 m long corridor in three different scenarios. To achieve this goal, we have trained six well-known machine learning models: support vector machines, logistic regression, neural networks, k nearest neighbors, decision trees and random forest. We thoroughly explored several ways to mitigate the problems derived from the small amount of available data. We found that, while achieving accuracy rates of over 70% is quite common, the accuracy of the best model trained is only slightly above the 80% mark. This model has high precision and specificity (over 90%), but lower sensitivity (only 71%). We believe that these results are promising, especially given the size of the population sample (41 PD patients and 36 healthy controls), and that this research venue should be further explored.

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“…They were successful in creating a model that differentiated between a young and an elderly subject but couldn't differentiate between the two genders. Ince et al [11] studied the shape of the body as age determining construct by differentiating a child and an adult from their head to body proportion. Callisaya et al [7] discovered that the gender of an person alters the relationship between the age of the person and their gait as they found a substantial relationship between gender and various Gait attributes.…”

Section: Introductionmentioning

confidence: 99%

Gait based Age Estimation using LeNet-50 inspired Gait-Net

Islam¹,

Awasthi²,

Garg³

2021

IJAERS

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Gait is a behavioural biometric that does not require the subject's collaboration as it can be captured at a distance. The Gait-based age estimation has extensive applications in surveillance, customer age estimation in shopping centres and malls for business intelligence purposes and age-constrained access control to places like liquor shops, etc. In this paper, we propose Gait-Net, a LeNet-50 inspired age classification Convolutional Neural Network (CNN) for Gait-based age estimation. We propose the application of a heat map filter on each Gait Energy Image (GEI), for the enhancement of age differentiating features in the GEI, subsequently followed by the sequential age group and age estimation CNN models. We addressed the inherent class imbalance problem induced by the non-availability of sufficient data for the elderly subjects, by using the image augmentation technique. We evaluated our model on the OU-ISIR Large Population Gait Database and the results confirmed its efficiency. I.

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Gait analysis and identification based on joint information using RGB-depth camera

Cited by 7 publications

References 8 publications

Analyzing gait symmetry with automatically synchronized wearable sensors in daily life

Analyzing gait symmetry with automatically synchronized wearable sensors in daily life

A Machine Learning Approach to Detect Parkinson’s Disease by Looking at Gait Alterations

Gait based Age Estimation using LeNet-50 inspired Gait-Net

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