The MS kinect image and depth sensors use for gait features detection

Procházka, Aleš; Schätz, Martin; Ťupa, Ondřej; Yadollahi, Mohammadreza; Vyšata, Oldřich; Walls, Michael L.

doi:10.1109/icip.2014.7025460

Cited by 24 publications

(15 citation statements)

References 18 publications

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“…Swing and double support proved to be more effective for measuring frailty [9,19]. Using a Kinect system in a clinical set up, Prochazka and colleagues [46] had earlier conducted an experiment to similarly extract gait parameters of patients suffering from Parkinson's disease for comparison with healthy age-matched people. A skeleton tracking algorithm was used for joint location.…”

Section: Analysis Of Gait Characteristics Using Vision Sensorsmentioning

confidence: 99%

Assessment of frailty: a survey of quantitative and clinical methods

Panhwar

Naghdy

et al. 2019

BMC biomed eng

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Background: Frailty assessment is a critical approach in assessing the health status of older people. The clinical tools deployed by geriatricians to assess frailty can be grouped into two categories; using a questionnaire-based method or analyzing the physical performance of the subject. In performance analysis, the time taken by a subject to complete a physical task such as walking over a specific distance, typically three meters, is measured. The questionnaire-based method is subjective, and the time-based performance analysis does not necessarily identify the kinematic characteristics of motion and their root causes. However, kinematic characteristics are crucial in measuring the degree of frailty. Results: The studies reviewed in this paper indicate that the quantitative analysis of activity of daily living, balance and gait are significant methods for assessing frailty in older people. Kinematic parameters (such as gait speed) and sensor-derived parameters are also strong markers of frailty. Seventeen gait parameters are found to be sensitive for discriminating various frailty levels. Gait velocity is the most significant parameter. Short term monitoring of daily activities is a more significant method for frailty assessment than is long term monitoring and can be implemented easily using clinical tests such as sit to stand or stand to sit. The risk of fall can be considered an outcome of frailty. Conclusion: Frailty is a multi-dimensional phenomenon that is defined by various domains; physical, social, psychological and environmental. The physical domain has proven to be essential in the objective determination of the degree of frailty in older people. The deployment of inertial sensor in clinical tests is an effective method for the objective assessment of frailty.

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Section: Analysis Of Gait Characteristics Using Vision Sensorsmentioning

confidence: 99%

Assessment of frailty: a survey of quantitative and clinical methods

Panhwar

Naghdy

et al. 2019

BMC biomed eng

View full text Add to dashboard Cite

show abstract

“…Further classification algorithms [56,60] based upon artificial neural networks with sigmoidal and radial basis functions provided similar classification accuracy. The advantage of the Bayesian classification is in its simplicity and efficiency as it can also outperform more sophisticated classification methods [55] including the decision tree and k-nearest neighbor [62].…”

Section: Resultsmentioning

confidence: 86%

“…Its reliability and accuracy are studied in many papers [18,25,60,40,61] now comparing its use and error bounds with more established techniques.…”

Section: Resultsmentioning

confidence: 99%

Bayesian classification and analysis of gait disorders using image and depth sensors of Microsoft Kinect

Procházka

Vyšata

Vališ

et al. 2015

Digital Signal Processing

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“…5, Image and depth sensors of this system enable to obtain image frames and detection of joints in the three-dimensional space. Digital filtering and data analysis was then used for detection of stride length and speed velocity [20] as main features used to detect Parkinson's disease. The spatial modelling using both complex video systems and much more simple Kinect device can be used both for diagnostical purposes and for rehabilitation.…”

Section: Gait Features Extraction Using Ms Kinectmentioning

confidence: 99%

History and biomedical applications of digital signal and image processing

Procházka

Vyšata

2014

2014 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)

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The paper presents in its initial part historical notes to the development of digital signal and image processing methods. The following case studies are devoted to their application in biomedicine and they include the use of computational intelligence in EEG and EMG signal processing, image segmentation and registration in orthodontia, the human-machine interaction and the three-dimensional modelling using MS Kinect in diagnostics and treatment of human motion disorders and neurological diseases. Associated comments include remarks and references to the development of modern computational tools, biosensors, wireless communication and data fusion used in assistive technologies and robotic systems. Mathematical tools common to all these applications form the next part of the paper that includes notes to spectral analysis, functional transforms, digital filtering, image enhancement, classification of multi-dimensional signal components and optimization using neural networks. Final remarks emphasize the interdisciplinary significance of the digital signal processing forming the integrating basis of many diverse research areas.Index Termshistory of digital signal and image processing, computational intelligence, digital signal processing methods, computer aided biomedical diagnostics and treatment, Microsoft Kinect three-dimensional modelling

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The MS kinect image and depth sensors use for gait features detection

Cited by 24 publications

References 18 publications

Assessment of frailty: a survey of quantitative and clinical methods

Assessment of frailty: a survey of quantitative and clinical methods

Bayesian classification and analysis of gait disorders using image and depth sensors of Microsoft Kinect

History and biomedical applications of digital signal and image processing

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