A low-cost video-based tool for clinical gait analysis

Soda, Paolo; Carta, Alfonso; Formica, Domenico; Guglielmelli, Eugenio

doi:10.1109/iembs.2009.5333623

Cited by 21 publications

(18 citation statements)

References 14 publications

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“…In the peripheral motor gait disorder, 8 studies were discussed on gait disorder with symptoms of motor control of abnormal gait [10], neuropathy of ankle dossal-planter-flexion [11], motor control of weight bearing [12], neuropathy of lower limb joint [13,14], motor control of cerebral palsy [15], arthritis of trunk control in children [16], and neuropathy of pelvis [17]. In the spasticity gait disorder, 7 studies were discussed on gait disorder with symptoms of hemiplegia of the leg movement [18,19], ankle and subtalar joint [20], lower limb [21], feet [22], intralimb [23], and paraplegia of the stiff knee [24]. In the parkinsonism gait disorder, 2 studies were discussed on gait disorder with symptoms of parkinsonism of the stride (length, duration, velocity) [25], and of the posture and gait cycle [26].…”

Section: A Marker-based Technique In Rehabilitation Systemsmentioning

confidence: 99%

Vision-based motion tracking rehabilitation system for gait disorder

Ali

Sundaraj

Ahmad

et al. 2012

2012 IEEE International Conference on Control System, Computing and Engineering

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Aim of the present study was to review the articles for the vision-based motion tracking, usually applied on the rehabilitation systems of gait disorder. Based on our ultimate goal, we searched several articles, available from the online database of some journals and conferences, published in the English language between January, 1993 and June, 2012. We especially searched for the phrases, such as "vision sensor", "motion tracking", "gait disorder", and "rehabilitation" in the title, abstract, or keywords sections. Besides, some common synonyms of these phrases, along with the logical words, like "and", "or", and "not" were also used in the article searching procedure. From among the 37 articles found, this review identified 25 articles, which utilized the marker during video recording for motion tracking of the subject; whereas about 10 articles did not utilize any marker, 1 article utilized both markerbased and markerless, and another 1 articles were unrelated to the motion tracking but related to the gait disorder. Moreover, through this literature, we presented a strong evidence that enabled the analysis of motion tracking by the help of vision sensor for the rehabilitation systems of gait disorder. Hence, we believe that the information contained in this review will remarkably assist and guide the progress of the vision-based motion tracking, applied on the rehabilitation systems of gait disorder.

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Section: A Marker-based Technique In Rehabilitation Systemsmentioning

confidence: 99%

Vision-based motion tracking rehabilitation system for gait disorder

Ali

Sundaraj

Ahmad

et al. 2012

2012 IEEE International Conference on Control System, Computing and Engineering

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“…Motivated by cost and providing a convenient option to patients and health services, research and development on cost effective and portable systems has emerged. Related range sensor and home-video based systems, which cost about £700, such as [9], [10] and [11], that build on the work of [12], with Pro-Trainer motion analysis software (Sports Motion, Inc., Cardiff, CA), offer gait analysis outside the gait laboratory, e.g., in local clinics and at homes. Similar to other range sensor and home-video based gait analysis systems [2,[13][14][15][16][17][18][19][20][21] and Inertial Measurement Unit (IMU) based gait analysis systems [22][23][24][25][26], the gait parameters obtained after data processing can be sent to physiatrists for clinical consultation, indicating the potential for tele-rehabilitation [27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by the opportunity offered by [9], [11] for costeffective tele-rehabilitation, in conference versions of this work [33,34], we designed a portable, single-camera gait analysis system, that tracks bulls-eye markers (see Figure 1(a)) attached to the patient joints and displays the calculated joint angles. The system consists of marker tracking, based on a Kalman filter [35,36] and Structural-Similarity [37], and autonomous knee angle calculation.…”

Section: Introductionmentioning

confidence: 99%

Automation enhancement and accuracy investigation of a portable single‐camera gait analysis system

Yang

Ugbolue

McNicol

et al. 2019

IET Science, Measurement & Technology

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While optical motion analysis systems can provide high-fidelity gait parameters, they are usually impractical for local clinics and home use, due to high cost, requirement for large space, and lack of portability. In this study, we focus on a cost-effective and portable, single-camera gait analysis solution, based on video acquisition with calibration, autonomous detection of frames-ofinterest, Kalman-filter+Structural-Similarity-based marker tracking, and autonomous knee angle calculation. The proposed system is tested using 15 participants, including 10 stroke patients and 5 healthy volunteers. The evaluation of autonomous frames-ofinterest detection shows only 0.2% difference between the frame number of the detected frame compared to the frame number of the manually labelled ground truth frame, and thus can replace manual labelling. The system is validated against a gold standard optical motion analysis system, using knee angle accuracy as metric of assessment. The accuracy investigation between the RGBand the grayscale-video marker tracking schemes shows that the grayscale system suffers from negligible accuracy loss with a significant processing speed advantage. Experimental results demonstrate that the proposed system can automatically estimate the knee angle, with R-squared value larger than 0.95 and Bland-Altman plot results smaller than 3.0127 degrees mean error. r row centre coordinate of the marker t duration of one frame a candidate block b marker-template ∈ set membership,

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“…1 However, evidence-based practice demands that improvements in motion as a result of device use be documented. 6,7 All that is required is a camera, a tripod, a computer or mobile device, relatively inexpensive (and sometimes free) digitizing software, a recording area, a leveling device, and markers. Unfortunately, the criterion standard approach of computerized three-dimensional (3D) motion analysis is too expensive and time-consuming for routine use in clinical practice because it requires specialized equipment and trained personnel.…”

mentioning

confidence: 99%

Capturing Quality Clinical Videos for Two-Dimensional Motion Analysis

Fatone

Stine²

2015

JPO Journal of Prosthetics and Orthotics

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Introduction: Motion analysis, particularly two-dimensional (2D) analysis, can be a useful tool for prosthetists and orthotists in their clinical practice as it provides a mechanism by which improvements in motion as a result of device use may be documented and quantified. However, usefulness of video-based 2D analysis is contingent on recording quality clinical videos using a standardized protocol. Materials and Methods: The purpose of this work was to provide practical tips for taking quality clinical videos for use in 2D motion analysis intended to enhance evidence-based practice. Conditions that affect the quality of the video include illumination, image size, camera position, steadiness of the camera, and marker placement on the patient. A bright image is needed to track the joints or segments. The camera needs to be held steady, level, and placed perpendicular to the image field. The camera should be as close as possible to the image field while maintaining sufficient space to view the activity. Correct marker placement on the joints of interest improves measurement of joint angles. Results and Conclusions: Good quality videos as part of 2D motion analysis can result in data that are comparable to that of instrumented motion analysis systems when used for 2D analysis, enhancing clinician's evidence-based practice. Care must be taken during the setup process and during data capture to ensure that the resulting video is recorded in a manner that is appropriate with respect to patient privacy concerns. (J Prosthet Orthot. 2015;27:27Y32.) KEY INDEXING TERMS: 2D motion analysis, clinical videos, gait analysis, kinematics Disclosure: The authors declare no conflict of interest.

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A low-cost video-based tool for clinical gait analysis

Cited by 21 publications

References 14 publications

Vision-based motion tracking rehabilitation system for gait disorder

Vision-based motion tracking rehabilitation system for gait disorder

Automation enhancement and accuracy investigation of a portable single‐camera gait analysis system

Capturing Quality Clinical Videos for Two-Dimensional Motion Analysis

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