Detecting Anatomical Leg Length Discrepancy Using the Plug-in-Gait Model

Khamis, Sam; Danino, Barry; Springer, Shmuel; Ovadia, Dror; Carmeli, Eli

doi:10.3390/app7090926

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…Body-segment length measurements were within 1.56 (SD = 2.77) cm of ground-truth values. This is comparable to leg-length measurements used for clinical decision making, where the difference between Vicon limb lengths and X-Ray bone measurements was 0.98 (SD = 0.55) cm [35]. Stride parameters and joint angles were analyzed to determine the Smart Hallway's capability for human motion analysis.…”

Section: Discussionmentioning

confidence: 54%

Video-Based Deep Learning Approach for 3D Human Movement Analysis in Institutional Hallways: A Smart Hallway

2021

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New artificial intelligence- (AI) based marker-less motion capture models provide a basis for quantitative movement analysis within healthcare and eldercare institutions, increasing clinician access to quantitative movement data and improving decision making. This research modelled, simulated, designed, and implemented a novel marker-less AI motion-analysis approach for institutional hallways, a Smart Hallway. Computer simulations were used to develop a system configuration with four ceiling-mounted cameras. After implementing camera synchronization and calibration methods, OpenPose was used to generate body keypoints for each frame. OpenPose BODY25 generated 2D keypoints, and 3D keypoints were calculated and postprocessed to extract outcome measures. The system was validated by comparing ground-truth body-segment length measurements to calculated body-segment lengths and ground-truth foot events to foot events detected using the system. Body-segment length measurements were within 1.56 (SD = 2.77) cm and foot-event detection was within four frames (67 ms), with an absolute error of three frames (50 ms) from ground-truth foot event labels. This Smart Hallway delivers stride parameters, limb angles, and limb measurements to aid in clinical decision making, providing relevant information without user intervention for data extraction, thereby increasing access to high-quality gait analysis for healthcare and eldercare institutions.

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Section: Discussionmentioning

confidence: 54%

Video-Based Deep Learning Approach for 3D Human Movement Analysis in Institutional Hallways: A Smart Hallway

2021

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“…Inequality between lower extremities is known as Leg Length Discrepancy (LLD). LLD affects the patients' quality of life due to several functional activities such as posture, balance [1,2], walking [3][4][5] and running [6] being disrupted.…”

Section: Introductionmentioning

confidence: 99%

“…The most precise LLD measuring methods are radiography and CT. However, these methods involve subjecting patients to radiation [3,10].…”

Section: Introductionmentioning

confidence: 99%

System for Evaluation and Compensation of Leg Length Discrepancy for Human Body Balancing

Vrhovski¹,

Obrovac²,

Nižetić³

et al. 2019

Applied Sciences

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Leg Length Discrepancy (LLD) causes a shift of the Center of Mass (CoM) of the human body, as well as an asymmetry in load distribution on the lower extremities. Existing LLD evaluation methods do not take into account this shift in the human body’s CoM. In this paper, a methodology and mechatronic system for the Evaluation and Compensation of LLD for Human Body Balancing are described. The human body’s CoM is measured with two force plates located on two parallel manipulators. Since persons with LLD experience a shift in their CoM, by raising the force plate that is under the shorter leg, the human body can be balanced. For this purpose, the Human Body Balancing Algorithm (HBBA) was proposed and developed. By running the HBBA, the height difference between the force plates under the left and right leg can be measured, which then represents the LLD evaluation. Based on this evaluation, it is possible to design and make a shoe insole which compensates the influence of LLD with the goal of equalizing the load on the legs. A virtual mathematical model of the system was created and the simulation results of the HBBA are presented. The mechatronic system, developed and used to conduct experiments and measurements, is described in detail.

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Anatomical leg length discrepancy in children: Can it be accurately determined using 3-D motion capturing?

Böhm,

Reinhold,

Dussa

2024

Gait & Posture

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Detecting Anatomical Leg Length Discrepancy Using the Plug-in-Gait Model

Cited by 4 publications

References 30 publications

Video-Based Deep Learning Approach for 3D Human Movement Analysis in Institutional Hallways: A Smart Hallway

Video-Based Deep Learning Approach for 3D Human Movement Analysis in Institutional Hallways: A Smart Hallway

System for Evaluation and Compensation of Leg Length Discrepancy for Human Body Balancing

Anatomical leg length discrepancy in children: Can it be accurately determined using 3-D motion capturing?

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