A Novel Augmented Reality Mobile-Based Application for Biomechanical Measurement

Basiratzadeh, Shahin; Lemaire, Edward D.; Baddour, Natalie

doi:10.3390/biomed2020021

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…To test SLL joint motion, time-stepped component angular motion was tracked using AprilTag2 fiducial markers, an Android Samsung Galaxy S9 + phone, and the “Biomechanics Augmented Reality – Marker” (BAR-M) application developed by Basiratzadeh et al 19,20 The BAR-M application has an angular accuracy of 0.29° and linear accuracy of 0.27 cm in static conditions. Surrogate lower limb joint rotation angles were calculated by securing markers on the SLL segment surfaces and aligning the smartphone parallel to the plane of motion being measured.…”

Section: Pilot Testingmentioning

confidence: 99%

Surrogate lower limb design for ankle-foot orthosis mechanical evaluation

Thibodeau

Dumond

Kim

et al. 2022

Journal of Rehabilitation and Assistive Technologies Engineerin

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Purpose This study designs and provides a pilot evaluation of a novel surrogate lower limb (SLL) that provides anatomically realistic three-dimensional (3D) foot motion, based on a literature consensus of passive lower limb motion. This SLL is intended to replace single axis surrogates currently used in mechanical testing of ankle-foot orthoses (AFO). Material and Methods The SLL design is inspired by the Rizzoli foot model, with shank, hindfoot, midfoot, forefoot, and toe sections. Ball and socket joints were used between hindfoot-midfoot (HM)-forefoot sections. Forefoot-toes used a hinge joint. Three-dimensional printed nylon, thermoplastic polyurethane (TPU) and polylactic acid (PLA), as well as casted silicone rubber were used to re-create foot components. After fabrication, motion capture was performed to measure rotation using fiducial markers. The SLL was then loaded under both static and cyclic loads representing a 100 kg person walking for 500,000 cycles. Results Most joints were within 5° of target angles. The SLL survived static loads representing 1.5 times body weight for both static and cyclical loading. Conclusions This SLL moved as designed and survived testing loads, warranting further investigation towards enabling essential mechanical testing for AFO currently on the market, and helping to guide device prescription.

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Section: Pilot Testingmentioning

confidence: 99%

Surrogate lower limb design for ankle-foot orthosis mechanical evaluation

Thibodeau

Dumond

Kim

et al. 2022

Journal of Rehabilitation and Assistive Technologies Engineerin

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“…Its specific approach involves localizing the keypoints of the human body (such as knees and elbows) from an image. Human pose estimation has a wide range of applications in daily life, such as action recognition [1][2][3], motion tracking [4][5][6], and augmented reality [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

SSA Net: Small Scale-Aware Enhancement Network for Human Pose Estimation

Li,

Zhang,

et al. 2023

Sensors

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In the field of human pose estimation, heatmap-based methods have emerged as the dominant approach, and numerous studies have achieved remarkable performance based on this technique. However, the inherent drawbacks of heatmaps lead to serious performance degradation in methods based on heatmaps for smaller-scale persons. While some researchers have attempted to tackle this issue by improving the performance of small-scale persons, their efforts have been hampered by the continued reliance on heatmap-based methods. To address this issue, this paper proposes the SSA Net, which aims to enhance the detection accuracy of small-scale persons as much as possible while maintaining a balanced perception of persons at other scales. SSA Net utilizes HRNetW48 as a feature extractor and leverages the TDAA module to enhance small-scale perception. Furthermore, it abandons heatmap-based methods and instead adopts coordinate vector regression to represent keypoints. Notably, SSA Net achieved an AP of 77.4% on the COCO Validation dataset, which is superior to other heatmap-based methods. Additionally, it achieved highly competitive results on the Tiny Validation and MPII datasets as well.

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