American society of biomechanics early career achievement award 2020: Toward portable and modular biomechanics labs: How video and IMU fusion will change gait analysis

Halilaj, Eni; Shin, Soyong; Rapp, Eric; Xiang, Donglai

doi:10.1016/j.jbiomech.2021.110650

Cited by 22 publications

(15 citation statements)

References 23 publications

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“…For fusion of IMU and video data via unconstrained optimization, we formulated a simplified optimization problem where J track from the IMU and vision optimization was minimized, excluding J effort and constraints on system dynamics and model controls (Halilaj et al, 2021). Here, the optimal set of kinematics was determined by minimizing the error between the experimental IMU and video data and the synthetic IMU and keypoint profiles projected from the subject’s current state.…”

Section: Methodsmentioning

confidence: 99%

“…IMU and vision data have complementary strengths that can be leveraged to overcome their individual limitations, but it is unclear if fusion via a dynamically constrained biomechanical model would improve estimation of kinematics over unconstrained optimization (Halilaj et al, 2021). Inertial sensing can compensate for occlusions in videos, videos can compensate for drift in inertial data, and biomechanical models can add physiological plausibility and dynamical robustness.…”

Section: Introductionmentioning

confidence: 99%

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Fusion of Video and Inertial Sensing Data via Dynamic Optimization of a Biomechanical Model

Pearl

Shin

Godura

et al. 2022

Preprint

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Inertial sensing and computer vision are promising alternatives to traditional optical motion tracking, but until now these data sources have been explored either in isolation or fused via unconstrained optimization, which may not take full advantage of their complementary strengths. By adding physiological plausibility and dynamical robustness to a proposed solution, biomechanical modeling may enable better fusion than unconstrained optimization. To test this hypothesis, we fused video and inertial sensing data via dynamic optimization with a nine degree-of-freedom model and investigated when this approach outperforms video-only, inertial-sensing-only, and unconstrained-fusion methods. We used both experimental and synthetic data that mimicked different ranges of video and inertial measurement unit (IMU) data noise. Fusion with a dynamically constrained model improved estimation of lower-extremity kinematics by a mean ± std root-mean-square error of 6.0° ± 1.2° over the video-only approach and estimation of joint centers by 4.5 ± 2.8 cm over the IMU-only approach. It consistently outperformed single-modality approaches across different noise profiles. When the quality of video data was high and that of inertial data was low, dynamically constrained fusion improved joint kinematics by 3.7° ± 1.2° and joint centers by 1.9 ± 0.5 cm over unconstrained fusion, while unconstrained fusion was advantageous by 3.0° ± 1.4° and 1.2 ± 0.7 cm in the opposite scenario. These findings indicate that complementary modalities and techniques can improve motion tracking by clinically meaningful margins and that data quality and computational complexity must be considered when selecting the most appropriate method for a particular application.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fusion of Video and Inertial Sensing Data via Dynamic Optimization of a Biomechanical Model

Pearl

Shin

Godura

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…A review of the use of RGB-D sensors for musculoskeletal health monitoring has revealed a lack of validity assessment and although the algorithms for 3D joint parameters are found to be acceptable, there are still limitations (Mangal and Tiwari 2021). Though camera based skeletal tracking has accuracy limitations (Abedtash and Holden 2017), advances in marker-less motion capture will likely change the future of data collection for biomechanics (Halilaj et al 2021). These systems offer potential to deliver an alternative approach with practical bene ts for both xed and WS due to the ability to capture data in any environment where cameras can be set up (Latorre et al 2018;Milosevic et al 2020).…”

Section: Al 2020)mentioning

confidence: 99%

Biomechanics Beyond the Lab: Remote Technology for Osteoarthritis Patient Data - A Scoping Review

Hamilton

Williams

Consortium³

et al. 2022

Preprint

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The objective of the project was to produce a review of available and validated technology suitable for gathering biomechanical and functional research data in patients with osteoarthritis (OA), outside of a traditional fixed laboratory setting. A scoping review was conducted using defined search terms across three databases (SCOPUS, OVID MEDLINE, and PEDRO) and additional sources of information from grey literature were added. One author carried out an initial title and abstract review, and two authors independently completed full text screenings. Out of the total 5,164 articles screened, 75 were included based on inclusion criteria covering a range of technologies in articles published from 2015. These were subsequently categorised by technology type (wearables-IMUs, wearables-other, insoles/platforms and cameras) and metric (kinematic and spatiotemporal measures (SPTs), kinetic and SPTs, joint angles/ROM only and EMG), and as suitable for portable, part remote or remote use. Those technologies that are commercially available were also identified. Results concluded that from the growing number of available and emerging technologies, there is a well-established range in use. These are primarily inertial measurement units, as well as other wearables and camera-based technologies, particularly for collection of gait SPTs. Results demonstrate that biomechanical and functional remote data collection is both feasible and has growing potential for OA researchers.

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“…A review of the use of RGB-D sensors for musculoskeletal health monitoring has revealed a lack of validity assessment along with limitations (such as limited camera depth information), although models for 3D joint parameters are found to be acceptable ( 6 ). Despite these limitations ( 39 ), advances in markerless motion capture will likely change the future of data collection in biomechanics ( 40 ). These systems offer the potential to deliver an alternative approach with practical benefits for both fixed and WS due to the ability to capture data in any environment where cameras can be set up ( 41 , 42 ).…”

Section: Introductionmentioning

confidence: 99%

Biomechanics beyond the lab: Remote technology for osteoarthritis patient data—A scoping review

Hamilton

Williams²,

Holt³

2022

Front. Rehabilit. Sci.

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The objective of this project is to produce a review of available and validated technologies suitable for gathering biomechanical and functional research data in patients with osteoarthritis (OA), outside of a traditionally fixed laboratory setting. A scoping review was conducted using defined search terms across three databases (Scopus, Ovid MEDLINE, and PEDro), and additional sources of information from grey literature were added. One author carried out an initial title and abstract review, and two authors independently completed full-text screenings. Out of the total 5,164 articles screened, 75 were included based on inclusion criteria covering a range of technologies in articles published from 2015. These were subsequently categorised by technology type, parameters measured, level of remoteness, and a separate table of commercially available systems. The results concluded that from the growing number of available and emerging technologies, there is a well-established range in use and further in development. Of particular note are the wide-ranging available inertial measurement unit systems and the breadth of technology available to record basic gait spatiotemporal measures with highly beneficial and informative functional outputs. With the majority of technologies categorised as suitable for part-remote use, the number of technologies that are usable and fully remote is rare and they usually employ smartphone software to enable this. With many systems being developed for camera-based technology, such technology is likely to increase in usability and availability as computational models are being developed with increased sensitivities to recognise patterns of movement, enabling data collection in the wider environment and reducing costs and creating a better understanding of OA patient biomechanical and functional movement data.

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American society of biomechanics early career achievement award 2020: Toward portable and modular biomechanics labs: How video and IMU fusion will change gait analysis

Cited by 22 publications

References 23 publications

Fusion of Video and Inertial Sensing Data via Dynamic Optimization of a Biomechanical Model

Fusion of Video and Inertial Sensing Data via Dynamic Optimization of a Biomechanical Model

Biomechanics Beyond the Lab: Remote Technology for Osteoarthritis Patient Data - A Scoping Review

Biomechanics beyond the lab: Remote technology for osteoarthritis patient data—A scoping review

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