دانلود مقاله انگلیسی رایگان:انجمن آمریکایی بیومکانیک جایزه دستاورد اولیه شغلی 2020: به سوی آزمایشگاههای بیومکانیک قابل حمل و مدولار: چگونه تجزیه ویدئو و IMU تجزیه و تحلیل راه رفتن را تغییر می دهد - 2021
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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 American Society of Biomechanics Early Career Achievement Award 2020: Toward Portable and Modular Biomechanics Labs: How Video and IMU Fusion Will Change Gait Analysis
    American Society of Biomechanics Early Career Achievement Award 2020: Toward Portable and Modular Biomechanics Labs: How Video and IMU Fusion Will Change Gait Analysis

    سال انتشار:

    2021


    عنوان انگلیسی مقاله:

    American Society of Biomechanics Early Career Achievement Award 2020: Toward Portable and Modular Biomechanics Labs: How Video and IMU Fusion Will Change Gait Analysis


    ترجمه فارسی عنوان مقاله:

    انجمن آمریکایی بیومکانیک جایزه دستاورد اولیه شغلی 2020: به سوی آزمایشگاههای بیومکانیک قابل حمل و مدولار: چگونه تجزیه ویدئو و IMU تجزیه و تحلیل راه رفتن را تغییر می دهد


    منبع:

    Sciencedirect - Elsevier - Journal of Biomechanics, Journal Pre-proof, 110650: doi:10:1016/j:jbiomech:2021:110650


    نویسنده:

    Eni Halilaj


    چکیده انگلیسی:

    The field of biomechanics is at a turning point, with marker-based motion capture set to be replaced by portable and inexpensive hardware, rapidly improving markerless tracking algorithms, and open datasets that will turn these new technologies into field-wide team projects. Despite progress, several challenges inhibit both inertial and vision-based motion tracking from reaching the high accuracies that many biomechanics applications require. Their complementary strengths, however, could be harnessed toward better solutions than those offered by either modality alone. The drift from inertial measurement units (IMUs) could be corrected by video data, while occlusions in videos could be corrected by inertial data. To expedite progress in this direction, we have collected the CMU Panoptic Dataset 2.0, which contains 86 subjects captured with 140 VGA cameras, 31 HD cameras, and 15 IMUs, performing on average 6.5 minutes of activities, including range of motion activities and tasks of daily living. To estimate ground-truth kinematics, we imposed simultaneous consistency with the video and IMU data. Threedimensional joint centers were first computed by geometrically triangulating proposals from a convolutional neural network applied to each video independently. A statistical meshed model parametrized in terms of body shape and pose was then fit through a top-down optimization approach that enforced consistency with both the video-based joint centers and IMU data. This sensor-dense dataset can be used to benchmark new methods that integrate a much sparser set of videos and IMUs to estimate both kinematics and kinetics in a markerless fashion.
    Key words: markerless motion tracking | computer vision | inertial measurement units


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 23
    حجم فایل: 3995 کیلوبایت

    قیمت: رایگان


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