Dataset Open Access

Dataset of Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

Filippeschi, Alessandro; Ruffaldi, Emanuele


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    <dct:title>Dataset of Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion</dct:title>
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    <dcat:keyword>imu</dcat:keyword>
    <dcat:keyword>inertial</dcat:keyword>
    <dcat:keyword>human motion</dcat:keyword>
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    <dct:description>&lt;p&gt;MATLAB Dataset for the paper. &lt;/p&gt; &lt;p&gt;Paper Abstract:&lt;/p&gt; &lt;p&gt;Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error).&lt;/p&gt;</dct:description>
    <dct:description>https://github.com/eruffaldi/imu_comparison_data</dct:description>
    <dct:description>{"references": ["Filippeschi, A.; Schmitz, N.; Miezal, M.; Bleser, G.; Ruffaldi, E.; Stricker, D.\tSurvey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion. Sensors 2017, 17, 1257."]}</dct:description>
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