Whole-Body Pose Estimation in Human Bicycle Riding Using a Small Set of Wearable Sensors
Tracking whole-body human pose in physical human-machine interactions is challenging because of highly dimensional human motions and lack of inexpensive, nonintrusive motion sensors in outdoor environment. In this paper, we present a computational scheme to estimate the human whole-body pose with ap...
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| Veröffentlicht in: | IEEE/ASME transactions on mechatronics 2016-02, Vol.21 (1), p.163-174 |
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| container_title | IEEE/ASME transactions on mechatronics |
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| creator | Zhang, Yizhai Chen, Kuo Yi, Jingang Liu, Tao Pan, Quan |
| description | Tracking whole-body human pose in physical human-machine interactions is challenging because of highly dimensional human motions and lack of inexpensive, nonintrusive motion sensors in outdoor environment. In this paper, we present a computational scheme to estimate the human whole-body pose with application to bicycle riding using a small set of wearable sensors. The estimation scheme is built on the fusion of gyroscopes, accelerometers, force sensors, and physical rider-bicycle interaction constraints through an extended Kalman filter design. The use of physical rider-bicycle interaction constraints helps not only eliminate the integration drifts of inertial sensor measurements but also reduce the number of the needed wearable sensors for pose estimation. For each set of the upper and the lower limb, only one tri-axial gyroscope is needed to accurately obtain the 3-D pose information. The drift-free, reliable estimation performance is demonstrated through both indoor and outdoor riding experiments. |
| doi_str_mv | 10.1109/TMECH.2015.2490118 |
| format | Article |
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In this paper, we present a computational scheme to estimate the human whole-body pose with application to bicycle riding using a small set of wearable sensors. The estimation scheme is built on the fusion of gyroscopes, accelerometers, force sensors, and physical rider-bicycle interaction constraints through an extended Kalman filter design. The use of physical rider-bicycle interaction constraints helps not only eliminate the integration drifts of inertial sensor measurements but also reduce the number of the needed wearable sensors for pose estimation. For each set of the upper and the lower limb, only one tri-axial gyroscope is needed to accurately obtain the 3-D pose information. 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(IEEE) 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c328t-1517287920f11e6f2c04fc3f8125ab8090e1cf17b483557626f5c1bde5fccf7d3</citedby><cites>FETCH-LOGICAL-c328t-1517287920f11e6f2c04fc3f8125ab8090e1cf17b483557626f5c1bde5fccf7d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7296666$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,778,782,794,27911,27912,54745</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7296666$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhang, Yizhai</creatorcontrib><creatorcontrib>Chen, Kuo</creatorcontrib><creatorcontrib>Yi, Jingang</creatorcontrib><creatorcontrib>Liu, Tao</creatorcontrib><creatorcontrib>Pan, Quan</creatorcontrib><title>Whole-Body Pose Estimation in Human Bicycle Riding Using a Small Set of Wearable Sensors</title><title>IEEE/ASME transactions on mechatronics</title><addtitle>TMECH</addtitle><description>Tracking whole-body human pose in physical human-machine interactions is challenging because of highly dimensional human motions and lack of inexpensive, nonintrusive motion sensors in outdoor environment. In this paper, we present a computational scheme to estimate the human whole-body pose with application to bicycle riding using a small set of wearable sensors. The estimation scheme is built on the fusion of gyroscopes, accelerometers, force sensors, and physical rider-bicycle interaction constraints through an extended Kalman filter design. The use of physical rider-bicycle interaction constraints helps not only eliminate the integration drifts of inertial sensor measurements but also reduce the number of the needed wearable sensors for pose estimation. For each set of the upper and the lower limb, only one tri-axial gyroscope is needed to accurately obtain the 3-D pose information. The drift-free, reliable estimation performance is demonstrated through both indoor and outdoor riding experiments.</description><subject>accelerometer and gyroscope</subject><subject>Bicycles</subject><subject>cycling</subject><subject>Gyroscopes</subject><subject>Human</subject><subject>Human motion</subject><subject>Joints</subject><subject>Kinematics</subject><subject>Outdoor</subject><subject>Riding</subject><subject>sensor fusion</subject><subject>Sensors</subject><subject>Wearable</subject><subject>Wearable sensors</subject><subject>whole-body pose estimation</subject><issn>1083-4435</issn><issn>1941-014X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkMFOwkAQhhujiYi-gF428eKlOLPddrdHISgmGI1A4LZZllktKV3slgNvbxHjwTnMzOH7J5Mviq4ReoiQ309fhoNRjwOmPS5yQFQnUQdzgTGgWJy2O6gkFiJJz6OLENYAIBCwEy3mn76kuO9Xe_bmA7FhaIqNaQpfsaJio93GVKxf2L0tib0Xq6L6YLNw6IZNNqYs2YQa5h2bk6nNsoUmVAVfh8vozJky0NXv7Eazx-F0MIrHr0_Pg4dxbBOumhhTlFzJnINDpMxxC8LZxCnkqVkqyIHQOpRLoZI0lRnPXGpxuaLUWevkKulGd8e729p_7Sg0elMES2VpKvK7oFFhBhKF4C16-w9d-11dtd9plEryRCgBLcWPlK19CDU5va1bI_VeI-iDbP0jWx9k61_ZbejmGCqI6C8geZ61lXwD5d15Eg</recordid><startdate>20160201</startdate><enddate>20160201</enddate><creator>Zhang, Yizhai</creator><creator>Chen, Kuo</creator><creator>Yi, Jingang</creator><creator>Liu, Tao</creator><creator>Pan, Quan</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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In this paper, we present a computational scheme to estimate the human whole-body pose with application to bicycle riding using a small set of wearable sensors. The estimation scheme is built on the fusion of gyroscopes, accelerometers, force sensors, and physical rider-bicycle interaction constraints through an extended Kalman filter design. The use of physical rider-bicycle interaction constraints helps not only eliminate the integration drifts of inertial sensor measurements but also reduce the number of the needed wearable sensors for pose estimation. For each set of the upper and the lower limb, only one tri-axial gyroscope is needed to accurately obtain the 3-D pose information. The drift-free, reliable estimation performance is demonstrated through both indoor and outdoor riding experiments.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TMECH.2015.2490118</doi><tpages>12</tpages></addata></record> |
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| subjects | accelerometer and gyroscope Bicycles cycling Gyroscopes Human Human motion Joints Kinematics Outdoor Riding sensor fusion Sensors Wearable Wearable sensors whole-body pose estimation |
| title | Whole-Body Pose Estimation in Human Bicycle Riding Using a Small Set of Wearable Sensors |
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