Database (splitted 4/9)
The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body...
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| creator | Losing, Viktor Hasenjäger, Martina |
| description | The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction. |
| doi_str_mv | 10.6084/m9.figshare.20224011 |
| format | Dataset |
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Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.</description><identifier>DOI: 10.6084/m9.figshare.20224011</identifier><language>eng</language><publisher>figshare</publisher><subject>FOS: Clinical medicine ; Neuroscience ; Vision Science</subject><creationdate>2022</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>780,1892</link.rule.ids><linktorsrc>$$Uhttps://commons.datacite.org/doi.org/10.6084/m9.figshare.20224011$$EView_record_in_DataCite.org$$FView_record_in_$$GDataCite.org$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Losing, Viktor</creatorcontrib><creatorcontrib>Hasenjäger, Martina</creatorcontrib><title>Database (splitted 4/9)</title><description>The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.</description><subject>FOS: Clinical medicine</subject><subject>Neuroscience</subject><subject>Vision Science</subject><fulltext>true</fulltext><rsrctype>dataset</rsrctype><creationdate>2022</creationdate><recordtype>dataset</recordtype><sourceid>PQ8</sourceid><recordid>eNo1zj1vwjAUhWEvDAiYWTow0iHhXsfxx1jxLUXqwm5d29c0EpFQkqX_vlTAdPQuR48QHwilBqs2nStzex1-qOdSgpQKEKdiuaORAg28Wg_3WzuOnFZq4z7nYpLpNvDitTNxOewv21PRfB_P26-mSA6xcNLayhjQltDkSBwZXa2NyQHqOqZYB8YQOGStpLGcXFaPBoJUaW10NRPqeZsejNiO7O9921H_6xH8P9t3zr_Z_s2u_gBVXDwt</recordid><startdate>20220728</startdate><enddate>20220728</enddate><creator>Losing, Viktor</creator><creator>Hasenjäger, Martina</creator><general>figshare</general><scope>DYCCY</scope><scope>PQ8</scope></search><sort><creationdate>20220728</creationdate><title>Database (splitted 4/9)</title><author>Losing, Viktor ; Hasenjäger, Martina</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-d911-9288377068a17fcaece195677fb055cdc5be1bbebf64278ed9f41bb0a0d366763</frbrgroupid><rsrctype>datasets</rsrctype><prefilter>datasets</prefilter><language>eng</language><creationdate>2022</creationdate><topic>FOS: Clinical medicine</topic><topic>Neuroscience</topic><topic>Vision Science</topic><toplevel>online_resources</toplevel><creatorcontrib>Losing, Viktor</creatorcontrib><creatorcontrib>Hasenjäger, Martina</creatorcontrib><collection>DataCite (Open Access)</collection><collection>DataCite</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Losing, Viktor</au><au>Hasenjäger, Martina</au><format>book</format><genre>unknown</genre><ristype>DATA</ristype><title>Database (splitted 4/9)</title><date>2022-07-28</date><risdate>2022</risdate><abstract>The database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 $\pm$ 7.64 cm, 72.9 $\pm$ 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction.</abstract><pub>figshare</pub><doi>10.6084/m9.figshare.20224011</doi><oa>free_for_read</oa></addata></record> |
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| identifier | DOI: 10.6084/m9.figshare.20224011 |
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| language | eng |
| recordid | cdi_datacite_primary_10_6084_m9_figshare_20224011 |
| source | DataCite |
| subjects | FOS: Clinical medicine Neuroscience Vision Science |
| title | Database (splitted 4/9) |
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