Robust Hand Motion Tracking through Data Fusion of 5DT Data Glove and Nimble VR Kinect Camera Measurements
Identifieur interne : 003E65 ( Ncbi/Curation ); précédent : 003E64; suivant : 003E66Robust Hand Motion Tracking through Data Fusion of 5DT Data Glove and Nimble VR Kinect Camera Measurements
Auteurs : Ewout A. Arkenbout ; Joost C. F. De Winter ; Paul BreedveldSource :
- Sensors (Basel, Switzerland) [ 1424-8220 ] ; 2015.
Abstract
Vision based interfaces for human computer interaction have gained increasing attention over the past decade. This study presents a data fusion approach of the Nimble VR vision based system, using the Kinect camera, with the contact based 5DT Data Glove. Data fusion was achieved through a Kalman filter. The Nimble VR and filter output were compared using measurements performed on (1) a wooden hand model placed in various static postures and orientations; and (2) three differently sized human hands during active finger flexions. Precision and accuracy of joint angle estimates as a function of hand posture and orientation were determined. Moreover, in light of possible self-occlusions of the fingers in the Kinect camera images, data completeness was assessed. Results showed that the integration of the Data Glove through the Kalman filter provided for the proximal interphalangeal (PIP) joints of the fingers a substantial improvement of 79% in precision, from 2.2 deg to 0.9 deg. Moreover, a moderate improvement of 31% in accuracy (being the mean angular deviation from the true joint angle) was established, from 24 deg to 17 deg. The metacarpophalangeal (MCP) joint was relatively unaffected by the Kalman filter. Moreover, the Data Glove increased data completeness, thus providing a substantial advantage over the sole use of the Nimble VR system.
Url:
DOI: 10.3390/s151229868
PubMed: 26694395
PubMed Central: 4721788
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Robust Hand Motion Tracking through Data Fusion of 5DT Data Glove and Nimble VR Kinect Camera Measurements</title><author><name sortKey="Arkenbout, Ewout A" sort="Arkenbout, Ewout A" uniqKey="Arkenbout E" first="Ewout A." last="Arkenbout">Ewout A. Arkenbout</name></author><author><name sortKey="De Winter, Joost C F" sort="De Winter, Joost C F" uniqKey="De Winter J" first="Joost C. F." last="De Winter">Joost C. F. De Winter</name></author><author><name sortKey="Breedveld, Paul" sort="Breedveld, Paul" uniqKey="Breedveld P" first="Paul" last="Breedveld">Paul Breedveld</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">26694395</idno><idno type="pmc">4721788</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4721788</idno><idno type="RBID">PMC:4721788</idno><idno type="doi">10.3390/s151229868</idno><date when="2015">2015</date><idno type="wicri:Area/Pmc/Corpus">000390</idno><idno type="wicri:Area/Pmc/Curation">000390</idno><idno type="wicri:Area/Pmc/Checkpoint">000359</idno><idno type="wicri:Area/Ncbi/Merge">003E65</idno><idno type="wicri:Area/Ncbi/Curation">003E65</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Robust Hand Motion Tracking through Data Fusion of 5DT Data Glove and Nimble VR Kinect Camera Measurements</title><author><name sortKey="Arkenbout, Ewout A" sort="Arkenbout, Ewout A" uniqKey="Arkenbout E" first="Ewout A." last="Arkenbout">Ewout A. Arkenbout</name></author><author><name sortKey="De Winter, Joost C F" sort="De Winter, Joost C F" uniqKey="De Winter J" first="Joost C. F." last="De Winter">Joost C. F. De Winter</name></author><author><name sortKey="Breedveld, Paul" sort="Breedveld, Paul" uniqKey="Breedveld P" first="Paul" last="Breedveld">Paul Breedveld</name></author></analytic><series><title level="j">Sensors (Basel, Switzerland)</title><idno type="eISSN">1424-8220</idno><imprint><date when="2015">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Vision based interfaces for human computer interaction have gained increasing attention over the past decade. This study presents a data fusion approach of the Nimble VR vision based system, using the Kinect camera, with the contact based 5DT Data Glove. Data fusion was achieved through a Kalman filter. The Nimble VR and filter output were compared using measurements performed on (1) a wooden hand model placed in various static postures and orientations; and (2) three differently sized human hands during active finger flexions. Precision and accuracy of joint angle estimates as a function of hand posture and orientation were determined. Moreover, in light of possible self-occlusions of the fingers in the Kinect camera images, data completeness was assessed. Results showed that the integration of the Data Glove through the Kalman filter provided for the proximal interphalangeal (PIP) joints of the fingers a substantial improvement of 79% in precision, from 2.2 deg to 0.9 deg. Moreover, a moderate improvement of 31% in accuracy (being the mean angular deviation from the true joint angle) was established, from 24 deg to 17 deg. The metacarpophalangeal (MCP) joint was relatively unaffected by the Kalman filter. 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