TY - GEN
T1 - Tracking the path shape qualities of human motion
AU - Tu, Kai
AU - Thornburg, Harvey
AU - Fulmer, Matthew
AU - Spanias, Andreas
PY - 2007
Y1 - 2007
N2 - We propose a probabilistic generative model for extracting intended path shape qualities of an object moving under human control in real time. At each instant, we decide whether the object is moving in a straight, curved, or random path, or whether it has stopped moving. Our model incorporates sensor noise as well as human imperfections in the intended motion. As well as tracking the object's position, velocity, and motion direction, we compute the posterior probability of each shape quality hypothesis given all sensed-data in the horizon [t -N + 1, t]; the hypothesis maximizing this posterior is taken as the decision. The posterior is computed using the unscented Kaiman filter (UKF), as our model is inherently nonlinear. The path-shape quality tracking is successfully embedded in a hybrid physical-digital interface where the position of an illuminated ball, sensed by a low-cost video camera array, triggers multimodal feedback in a mediated learning environment. We show successful results on a variety of real-world motion paths where the participant is given only verbal descriptions of how to move. Our generative model is further validated by user studies involving a simple color-based interaction, where participants discover shape quality controls as they interact.
AB - We propose a probabilistic generative model for extracting intended path shape qualities of an object moving under human control in real time. At each instant, we decide whether the object is moving in a straight, curved, or random path, or whether it has stopped moving. Our model incorporates sensor noise as well as human imperfections in the intended motion. As well as tracking the object's position, velocity, and motion direction, we compute the posterior probability of each shape quality hypothesis given all sensed-data in the horizon [t -N + 1, t]; the hypothesis maximizing this posterior is taken as the decision. The posterior is computed using the unscented Kaiman filter (UKF), as our model is inherently nonlinear. The path-shape quality tracking is successfully embedded in a hybrid physical-digital interface where the position of an illuminated ball, sensed by a low-cost video camera array, triggers multimodal feedback in a mediated learning environment. We show successful results on a variety of real-world motion paths where the participant is given only verbal descriptions of how to move. Our generative model is further validated by user studies involving a simple color-based interaction, where participants discover shape quality controls as they interact.
KW - Activity analysis
KW - Computer vision
KW - Human motion analysis
KW - Human-computer interaction
KW - Hybrid physical-digital environment
KW - Multimedia signal processing
KW - Natural information interface
KW - Unscented Kalman filter
KW - Video sensing
UR - http://www.scopus.com/inward/record.url?scp=34547499401&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34547499401&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2007.366352
DO - 10.1109/ICASSP.2007.366352
M3 - Conference contribution
AN - SCOPUS:34547499401
SN - 1424407281
SN - 9781424407286
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - II781-II784
BT - 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
T2 - 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Y2 - 15 April 2007 through 20 April 2007
ER -