TY - GEN
T1 - Using penalized spline regression to calculate mean trajectories including confidence intervals of human motion data
AU - Carton, Daniel
AU - Turnwald, Annemarie
AU - Olszowy, Wiktor
AU - Buss, Martin
AU - Wollherr, Dirk
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2015/1/23
Y1 - 2015/1/23
N2 - Research in motion planning for mobile robots increasingly focuses on modeling human-like motions and behaviors. Applied to robots, these models help generating motions that are intuitively comprehensible for a human interaction partner. However, identifying the underlying parameters of such human motion models is challenging. These parameters are commonly estimated by analyzing measured single trajectories or means of trajectory sets. Indeed, raw trajectories as well as the means are often not representative for the data, as measurements are noisy and the amount of generated data is limited. For a reasonable analysis it is necessary to smooth the data and estimate an according confidence interval for the mean. In this paper we apply penalized splines to smooth single trajectories and to estimate means of trajectory sets, which ensures little distortion of the original data. Based on that, a method is presented that yields a confidence interval for the mean of human motion data. In order to cope with unknown distributions and small datasets our method employs bootstrapping. The analysis based on confidence intervals takes the variance of the data into account and allows for reasonable conclusions about underlying human motion parameters.
AB - Research in motion planning for mobile robots increasingly focuses on modeling human-like motions and behaviors. Applied to robots, these models help generating motions that are intuitively comprehensible for a human interaction partner. However, identifying the underlying parameters of such human motion models is challenging. These parameters are commonly estimated by analyzing measured single trajectories or means of trajectory sets. Indeed, raw trajectories as well as the means are often not representative for the data, as measurements are noisy and the amount of generated data is limited. For a reasonable analysis it is necessary to smooth the data and estimate an according confidence interval for the mean. In this paper we apply penalized splines to smooth single trajectories and to estimate means of trajectory sets, which ensures little distortion of the original data. Based on that, a method is presented that yields a confidence interval for the mean of human motion data. In order to cope with unknown distributions and small datasets our method employs bootstrapping. The analysis based on confidence intervals takes the variance of the data into account and allows for reasonable conclusions about underlying human motion parameters.
UR - http://www.scopus.com/inward/record.url?scp=84937417149&partnerID=8YFLogxK
U2 - 10.1109/ARSO.2014.7020984
DO - 10.1109/ARSO.2014.7020984
M3 - Conference contribution
AN - SCOPUS:84937417149
T3 - Proceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO
SP - 76
EP - 81
BT - ARSO 2014 - Workshop Digest, IEEE International Workshop on Advance Robotics and its Social Impacts
A2 - Admoni, Henny
A2 - Asfour, Tamim
A2 - Bethel, Cindy
A2 - Bourne, David
A2 - Dragan, Anca
A2 - Feil-Seifer, David
A2 - Graf, Birgit
A2 - Han, Jeonghye
A2 - Kirchner, Nathan
A2 - Konyo, Masashi
A2 - Kotosaka, Shinya
A2 - Kwak, Sonya
A2 - Liu, Changchun
A2 - MacDonald, Bruce
A2 - Sabanovic, Selma
A2 - Salvini, Pericle
A2 - Scherer, Sebastian
A2 - Shiomi, Masahiro
A2 - Teti, Giancarlo
A2 - Tully, Stephen
A2 - Vanderborght, Bram
A2 - Wada, Kazuyoshi
A2 - Wrede, Britta
A2 - Zhang, Fumin
PB - IEEE Computer Society
T2 - 9th IEEE International Workshop on Advance Robotics and its Social Impacts, ARSO 2014
Y2 - 11 September 2014 through 13 September 2014
ER -