Optimal Reconstruction of Human Motion From Scarce Multimodal Data

Wearable sensing has emerged as a promising solution
for enabling unobtrusive and ergonomic measurements of
the human motion. However, the reconstruction performance of
these devices strongly depends on the quality and the number of
sensors, which are typically limited by wearability and economic
constraints. A promising approach to minimize the number of
sensors is to exploit dimensionality reduction approaches that
fuse prior information with insufficient sensing signals, through
minimum variance estimation. These methods were successfully
used for static hand pose reconstruction, but their translation to
motion reconstruction has not been attempted yet. In this work,
we propose the usage of functional principal component analysis
to decompose multimodal, time-varying motion profiles in terms
of linear combinations of basis functions. Functional decomposition
enables the estimation of the a priori covariance matrix, and
hence the fusion of scarce and noisy measured data with a priori
information. We also consider the problem of identifying which
elemental variables to measure as the most informative for a given
class of tasks. We applied our method to two different datasets of
upper limb motion D1 (joint trajectories) and D2 (joint trajectories
+ EMG data) considering an optimal set of measures (four joints
for D1 out of seven, three joints, and eight EMGs for D2 out
of seven and twelve, respectively). We found that our approach
enables the reconstruction of upper limb motion with a median
error of 0.013 ± 0.006 rad for D1 (relative median error 0.9%),
and 0.038 ± 0.023 rad and 0.003 ± 0.002 mV for D2 (relative
median error 2.9% and 5.1%, respectively).Wearable sensing has emerged as a promising solution
for enabling unobtrusive and ergonomic measurements of
the human motion. However, the reconstruction performance of
these devices strongly depends on the quality and the number of
sensors, which are typically limited by wearability and economic
constraints. A promising approach to minimize the number of
sensors is to exploit dimensionality reduction approaches that
fuse prior information with insufficient sensing signals, through
minimum variance estimation. These methods were successfully
used for static hand pose reconstruction, but their translation to
motion reconstruction has not been attempted yet. In this work,
we propose the usage of functional principal component analysis
to decompose multimodal, time-varying motion profiles in terms
of linear combinations of basis functions. Functional decomposition
enables the estimation of the a priori covariance matrix, and
hence the fusion of scarce and noisy measured data with a priori
information. We also consider the problem of identifying which
elemental variables to measure as the most informative for a given
class of tasks. We applied our method to two different datasets of
upper limb motion D1 (joint trajectories) and D2 (joint trajectories
+ EMG data) considering an optimal set of measures (four joints
for D1 out of seven, three joints, and eight EMGs for D2 out
of seven and twelve, respectively). We found that our approach
enables the reconstruction of upper limb motion with a median
error of 0.013 ± 0.006 rad for D1 (relative median error 0.9%),
and 0.038 ± 0.023 rad and 0.003 ± 0.002 mV for D2 (relative
median error 2.9% and 5.1%, respectively).