  This study aimed to develop daily living support robots for patients with
hemiplegia and the elderly. To support the daily living activities using robots
in ordinary households without imposing physical and mental burdens on users,
the system must detect the actions of the user and move appropriately according
to their motions.
  We propose a reaching-position prediction scheme that targets the motion of
lifting the upper arm, which is burdensome for patients with hemiplegia and the
elderly in daily living activities.
  For this motion, it is difficult to obtain effective features to create a
prediction model in environments where large-scale sensor system installation
is not feasible and the motion time is short.
  We performed motion-collection experiments, revealed the features of the
target motion and built a prediction model using the multimodal motion features
and deep learning.
  The proposed model achieved an accuracy of 93 \% macro average and F1-score
of 0.69 for a 9-class classification prediction at 35\% of the motion
completion.
