  In this paper, we propose a method that estimates a gait index for a sequence
of skeletons. Our system is a stack of an encoder and a decoder that are formed
by Long Short-Term Memories (LSTMs). In the encoding stage, the characteristics
of an input are automatically determined and are compressed into a latent
space. The decoding stage then attempts to reconstruct the input according to
such intermediate representation. The reconstruction error is thus considered
as a weak gait index. By combining such weak indices over a long-time movement,
our system can provide a good estimation for the gait index. Our experiments on
a large dataset (nearly one hundred thousand skeletons) showed that the index
given by the proposed method outperformed some recent works on gait analysis.
