Skeleton-based Gait Index Estimation with LSTMs
This work addresses gait analysis for applications like healthcare or biometrics, but it is incremental as it builds on existing LSTM and reconstruction techniques.
The paper tackled estimating a gait index from skeleton sequences using an LSTM-based encoder-decoder, where reconstruction error serves as a weak index aggregated over time, and experiments on a large dataset showed it outperformed recent gait analysis methods.
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.