Applying Adversarial Auto-encoder for Estimating Human Walking Gait Abnormality Index
This work addresses gait abnormality assessment for medical or rehabilitation applications, but it is incremental as it applies an existing method (AAE) to a new data type.
The paper tackles the problem of estimating human walking gait quality by using an adversarial auto-encoder (AAE) on 3D point cloud sequences, achieving results that outperform related approaches on a dataset of nearly 100,000 point clouds.
This paper proposes an approach that estimates human walking gait quality index using an adversarial auto-encoder (AAE), i.e. a combination of auto-encoder and generative adversarial network (GAN). Since most GAN-based models have been employed as data generators, our work introduces another perspective of their application. This method directly works on a sequence of 3D point clouds representing the walking postures of a subject. By fitting a cylinder onto each point cloud and feeding obtained histograms to an appropriate AAE, our system is able to provide different measures that may be used as gait quality indices. The combinations of such quantities are also investigated to obtain improved indicators. The ability of our method is demonstrated by experimenting on a large dataset of nearly 100 thousands point clouds and the results outperform related approaches that employ different input data types.