Estimating skeleton-based gait abnormality index by sparse deep auto-encoder
This paper proposes an approach estimating a gait abnormality index based on skeletal information provided by a depth camera. Differently from related works where the extraction of hand-crafted features is required to describe gait characteristics, our method automatically performs that stage with the support of a deep auto-encoder. In order to get visually interpretable features, we embedded a constraint of sparsity into the model. Similarly to most gait-related studies, the temporal factor is also considered as a post-processing in our system. This method provided promising results when experimenting on a dataset containing nearly one hundred thousand skeleton samples.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Skeleton-based Gait Index Estimation with LSTMs
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, th…
DecoderApplying Adversarial Auto-encoder for Estimating Human Walking Gait Abnormality Index
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 m…
Generative Adversarial NetworkSoftware Based Higher Order Structural Foot Abnormality Detection Using Image Processing
The entire movement of human body undergoes through a periodic process named Gait Cycle. The structure of human foot is the key element to complete the cycle successfully. Abnormality of this foot structure is an alarmin…
Anomaly DetectionGeneral ClassificationSkeletonGait: Gait Recognition Using Skeleton Maps
The choice of the representations is essential for deep gait recognition methods. The binary silhouettes and skeletal coordinates are two dominant representations in recent literature, achieving remarkable advances in ma…
Gait RecognitionSpatial Transformer Network on Skeleton-based Gait Recognition
Skeleton-based gait recognition models usually suffer from the robustness problem, as the Rank-1 accuracy varies from 90\% in normal walking cases to 70\% in walking with coats cases. In this work, we propose a state-of-…
Gait Recognition