Kinematic Covariance Based Abnormal Gait Detection

Margarita Khokhlova (1) , Cyrille Migniot (1) , Albert Dipanda (1)

1 CORES - Equipe CORES [ImViA - EA7535]

This paper proposes an approach for automatic detection of abnormal human gait. We use an improved skeleton data covariance based gait assessment approach. Low-limbs flexion angles are derived using skeletons computed from data acquired by the Kinect sensor. Then for each gait sequence, we calculate a covariance matrix from the obtained angles data. The matrices are used as features for two classification schemes: a normal gait model-based and a k-NN-based. The resulting descriptor is compact, does not require prior temporal segmentation and shows competitive results on available pathological gait datasets.

Mots clés: gait assessment; skeleton data; dissemin; covariance features; abnormal gait

Domaines: Sciences de l'ingénieur [physics]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Traitement des images [eess.IV]

Margarita Khokhlova, Cyrille Migniot, Albert Dipanda. Kinematic Covariance Based Abnormal Gait Detection. 2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2018, -, France. ⟨10.1109/sitis.2018.00111⟩. ⟨hal-02064251⟩