Abstract
The essential human gait parameters are briefly reviewed, followed by a detailed review of the state of the art in deep learning for the human gait analysis. The modalities for capturing the gait data are grouped according to the sensing technology: video sequences, wearable sensors, and floor sensors, as well as the publicly available datasets. The established artificial neural network architectures for deep learning are reviewed for each group, and their performance are compared with particular emphasis on the spatiotemporal character of gait data and the motivation for multi-sensor, multi-modality fusion. It is shown that by most of the essential metrics, deep learning convolutional neural networks typically outperform shallow learning models. In the light of the discussed character of gait data, this is attributed to the possibility to extract the gait features automatically in deep learning as opposed to the shallow learning from the handcrafted gait features.
Highlights
G AIT refers to the displacement of the center of gravity during locomotion
We show that the sensing principle used for this grouping shapes the choice of deep learning processing methodology: the video sequence (VS) solutions are based on action recognition using spatiotemporal information; wearable sensors (WS) systems typically comprise inertial sensors to acquire human body velocity, acceleration and orientation during physical human activity; floor sensors (FS) characteristically monitor the Ground Reaction Force (GRF) induced by floor contact during the gait cycle
Since performance is key in real world applications, deep learning has emerged as a promising data processing method by extracting automatically reliable discriminative features of human gait, outperforming the approaches based on handcrafted features
Summary
G AIT refers to the displacement of the center of gravity during locomotion In humans, it is achieved through the synchronized movement of the lower limbs and the trunk, resulting in a move from one position to the other [1]. It is a chain of commands generated in the brain and transmitted through the spinal cord to activate the lower neural center, which will result in muscle contraction patterns assisted by sensory feedback from joints, muscles and other receptors to control the movements This will result in the feet recurrently contacting the ground surface to move the trunk and lower limbs in a coordinated way, delivering a change in the body center-of-mass position. This phase is subdivided into four intervals (A, B, C, D)
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