Detecting Toe-Off Events Utilizing a Vision-Based Method

Abstract

Detecting gait events from video data accurately would be a challenging problem. However, most detection methods for gait events are currently based on wearable sensors, which need high cooperation from users and power consumption restriction. This study presents a novel algorithm for achieving accurate detection of toe-off events using a single 2D vision camera without the cooperation of participants. First, a set of novel feature, namely consecutive silhouettes difference maps (CSD-maps), is proposed to represent gait pattern. A CSD-map can encode several consecutive pedestrian silhouettes extracted from video frames into a map. And different number of consecutive pedestrian silhouettes will result in different types of CSD-maps, which can provide significant features for toe-off events detection. Convolutional neural network is then employed to reduce feature dimensions and classify toe-off events. Experiments on a public database demonstrate that the proposed method achieves good detection accuracy.

Gait Segmentation Method Using a Plantar Pressure Measurement System with Custom-Made Capacitive Sensors

“…Gait detection consists in determining and recognizing the different periodic patterns during a gait. Segmentation is the use of those spatial and temporal patterns in order to identify the gait phases and subphases [13,32,34,35]. This allows us to study and characterize the human gait, enabling the identification of normal and pathological conditions [34,35].…”

Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors

“…In wearable sensor-based technology, many sensors (e.g., accelerometers, gyroscopes and magnetometers) are attached to the subject’s limb and body in order to examine interactions with the surroundings [ 26 , 27 , 28 ]. In [ 29 ], A. Howedi et al proposed a unique HIR methodology based on different entropy measures, such as Fuzzy, sample and approximate entropy.…”

Statistical Machine Learning for Human Behaviour Analysis

“…We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field.…”

“…Four papers in this special issue have covered action recognition [ 6 , 9 , 12 , 13 ]. Reference [ 12 ] has proposed a system for toe-off detection using a regular camera. The system extracts the differences between consecutive frames to build silhouettes difference maps, that are then fed into a CNN for feature extraction and classification.…”

“…Different types of maps are developed and tested in this paper. The experimental results reported in [ 12 ] on public databases show state-of-the-art performance. Reference [ 6 ] proposes a system for individuals and then crowd condition monitoring and prediction.…”