# Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory
## 1 Introduction  
Human beings move through their environment using repetitive movements of the lower limbs, such as walking or running. The sequence of these movements constitutes one's gait. One gait cycle (or stride) is created by the alternation of stance and swing phases performed by the legs. The gait pattern is constrained by biomechanical and energetic factors [1]. Furthermore, each individual has a unique gait signature that can be used for identification purposes, in a process known as gait recognition [2]. The most interesting aspect of gait recognition is that it can identify subjects without their knowledge or approval, contrary to other biometric methods such as fingerprint or iris recognition. Video-based methods dominate the field of gait recognition [3, 4]. Researchers can benefit from numerous easily available video databases [5], the largest of which contains more than 10,000 individuals [6]. Recent advances show a recognition rate of between 90% and 95% in optimal viewing conditions; but the accuracy decreases under challenging conditions (such as occlusions, view variations, or appearance changes) [4]. Wearable inertial sensors have also been proposed for recognizing gait [7, 8]. These sensors are used extensively in biomedical applications for gait analysis, and, therefore, significantly aid research efforts [9]. High recognition rates (>95%) have been observedunder laboratory conditions [10], but a lower accuracy (\(\sim\)70%) has been reported in more realistic datasets [11]. Analyzing the force a walking individual applies to the ground has also been proposed for identifying people, an approach referred to as footstep recognition [12]. Different gait features can be extracted through force sensors embedded in the floor, including the temporal sequence of the ground reaction force (GRF) [13; 14; 15], the shape of the foot on the ground (footprint) [16], and the trajectory of the center of pressure (COP) [17]. The COP is the point at which the resulting vertical force is applied (i.e., the integrated vectorial pressure field). Promising results have been obtained, with classification rates higher than 95% [15; 16] (see also Section 2); however, the number of footstep recognition studies is still low, particularly those that include a COP analysis. COP trajectory has never been used alone for identification and verification aims. In addition, most of the footstep recognition studies included only a few individuals performing a limited number of strides [2; 12]. Recognizing people through their gaits relies on the analysis of multiple complex features. Neural networks are therefore helpful for this task [18]. Convolutional neural networks (CNNs) have been used with great success for video-based gait recognition [19; 20]. CNNs are especially well suited for working with images as a result of their strong spatial dependencies in local regions and a substantial degree of translation invariance. Similarly, time series can exhibit locally correlated points that are invariant with time shifts. The successful use of deep CNNs for the classification of uni-dimensional or multidimensional time series has been attested [21; 22]. Like for image classification, CNNs can extract deep features from a signal's internal structure. CNNs are potent tools for bypassing feature engineering in signal processing tasks (end-to-end learning) [22]. However, CNNs, like other artificial neural networks, require hundreds of examples in each class for efficient learning; therefore, they have not been applied in footstep recognition studies so far due to the difficulty of collecting many strides using sensing floors or force platforms. The innovative idea behind this study was to use an instrumented treadmill equipped with a force platform to record hundreds of consecutive strides. The recorded COP trajectories were then harnessed to identify individuals. I applied state-of-the-art CNNs and supervised end-to-end learning. The feature extraction capabilities of CNNs were used to classify individual gaits.