# Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory
## 2 Related Works  
In 2004, [PERSON] et al. [17] suggested combining static and dynamic foot pressure features to identify walking individuals. They used a mat containing \(40\times 80\) pressure sensors (\(1\times 1\) cm\({}^{2}\) resolution) to record footprints and COP trajectories and collected one-step footprints from 11 participants. Forty footprints were recorded from each subject for two months. An overall classification accuracy of 98.6% was obtained using the hidden Markov model and Levenberg-Marquart learning methods. One strength of the study was that data were recorded over a long period of time and tended to demonstrate that foot pressure features are time invariant to a substantial degree. The study's limitations included the small sample size and the fact that the subjects had to walk barefoot, making the method difficult to apply in practice.  
In an article published in 2007, [PERSON] and [PERSON] described a method for using a pressure-sensitive floor to identify individuals [18]. They covered the floor of their research laboratory with 100 m\({}^{2}\) of an electro-mechanical film sensitive to pressure and collected gait pressure data from 10 individuals. The researchers focused on the pressure intensity profiles, rather than on pressure trajectories or foot shape. They applied several classifying algorithms, of which support vector machines and multilayer perceptrons were the most accurate. The most outstanding finding was that 95% classification accuracy was achieved when multiple features collected from several consecutive steps were combined. Despite the small sample, this study demonstrated the technical feasibility of using an instrumented (sensing) floor for biometric recognition.  
In their 2012 study, [PERSON] et al. [16] analyzed dynamic foot pressure patterns through plantar pressure imaging using a pedography platform that recorded footprints with 5 mm resolution. They recruited 104 individuals and collected 1040 barefoot steps. Several pre-features characterizing the pressure patterns where extracted, and a dimensionality reduction technique was used. A one-fold nearest-neighbor classifier was applied with cross-validation. The results show that the best feature was the pressure-time integral (PTI), with a classification rate of 99.6%. Overall, this study demonstrated that plantar pressure patterns are highly unique among individuals. The main practical limitation was that they investigated unshod walking.  
In 2015, [PERSON] studied foot pressure data collected from 92 subjects walking either unshod or shod on a pressure mat (255 \(\times\) 64 pressure sensors) [23]. The purpose was to evaluate the classification and identification performance under three scenarios: (1) a \"barefoot\" scenario, in which the recognition system classifies only barefoot data; (2) a \"same shoe\" scenario, in which the system classifies footsteps of subjects wearing the same footwear throughout; and (3) a \"different shoe\" scenario, in which the system learns from examples of individuals walking with one type of footwear, and then evaluates footsteps when they walked with a different type of footwear. [PERSON] then assessed many pressure-derived features and combinations thereof, including COP trajectories, GRF timing, footprint images, and general gait features. The results reveal that for the most difficult scenario (i.e., #3), it was possible to achieve a classification accuracy of 90.5%. Interestingly, COP parameters were among the most important features for an optimal classification in both shod scenarios (#2 and #3). In addition, gait metrics--such as cadence, step length, toe-out angle, and foot length--also played a substantial role. In short, this study demonstrated that COP trajectory might be appropriate for classifying shod walking gaits.