# Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory
## 5 Discussion
In other words, the COP trajectory seems to remain constant, even if the degree of attention dedicated to gait control is modified. Two techniques are in use for measuring the COP of walking individuals: (1) reaction force measured through strain gauges (force platform) and (2) pressure field measured through a grid of pressure sensors. In this study, I applied the first solution. Instrumented walkways including force platforms for analyzing overground walking exist for medical applications [70]. The COP trajectory is computed by aggregating the signals of strain gauges that are placed on the edges of the walkway every 1 m to 2 m. COP trajectory assessment requires the measurement of the vertical force only (i.e., a three-component force platform [71]), which is a major simplification as compared to methods relying on 3D GRF signals that require a six-component force platform [71]. The major issue is that the correct COP position can be obtained only when one individual at a time steps onto the sensitive area. The second solution exists for both treadmill [29] and overground walking [65], and has been favored for biometric applications, given that footprint shape is also helpful for recognizing gaits (see Section 2). In this case, COP is retrieved using the weighted average of the pressure sensor outputs. The gait of several people can be simultaneously recorded on the same sensitive area, provided a pre-processing algorithm separates individual footprints [72]. The drawback of the pressure sensor grid is a complicated technical setup that generates a large quantity of raw data. Indeed, thousands of sensors are required to cover a large area, each sensor generating its own pressure signal. Like other footstep recognition studies (Table 4), the present study was conducted under controlled laboratory conditions. This is not fully representative of spontaneous walking in habitual environments. One major issue is that the participants walked at a constant preferred speed. Although preferred walking speed is known to be constant (3% to 4% variation [58]), voluntary control and changing conditions (such as slope or crowding) can modify it. The impact of these speed changes on the COP trajectory requires further investigations. If any, the gait gallery used as reference for future identifications could include gaits collected at different speeds. A second major issue concerns the footwear. As shown by [PERSON] [23], identifying people wearing different shoes in the gallery and in the probe sets is challenging. Clarifying footwear's effect on the COP trajectory is therefore a priority for future studies. How well can the COP method perform compared to the reference methodology of gait recognition, namely video-based gait recognition? Both methods share the interesting ability to identify or verify users without their knowledge or cooperation. The performance of both methods can be potentially affected by the intrinsic variability of gait patterns induced by long term changes (age, gait disorders, weight gain) or transient modifications (walking speed, carrying conditions, injuries, clothing, footwear) [4]. On the contrary, the COP method is not affected by extrinsic sources of error known to affect video-based recognition, such as changes in lightning conditions, viewing angle, and occlusions. Furthermore, regarding storage and computational needs, COP recording has the advantage of generating far less data than videos and hence could be more efficient in real-time applications. That said, video-based gait recognition has the overwhelming advantage of being able to exploit the data of countless video surveillance networks installed all around the globe. On the contrary, COP analysis cannot exploit already installed infrastructures and thus could be used in specific situations where video monitoring is difficult or unwanted. For instance, in many countries, video monitoring in the workplace is restricted in order to protect employees' privacy.