# Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory
## 5 Discussion
These recent results are in accordance with older findings [61]. Another recent study [60] also showed that most gait parameters, including GRF, are reproducible from one day to another (ICCs \(>\) 0.95). Finally, [PERSON]'s results [17] show that footprint collected over two months can identify individuals with an accuracy of 98.6%. Therefore, the intrinsic variability of foot pressures appears to be compatible with biometric applications. Which part of COP's typical shape is important for recognizing individuals? Answering this question would clarify which phase of the gait cycle is the most discriminative. To answer this question, I applied the CAM technique (Figure 6). Overall, it seems that no gait phase stands out as a favored CNN target. When a pattern is sufficiently unique, as for Subjects #3 and #4, the CNN uses it for classification, but in other cases (#2 and #5), the CNN appears to prefer a more global approach. This illustrates the great adaptability of the CNNs, which can extract the most useful features for a task without preliminary feature engineering. One prominent particularity of the present study was the use of an instrumented treadmill to collect gait data. The main advantage was that a large number of footsteps (108,000) were recorded in a constant environment. It was thus possible to use deep neural networks, which are known to require substantial data to fulfill their full potential [62]. The drawback was that treadmill gaits can differ from standard (overground) gaits [63]. Although this difference is deemed to be small [64], future investigations should focus on overground walking. It is worth noting that the butterfly-like diagram of COP can also be obtained in overground walking by subtracting the average speed vector from the trajectory [65]. Because the continuous gait data were segmented into small segments of two strides (Figure 2), a pressure-sensitive floor of 3 m in length could capture enough foot pressure data to identify individuals; with such a length, the recording of two consecutive strides is possible even for fast walking [66]. Transfer learning results also highlighted that two strides could be sufficient to reconfigure a pretrained CNN to classify previously unseen gaits. Indeed, when feeding the reference CNN with only one segment per individual collected from the six participants not used in the CNN design phase,the classification accuracy of 60 segments of the test set reached 100% in most repetitions (Figure 4). Using four strides further reduces the occurrence of low accuracy outliers (Figure 4). Similarly, using the pretrained CNNs to classify the whole dataset including 36 individuals was very efficient (Table 2). The first layers of the CNN very likely learned to separate the general features of the butterfly-like shapes that are common to everyone. Based on this preliminary feature separation, the last layers can easily recognize new gaits by learning details that are unique to each individual. Transfer learning is therefore a potent tool that extends the use of deep CNNs in datasets of any size. The gait dataset used in the present study was reemployed from a study aimed at a better understanding of gait variability under different conditions that modified attentional demand [24]. Attention changes are frequent in free-living walking; for example, a greater attention to gait is required when navigating through crowded environments. Asking individuals to synchronize footsteps with visual or auditory cues--in other words, asking for continuous voluntary control of their gait--modifies the inter-stride variability structure [24; 67; 68; 69]. In contrast, the results of transfer learning (Figure 4) strongly suggest that these attentional changes did not impact gait recognition. Indeed, when only one segment per individual is used to learn new gaits, it must come from only one experimental condition. However, it was possible to correctly classify gaits from other experimental conditions.