# Effective Behavioural Dynamic Coupling through Echo State Networks
## 5 Properties of the Echo-State Network and Human-Machine Interface
### Recognition Before the End of the Sequence  
An important feature of the system presented in this work is that it provides the correct classification before the input sequence is completed. Given the feedback from the output is fed to the reservoir, the network is able to stabilise its dynamics and recognise a given pattern at an early stage of its presentation. This feature allows the system to have a fast response to the sequence in input, making it appealing for real time control cases.  
Figure 5 shows an example of the recognition of the \(pull\) (ID 1) sequence of the dataset. Similar behaviour is also shown by the network for the other sequences as well. That is, the network is able to classify the sequences before their completion. The time steps required for the network to settle to a sequence can vary. This is expected as the sequences do not share the same length.  
For a more comprehensive way of how the proposed architecture captures the dynamics of the input sequence, we tested the recognition with partial input sequences. Each input sequence was used to artificially create four new sequences, each one having 25%, 50%, 75% and 100% of the original sequence. Each sequence resulted from the initial one having the same starting point but a shorter time span, by omitting the remaining elements of the sequence. In this way, a set of 28 sequences were used for testing. The accuracy of the network is measured in the same fashion as before, reporting the percentage of time the network output indicates the correct input. Each sequence was presented to the network independently, resetting the network in between the sequences presented. At the same time, sequences were shuffled so as to eliminate any of their dynamics to be retained in the ESN's reservoir  
Figure 5: The figure shows the recognition of the \(pull\) (ID 1) input sequence from the initial presentation until the stabilisation of the network’s output. In the graph the output of the network is depicted, with each colour and line style representing a pattern recognised in the input. All values are plotted against time.  
Figure 6 shows the results obtained by the test. From the bar chart it is possible to observe that the network produces the correct answer even from the initial 25% of some sequences (i.e., _IDs1_ and 2). At the same time, for most sequences it reaches a good performance with only half (50%) of the sequence being presented. When the 75% of the input sequence is presented the network is able to recognise all input patterns with a high level of accuracy, with the exception of pattern 3, which is the only one that reaches its maximum recognition rate only when the entire 100% of the pattern is presented.  
Given the unified structure for the detection and recognition provided by the ESN, input patterns are detected before their completion. This allows for fast responses from the system, a feature necessary for real time control. It is shown that humans are very sensitive to the response time of user interfaces, with lags greater than 100 ms perceived as annoying [46; 47]. Being able to provide feedback within the time span of a given input sequence, that is, during the execution of a gesture, is a challenge that ESN can achieve given the simplicity of the computations performed, which allows for very fast computation in comparison with other methods. Indeed, more complex classification systems perform even more costly computations with similar performances [15].