## 3 Design and Implementation
### Classification Algorithm
Second, a more efficient way of processing the signal is needed for the system to be improved in terms of usability. The latency for each haptic gesture currently needs to be long, with 1500 ms needed to obtain enough signals to be classified. There needs to be another 1000 ms latency between each haptic gesture in addition for reliable data gathering. Users would expect the authentication procedure to be completed as fast as finger scanning or password typing on a keyboard. To make the authentication system usable and convenient, the latency between each haptic gesture should be far less than one second at maximum while maintaining the current accuracy level. At the same time, the diversity of gestures needs to be improved for the authentication process to make the system more secure. With at least ten distinct and concrete haptic gestures, the current system can completely replace any authentication system that uses a numeric key input for passwords as each gesture can be mapped to each Arabic number for the complete input. Furthermore, additional gestures will make it harder for copying attackers to replay the haptic passcode. Third, the classification rates of some difficult-to-make haptic gestures, such as the VF180 gesture, need to be improved. Those gestures should be generated with higher classification rates from a less-strictly-defined range of motions. Limited choices in haptic gestures and potentially frequent errors are possible reasons for users to avoid the suggested system of making haptic passcodes. Further studies are in progress to make a secured authentication system by adding additional haptic gestures to the current structure, for example, \"double tap\" and \"rotation\" of the ID tag. Another study is also underway to improve the user customization by utilizing unique personal characteristics during haptic gestures. Personal biometric characteristics such as the speed of manipulation, the location to tap and hold, and the force grabbing the ID tag can be extracted by observing the behaviors of each user over an extended period of time, and these may provide a lead to finding personal habits and preferences. Haptics can provide rich data for extracting unique personal characteristics, just like biometric data, such as fingerprints, facial patterns, and irises, which are also used for authentication and identification purposes [30]. Those extracted characteristics can be used to make more personal and secure haptic passcodes. Such personal factors uniquely characterizing the user can add another layer of security to prevent security breakdown by attackers [15; 17; 19; 20; 21; 22; 23]. Conceived the concept and surveyed references, S.L. ; Designed and performed the experiments, S.L., K.S. ; Analyzed the data, S.L., K.S., S.K. ; Wrote the original draft, S.L., S.K. ; Edited the paper, S.K. This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2019-2015-0-00742) supervised by the IITP (Institute for Information & communications Technology Promotion). The authors declare no conflict of interest. ## References

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