## 6 Discussion and Conclusions  
In this work, we introduce the concept of the haptic tuning fork and we provide a methodology to create haptic scales for different types of haptic devices, and apply these concepts to create frequency-based haptic scales for the Novint Falcon haptic device, validating the utility and the improvement reached. We believe that the haptic tuning fork can be applied to amplitude-based, shape-based or hybrid scales. Vibro-tactile devices have been used to convey abstract information in the past. These approaches suffer from the same limitations as kinesthetic devices and might benefit from the haptic tuning fork. Future work will have to test these hypotheses. Previous work addressed the short-term memory limitations of the haptic channel by including an additional channel, the auditory sense. Our work presents a method for increasing the discriminability of haptic icons that exclusively involves the sense of touch. By using the haptic tuning fork, we reduce the absolute dependence on short-term memory, because the user now has a reference against which to compare the haptic signal. Employingonly the sense of touch could allow using other channels to transmit additional information or reinforce the information sent. Future work will evaluate multi-modal approaches. We describe a methodology to create haptic scales and to specify the range and the values of the parameters of the haptic icons (haptic notes). We use semi-passive time-varying haptic icons that allow conveying the signal, independently from the user's exploration speed, by modifying the shape of the object to which the haptic icon is attached. We detailed how to use the haptic icons in the simplest case, by mapping them to a surface and having the proxy always constrained to lay on that surface. However, the technique is also applicable to any other proxy-based approach, although, in more complex cases, the signal should be considered in the algorithm to calculate the virtual proxy position. The paper exemplifies the application of the methodology by calculating three frequency-based haptic scales: a three-note, a five-note, and a seven-note haptic scale, for a particular haptic device. We demonstrated that the use of a frequency-based haptic tuning fork combined with our methodology to define haptic scales improved the users' ability to discriminate different haptic stimuli. The results of our experiments are compiled in a summary table available in the Supplementary Materials. Our experiment results proved that the use of the haptic tuning fork facilitates the identification of the frequency. The frequency identification hit rate improved in our experiments around 20% for complex scales when using the haptic tuning fork. Furthermore, our experiments prove that when increasing the number of haptic icons, the effect of the haptic tuning fork becomes more relevant. We tested the haptic tuning fork in complex scenarios in which the user had to perform tasks (involving selective and divided attention) while being aware of randomly presented haptic stimuli (from a seven-note sinusoidal frequency-based haptic scale), where each individual haptic icon was perceived just once. Results in our experiment proved that the use of the haptic tuning fork led to an improvement higher than 20% in the hit rate in this kind of environment. Specifically, in one of our experiments, users were able to identify 15 different haptic icons. The scope of this work is limited to demonstrating the usefulness of the haptic tuning fork in improving the discrimination of different haptic icons. It remains for future work to investigate what would be the maximum number of distinguishable haptic icons depending on the different haptic devices. Previous works reached distinguishable sets of 84 stimuli, however, this number was reached by using rhythm [39].