# Toward Personalized Vibrotactile Support When Learning Motor Skills
## 4 A Proposal for Personalized Vibrotactile Feedback to Support Motor Skills Learning
In addition, progress in data analysis is moving the paradigm of big data to the concept of big service where complex service ecosystems can speed up data processing, scale up with data volume, improve adaptability and extensibility despite data diversity and uncertainty and turn raw, low-level data into actionable knowledge [29]. In order to _deliver the adapted response_, both comparing and correcting the user's actions is required. Comparing requires the previous modelling of the user, the movement and the context. User modelling should include computing the relevant cognitive, affective and psychomotor issues from the user's interaction, resulting in knowledge regarding the current user state and skill level, as well as the effect produced by the feedback on the user. No user modelling is reported in the systems analyzed inTable 1, but it is still a general demand when developing wearable personal systems that should adapt to users' interaction [30]. In turn, movement modelling is reported in the analyzed systems as the result of training algorithms with the interactions carried out by expert users who can perform the movements with mastery. In addition, the context where the user is placed can also provide relevant information in order to deliver the vibrotactile support, as discussed, but not computed, in [19]. Current versus accurate performance (i.e., learner versus expert interactions) are to be compared once the modelling is done. Table 1 shows that ad hoc algorithms are implemented for error diagnosis, as they are much related to the domain being trained and the particularities of the movements analyzed. This process is key to triggering the adapted vibrotactile response at the corresponding user interface. From the systems analyzed, vibrotactile support is usually limited to specific movements in certain body parts, and the user is either informed when moving away from the desired performance or subtly guided on how to move the corresponding body part. This information is transmitted by modifying the amplitude or frequency of the signal. However, except for [19] that carried out interviews with instructors to understand the errors performed by users and [21] that contacted professionals to select the locations where to put the tactors, no other efforts were reported in the papers analyzed to design the support to be provided. Furthermore, the need of personalizing this support is not even an issue, with the exception of the attempt in [21] of defining the users' ideal movement. The personalization information that has been reported relates to considering users' variations when monitoring and modelling (e.g., calibrating to build the user skeletal model [16; 18; 21], and comparing normalized versions of movements [17; 18] using variables that do not depend much on users' differences [20]). However, as discussed in [9], the correcting action should take into account, among others, the specific user's features (goal, physical condition, motor skills achieved...), the type of movement and the current context of the user, so the appropriate psycho-educational support is delivered to the learner when needed (including cognitive, affective and psychomotor issues). As a result, even if two users make the same error, they may not receive the same response from the system because they may have different needs, goals, moods, etc. For instance, when the user is a novice, it might be better to be more supportive, and then decrease the support provided [21]. Nevertheless, it may also depend on the affective state [31]. If the user gets frustrated, the system may be more supportive so the user finds easier to carry out the movement, while if she is bored, it may change the movement being practiced. In addition, guiding the learner by delivering haptic guidance when the movement performed does not reflect the reference movement might not be the most appropriate psycho-educational approach to achieve long-term learning, although it might help to increase motivation by contributing to short-term performance [8].