# Toward Personalized Vibrotactile Support When Learning Motor Skills
## 2 Background  
The training process on motor skills acquisition can follow the _learning-by-doing_ approach [4]. This active interaction between action and perception within the user environment can generate the so-called _enactive knowledge_ that is acquired by doing [5] and is stored in the form of motor responses [6]. Enactive interfaces support the transmission of knowledge obtained through action and can feed the user back through diverse senses, such as when playing a musical instrument [7]. In fact, and in addition to playing a musical instrument, there are many human activities that involve enactive knowledge and require physical practice to be learnt, such as dancing, hand writing, hand drawing, training for surgery, improving the technique in sports and martial arts, learning sign language, etc. In order to perform these activities with mastery, the person needs to practice by repeating over and over the movements involved till the appropriate way to carry them out is learnt [8], that is, she needs to train her motor skills (i.e., conscious body movements involving muscular activity).  
Thus, there is a need to guide and correct the user's movements till her ideal movement (i.e., the best way this person can perform the movement considering her particular physical features) is achieved. In fact, since each person has different goals, physical condition and motor skills, this training should be done in a _personalized way_, considering her own body build, coronal features and specific physical abilities, her current performance and the particularities of the motor skills to be learnt [9]. This means that each person should receive the advice and support that is most appropriate for her individual characteristics in her current context, which might be very different from the advice and support required by another person in a different context. In order to elicit appropriate psychomotor support, user data driven methodologies are needed. The TORMES methodology [10], which combines user centered design methods with data mining techniques, has been proposed to support the elicitation of the personalized support required when learning motor skills by guiding qualitative analysis on quantitative indicators. This adapted response needs to be triggered to the user through a given interface. The system should be able to select the most appropriate feedback modality (i.e., visual, auditory and haptic, or a combination of them) and their delivery should be personalized to the learner's situation (e.g., attention-demanding phase, cognitive overload... ) since there is not a unique best way to provide the sensorial feedback in motor learning [11].  
Anyway, haptic feedback is the one that makes the difference from non-motor skills training systems, as it can be specifically applied when physical movements are involved in the learning process [12]. This kind of feedback can be classified into [13] (1) tactile simulations (i.e., electrotactile, thermal and mechanical); (2) kinesthesic (including exoskeleton forced-feedback systems); and (3) vibrotactile (i.e., vibrating components that deliver information through temporal parameters in the signal such as frequency, amplitude, waveform and duration). In fact, vibrotactile feedback is very useful in motor skills training to help learners avoid motion errors as it provides more subtle support [13].  
Thus, this paper explores how vibrotactile feedback is to be provided in motion learning systems in terms of how the movement is monitored, compared and corrected. Findings discuss the lack of personalization support provided in the systems analyzed, and point to the application of the TORMES methodology to identify personalization opportunities when designing vibrotactile interfaces for interactive intelligent systems that can provide adaptive learning of motor skills.