Toward Personalized Vibrotactile Support When Learning Motor Skills

• Published in: Algorithms Vol. 10; no. 1; p. 15
• Main Author: Santos, Olga
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 2017
• Subjects: big data; Data acquisition; Data management; Feedback; Learning; Motor ability; motor skills; personalization; psychomotor domain; quantified self; Skills; TORMES methodology; User interfaces; vibrotactile feedback; wearable technology
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a10010015

Personal tracking technologies allow sensing of the physical activity carried out by people. Data flows collected with these sensors are calling for big data techniques to support data collection, integration and analysis, aimed to provide personalized support when learning motor skills through varied multisensorial feedback. In particular, this paper focuses on vibrotactile feedback as it can take advantage of the haptic sense when supporting the physical interaction to be learnt. Despite each user having different needs, when providing this vibrotactile support, personalization issues are hardly taken into account, but the same response is delivered to each and every user of the system. The challenge here is how to design vibrotactile user interfaces for adaptive learning of motor skills. TORMES methodology is proposed to facilitate the elicitation of this personalized support. The resulting systems are expected to dynamically adapt to each individual user’s needs by monitoring, comparing and, when appropriate, correcting in a personalized way how the user should move when practicing a predefined movement, for instance, when performing a sport technique or playing a musical instrument.