Graduate Research Thesis
Shared Reality Lab, McGill University, Montreal, Canada
Learning a new motor skill typically requires repeated physical training, cognitive training, and retention. When learning a particular form of dance, it is critical to have a precise idea of rhythm, spatial movement, and body posture during the performance. Often these factors emerge while practicing movements intermittently. However, in the process of traditional dance learning in a classroom, it is difficult for novice dancers to follow the specifics of rhythm, spatial movement and body posture, while in sync with the instructor, at a defined pace. The situation becomes more difficult in large class sizes. We propose a haptic footwear based system to assist novice dancers with the motor learning procedure.
We are designing smart footwear that provides haptic cues corresponding to both the spatial movements and the rhythm of the dance. The haptic cues will be rendered on four different points on the bottoms of the feet through vibrotactile actuators. The shoes will also measure the force profile and movement dynamics by using FSR sensors and inertial measurement units, respectively. The two main research contributions are -
1. Exploring the dimensions of haptic cues corresponding to the dance steps, delivered intuitively through the smart footwear while remaining unaffected by the high impact movements
2. Using force sensor data and accelerometer readings from a professional dancer to create a dance step profile which can be leveraged to establish a baseline for feedback.