Tavanyar is a system which detects and evaluates physical therapy exercises automatically using motion tracking devices. This project holds the core algorithm of the system. It uses both dynamic time warping and subsequence dynamic time warping to do the pattern matching. Different kinds of optimization techniques, such as sakoe-chiba band, and keogh lower bounding techniques are used to make the algorithm of O(n) in terms of both performance and memory usage. Finally, we introduce 9 parameters so that optimizing them, optimizes the overall functionality of the system in terms of accuracy, performance, and memory usage.

• Distance function
• Local weights
• Global constraint width Factor (sakoe-chiba band technique)
• Generalization strategy (to multi-dimensional cases)
• Down sampling step
• Length tolerance factor
• Interpolation strategy
• Lower bounding radius (keogh lower bounding technique)
• Cost threshold

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