  The biomechanical energy harvester is expected to harvest the electric
energies from human motions. A tradeoff between harvesting energy and keeping
the user's natural movements should be balanced via optimization techniques. In
previous studies, the hardware itself has been specialized in advance for a
single task like walking with constant speed on a flat. A key ingredient is
Continuous Variable Transmission (CVT) to extend it applicable for multiple
tasks. CVT could continuously adjust its gear ratio to balance the tradeoff for
each task; however, such gear-ratio optimization problem remains open yet since
its optimal solution may depend on the user, motion, and environment.
Therefore, this paper focuses on a framework for data-driven optimization of a
gear ratio in a CVT-equipped biomechanical energy harvester. Since the data
collection requires a heavy burden on the user, we have to optimize the gear
ratio for each task in the shortest possible time. To this end, our framework
is designed sample-efficiently based on the fact that the user encounters
multiple tasks, which are with similarities with each other. Specifically, our
framework employs multi-task Bayesian optimization to reuse the optimization
results of the similar tasks previously optimized by finding their
similarities. Through experiments, we confirmed that, for each task, the
proposed framework could achieve the optimal gear ratio of around 50~\% faster
than one by random search, and that takes only around 20~minutes. Experimental
results also suggested that the optimization can be accelerated by actively
exploiting similarities with previously optimized tasks.
