Computational Foresight Using Machine Learning

We propose a machine learning-based system named “Computational Foresight” that can forecast human body motion 0.5 seconds before the actual motion in real-time. This forecasting system can be used to estimate human gestures in advance to the actual action for reducing delays in interactive system. In addition, the system can be applied to instruct sports actions properly, and prevent elderly from falling to the ground, and so on. Proposed system detects 25 human body joints to use those data for input dataset of machine learning. We created 5-layered neural network to estimate human body motion in real-time. In our experiment, we measured jump motions of subjects for learning. In our evaluation, the prototype system scored that the center of gravity of whole body can be forecasted 0.5 sec before with its accuracy of 12.8 cm.

Confference Paper: Yuuki Horiuchi, Yasutoshi Makino, and Hiroyuki Shinoda. 2017. Computational Foresight: Forecasting Human Body Motion in Real-time for Reducing Delays in Interactive System. In Proceedings of the 2017 ACM International Conference on Interactive Surfaces and Spaces (ISS ’17).

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