Furthermore, foodservice machinery 5, 12, 13, home appliances 14 or food manufacturing 15 industries also have interest in these data to develop autonomous/automatic systems to flip food.

Such data are, in fact, useful for various scientific fields including cooking education 3, automation and robotics science 2, 4, or human activity science 9, 10, 11. Specifically, kinematic and kinetic data would help characterizing their strategies 8. Yet, the strategies of professional, expert cooks were never investigated or modeled, e.g., with the motion primitives’ paradigm 6, 7. This is still a poorly understood and rarely automated action 5: past studies exclusively addressed hamburgers 2 or pancakes 4 flipping state-of-the-art methods to automate this movement have a limited robustness to different and uncertain levels of stickiness and slipperiness 2. This movement is applied to different food ( e.g., meat, vegetables, bread, eggs) 2, 3, 4 with heterogeneous characteristics ( e.g., greasy, adherent, deformable) that influence the movement stability, duration and outcome 1, 2. The reported dataset contains valuable measurements (1) to characterize and model flipping movements as performed by humans, (2) to develop bio-inspired methods to control a cooking robot, or (3) to study new algorithms for human actions recognition.įlipping food, typically when cooking, is not straightforward, either for humans 1 or robots 2. The action of flipping food is, indeed, gaining the attention of both researchers and manufacturers of foodservice technology. Complementary, the 2D position of food was computed from the videos. These data were collected using a custom experimental setup that allowed the execution of flipping movements with freshly cooked food, without having the sensors near the dangerous cooking area. The pinching force of the tweezers, the bending force and torsion torque of the spatula were also recorded, as well as videos and the subject gaze. The 3D trajectories of strategic points in the utensils were tracked using optoelectronic motion capture. This paper presents a multivariate dataset of 2866 food flipping movements, performed by 4 chefs and 5 home cooks, with different grilled food and two utensils (spatula and tweezers).

Culinary fundamentals day 24