Besides oil, coffee is one of the most traded commodities worldwide. Colombia is known as the producer of the highest quality coffee in the world, thanks to its smooth taste and aroma. One of the key elements that are responsible for the quality of Colombian coffee is its harvesting method, in which it is enforced that only mature fruits are harvested. Given the terrain conditions in which coffee trees grow, the preferred harvesting method in Colombia is selective hand picking, in which each coffee grain is individually teared off from the branch that is attached to. This work focuses on the analysis of the motion of a human hand performing the action of manual selective coffee harvesting. The analysis is based on the data collected from a custom made motion capture system, which consists of a glove capable of sensing the angular movement of the joints, and accelerations at the tip of the fingers, by means of a set of flex sensors and accelerometers, respectively. The methods followed in this investigation include the study of the biomechanics of the hand, as applied to the motion of hand picking of coffee, which proved to be fundamental for the analysis of the experimentally measured data. After processing the experimental data, the patterns of movement done by a human coffee harvester can be simulated and replicated, which allows identifying trajectories that a good harvester follows, as compared to other harvesters, which collect smaller amounts of grains during the same period of time. After having parameterized the motion of efficient selective hand picking, the results from this investigation serve as the basis for the design and optimization of an electromechanical tool to assist in the process of coffee harvesting, which minimizes the amount of green beans removed from the branches of the coffee trees.