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# SCONE Concepts

### SCONE Scenario

In SCONE, everything needed to perform a predictive simulation is bundled in what is called a **scenario**. Each SCONE scenario consists of the following components:

- A musculoskeletal
**model**of the human or animal, which is used in the simulation. - A
**controller**, which generates input for the model actuators. - An
**objective**, which describes the goal task for which you wish to optimize, through a weighted combination of**measures**. - An
**optimizer**, which attempts to find the parameters that best perform the specified objective.

### SCONE Models

SCONE is designed to work with OpenSim for modeling and simulation. Any type of model is supported (both 2D and 3D), as long as the model contains **controllable actuators**, such as hill-type muscle-tendon units or torque-based actuators. In case a model needs to interact with the environment, it must also contain **collision shapes** and **contact models**. This is needed to compute the reaction forces during the forward dynamics simulation. Finally, models should typically contain **limit forces** to simulate the forces generated by tendons and bony structures when a joint reaches a certain limit.

Models used for predictive simulations should be as simple as possible. The reason for this is threefold: 1) Models with less degrees-of-freedom are more restricted in their motion, making it easier to find an optimum; 2) The less actuators, the less control parameters are needed during the optimization; and 3) Simulation performance decreases with model complexity, so optimizations will run much quicker with simpler models.

### SCONE Controllers

Controllers compute the input values of the model actuators.

In SCONE, controllers consist of modular components that can be combined to perform complex behaviors.

### SCONE Objectives and Measures

Measures define the *fitness* or *quality* of a simulation. The goal of the optimizer is to minimize (or maximize, depending on the type) the measure.