Until now, the robots environment was characterized for being parametrized. This means, that all tasks must be defined and the robot only needs to repeat the programed action continually. Furthermore, the robot and the user don't share the same workspace. But now, new environments have appeared, bringing a new robotic paradigm where the robots and the users share the same workspace in semistructured environments. The tasks in this new paradigm have a plasticity, where they evolve depending on the user. It is therefore important that the system evolves at the same time than the user. In this project, a solution of an activity recognition system to be used in a robotic assistance on domestic environments is presented. This system works in real-time to monitor the user's activities at the same moment that the action is occurring, computing an early prediction and offering to the system future information about the possible necessities of the user's to finalize the activity that the system predicted. The solution proposed in this project must solve the following sub-objectives: 1. Object Segmentation: The system separates the objects from the scene. We used one model of scene to compare the differences between the model and the actual frame. 2. Object Recognition: The system recognizes what objects are involved in the action. To capture the characteristic points of the objects, we first use the SURF Key-Point of the objects and, then, we convert this information in the Bag of Features representation. In the second step of the process, we use a Support Vector Machine (SVM) to build the classifier structure. 3. Object Tracking: The system obtains the dynamic information of the objects in the scene. The system uses a CamShift algorithm to monitor the state of the objects. 4. Activity Recognition: The system identifies the action that the user is doing taking the previous information into account. Other important issues we need to take into account in the development of this project are: · Scalability: The system needs to be able to grow so that it can consider more cases. The installation of new hardware or the use of new algorithms are two examples of this. · Working in Real Time: The system needs to predict the actions at the same time than they are happening. · Consideration of Different Type of Objects: In domestic scenarios, different types of objects with different characteristics need to be considered. For example, labeled objects, smooth surfaces with a dominant color and textured objects. · The Use of Standard Robot Operation System: We use the Indigo ROS version to develop this project and share the intermediate information of the project so that other projects can use it. The results obtained applying all the process is an accuracy detection of the activity only using the static information. The dynamic information we used to increase the detection of the more complex activities or more complex situations, and the actual version of the project grows in complexity with respect to the previous project but it needs to grow more to take the results of this project into account to increase the trustworthiness of the project.