User:MichelangeloGuaitolini/sandbox

Centralized versus decentralized
In sensor fusion, centralized versus decentralized refers to where the fusion of the data occurs. In centralized fusion, the clients simply forward all of the data to a central location, and some entity at the central location is responsible for correlating and fusing the data. In decentralized, the clients take full responsibility for fusing the data. "In this case, every sensor or platform can be viewed as an intelligent asset having some degree of autonomy in decision-making."

Multiple combinations of centralized and decentralized systems exist.

Another classification of sensor configuration refers to the coordination of information flow between sensors. These mechanisms provide a way to resolve conflicts or disagreements and to allow the development of dynamic sensing strategies. Sensors are in redundant (or competitive) configuration if each node delivers independent measures of the same properties. This configuration can be used in error correction when comparing information from multiple nodes. Redundant strategies are often used with high level fusions in voting procedures. Complementary configuration occurs when multiple information sources supply different information about the same features. This strategy is used for fusing information at raw data level within decision-making algorithms. Complementary features are typically applied in motion recognition tasks with Neural network, , Hidden Markov model , Support-vector machine  , clustering methods and other techniques. Cooperative sensor fusion uses the information extracted by multiple independent sensors to provide information that would not be available from single sensors. For example, sensors connected to body segments are used for the detection of the angle between them. Cooperative sensor strategy gives information impossible to obtain from single nodes. Cooperative information fusion can be used in motion recognition, gait analysis, motion analysis , ,.

Levels
There are several categories or levels of sensor fusion that are commonly used.*


 * Level 0 – Data alignment
 * Level 1 – Entity assessment (e.g. signal/feature/object).
 * Tracking and object detection/recognition/identification
 * Level 2 – Situation assessment
 * Level 3 – Impact assessment
 * Level 4 – Process refinement (i.e. sensor management)
 * Level 5 – User refinement

Sensor fusion level can also be defined basing on the kind of information used to feed the fusion algorithm [gravina 2017]. More precisely, sensor fusion can be performed fusin raw data coming from different sources, extrapolated features or even decision made by single nodes.


 * Data level - data level (or early) fusion aims to fuse raw data from multiple sources and represent the fusion technique at the lowest level of abstraction. It is the most common sensor fusion technique in many fields of application. Data level fusion algorithms usually aim to to combine multiple homogeneous sources of sensory data to achieve more accurate and synthetic readings . When portable devices are employed data compression represent an important factor, since collecting raw information from multiple sources generates huge information spaces that could define an issue in terms of memory or communication bandwidth for portable systems. It should be noted that data level information fusion tends to generate big input spaces, that slow down the decision-making procedure. Also, data level fusion often cannot handle incomplete measurements. If one sensor modality becomes useless due to malfunctions, breakdown or other reasons the whole systems could occur in ambiguous outcomes.


 * Feature level - features represent information computed onboard by each sensing node. These features are then sent to a fusion node to feed the fusion algorithm . This procedure generates smaller information spaces with respet to the data level fusion, and this is better in terms of computational load. Obviously, it is important to properly select features on which define classification procedures: choosing the most efficient features set should be a main aspect in method design. Using features selection algorithms that properly detect correlated features and features subsets improving the recognition accuracy. However, usually large training sets are required to find the most significan feature subset.

. It is the highes level of abstraction and uses the information that has been already elaborated through preliminary data- or feature level processing. The main goal in decision fusion is to use meta-level classifier while data from nodes are preprocessed by extracting features from them . Typically decision level sensor fusion is used in classification an recognition activities and the two most common approaches are majority voting and Naive-Bayes . Advantages coming from decision level fusion include communication bandwidth and improved decision accuracy. It also allows the combination of heterogeneous sensors.
 * Decision level - decision level (or late) fusion is the procedure of selecting an hypothesis from a set of hypotheses generated by individual (usually weaker) decisions of multiple nodes

Homework:

Passive prostheses show better aesthetic features than those of mechanically powered devices. They do not help the user as they don’t have active component. For this reason, they normally imply abnormal biomechanics and require more metabolic energy to walk at the same velocity as non-amputees. Variable-damping prostheses adapt to different modes of gait modulating their damping level through a shared control between the user and a smart algorithm. They guarantee a better stability and adaptation to different ground surfaces. Quasi-passive prostheses are recently becoming more popular including commercially available devices. . Active prostheses can change their dynamic depending on the activity performed. These solutions involve the use of various type of sensors and actuation units. They usually exploit data from sensors like EMG electrodes, accelerometers and gyroscopes. Recent active prostheses embed classification algorithms that use machine learning techniques for the prediction of users' locomotion intention in order to adapt prosthetics actuation to the subject' biomechanics.