## 8 Sensor Fusion  
Sensor fusion is the combination of the information obtained by acquiring data from more synchronized sensors (of the same or different types) located in the same system (body area network or wider one). In particular, it can be defined as a suitable integration of the data acquired from more sensors that can produce more accurate results, which is not otherwise possible to obtain with the same obtained data but is singularly analyzed [270]. It is increasingly widespread and discussed nowadays. This fact results from two motivations: the easy availability of more measured sensor data and the requirement for a deep and accurate analysis of complex systems. Indeed, thanks to IoT, there is a wide and easy availability of many low-cost, unobtrusive, and wirelessly interconnected sensors. On the other hand, due to the vast number of features involved in complex systems, such as, for example, human movements, only one signal acquisition can rarely provide a reliable and complete analysis of them. Sensor fusion has emerged as a rapidly advancing field of research, driven by the growing availability of various sensors. As the number of sensors placed, for example, in a BAN, still increases and, consequently, the throughput of the acquired data, a strong need exists to manage and fuse this large amount of information effectively to facilitate the human perception, understanding, and evaluation of each measured parameter. Indeed, too much data could make its comprehension very difficult, if not even impossible. On the contrary, combining and integrating these data allows for new capabilities in myriads of areas. Information obtained throughout the fusion process is more abstract than the original individual input dataset. In addition, this feature allows higher resolution of the data compared to that of each initial source of information. We expect the probability of the data to increase after the fusion process, increasing the confidence rate of the data in use. The system's reliability is also improved by combining synchronously acquired data in case of sensor error or failure. Moreover, if the source data is noisy or presents errors, a proper fusion algorithm could try to reduce or eliminate them. Furthermore, the accuracy can be improved by the parallel processing of information from multiple sensors of different natures. Indeed, if individual sensors only provide information independent of the other sensors, once they are brought into a suitable space, this will give a broad view of the whole. Consequently, the accuracy would improve if the data acquired is consistent and redundant. Finally, the several datasets must be aligned and properly converted in a common space for the fusion to occur effectively. Moreover, it is very important to avoid possible calibration errors of such sensors to make the fusion procedure possible. The algorithms exploited for sensor fusion are in probability theory, classification methods, and artificial intelligence [271]. Among the others, the main ones used are the Extended Kalman Filter, Gradient Descent Method, Explicit Complementary Filter (ECF), Gauss-Newton Algorithm (GNCF and GNKF), Bayesian Inference Technique, Support Vector Machine, Particle filter (Sequential Monte Carlo methods), Dempster-Shafer Theory of Evidence, Artificial Neural Networks, and Fuzzy Logic. The individuation of the more suitable depends on the requested level of fusion [272; 273]. One example of sensor fusion application involves combining positioning data with inertial sensor data to monitor an athlete's movements precisely. Proposed in a recent study to overcome the problems caused by non-Gaussian noise and sharp direction changes, this solution has proved to be very satisfactory [274]. In kayaking, a system based on IMUs consisting of seventeen miniature IMUs with nine DoFs was used to track the kayaker's motion. They use the gradient descent algorithm for data fusion, assessing his paddling technique and reconstructing the motion information [201].