This paper addresses the problem of motion analysis performed from digital data captured by a network of motion sensors scattered over a field of interest where 3D+T motion analysis is per- formed. Motion analysis, as referred here for digital signals, proceeds through consecutive steps of detection, motion-oriented classification, parameter estimation and tracking. The scheme proposed in this paper is relevant to applications that can be found in medicine, earth science, surveillance and defense. The major challenges involved in the feasibility of this network are as follows: signal sampling from a sensor network, photodetection and optimal strategy for cope with energy harvesting and wireless communication capabilities. The motion sensors implement wireless communications to some gateway or data sink that relays the collected information to a remote central station. Motion sensors are assigned to catch motion with high sensitive sparsely distributed sensors and to build the trajectories. Other sensors can be added to the system for specific purpose like video camera. Video cameras are assigned to catch high resolution images or videos with densely and regularly distributed sensors to perform pattern classification and recognition. The central station implements the motion analysis algorithm. Motion analysis is performed as a dual control referring to both an accurate model based on theoretical mechanics and an adaptive learning system based on a supervised neural network. This paper describes the effective components of the system which are namely the sensor layer, the telecommunication layer, and the application layer.