This work is dedicated for research on new methods for three dimensional shape data acquisition applicable in mobile embedded systems with limited resources. The aim is to facilitate development of new cyber-physical system applications in fields such as flexible electronics, robotics, smart textile and wearable electronics. A particular attention is paid to application of miniature low-power, low-cost hardware, efficient data acquisition and processing that can provide real-time operation. Also, the work focuses on methods that enable ubiquitous operation and does not rely on external infrastructure. In this work a novel method for shape sensing is proposed that is based on 3-axis acceleration and magnetic sensor nodes that are embedded into the material and can measure local orientation data. The proposed algorithm for global shape reconstruction from local orientation measurements ensures fast computations utilizing data from large number of sensors. Detailed simulations are provided to demonstrate feasibility of the proposed method. Additional contribution is related to new network architecture that allows efficient data acquisition from more than 200 low-power sensors that is required for proposed shape sensing algorithm. Also, practical implementation of proposed methods is demonstrated and shape reconstruction performance is evaluated in experimental tests by comparison to commercial Kinect v2 sensor that can act as three dimensional scanner. In the end of the work approbation of proposed methods in mobile cyber-physical system for medical applications is demonstrated. Real-time posture monitoring and feedback system is developed using proposed methods and applied for medical studies in collaboration with medical specialists, demonstrating the applicability of the method in practical mobile embedded systems.