# A Smart Motor Rehabilitation System Based on the Internet of Things and Humanoid Robotics
By introducing controlled automation in this domain, I-TROPHYTS aims to enhance the overall sustainability of healthcare systems, as a single physiotherapist could supervise multiple patient groups simultaneously. Furthermore, by employing non-invasive IoT monitoring techniques together with edge-based processing [15; 16], we can continuously track individual patient performance over time and identify correlations that could lead to personalized therapies. The use of humanoid robotics may also increase patient engagement due to its morphological similarities to humans [17; 18]. However, realizing this concept requires the development of an innovative hardware/software framework with contributions spanning multiple fields, including IoT, knowledge representation and robotics. The I-TROPHYTS framework ensures IoT data acquisition from two types of wearable devices (IMU-based and vital-sign sensors), and integrates ML algorithms for exercise recognition and motion tracking. Moreover, it enables the representation of physiotherapy session data--including both patient states and domain-specific rules--via a uniform and unambiguous knowledge representation in the form of an ontology. This ontology facilitates reasoning and decision-making by the humanoid robot. To summarize, the key contributions of this paper are as follows:  
* We present the concept behind I-TROPHYTS for next-generation smart rehabilitation and the layered architecture that supports its implementation. * We describe in detail the current development phase of the I-TROPHYTS framework, with a focus on the IoT data acquisition and processing pipeline, as well as the robotic architecture. * We report experiments, involving multiple volunteers performing up to four motor routines, to assess the ability of the proposed IoT and ML-based techniques to automatically identify and distinguish between different exercises. Our results demonstrate that a performed exercise can be accurately identified by leveraging IMU data from wearables. In particular, we achieve 100% accuracy in distinguishing a shoulder abduction from a shoulder flexion, using IMU data from IoT wearable devices placed on the wrist and elbow of our test subjects. We also show that the I-TROPHYTS framework can discern the exercise with high accuracy (>90%) employing just one signal (acceleration or gyroscope) from a single IoT sensor (placed on the wrist or elbow of an arm). This proves that the exercise being performed can be identified, even if the active arm is not equipped with a sensor. The paper is organized as follows. Section 2 reviews the state of the art of IT solutions for rehabilitation systems, focusing on IoT and ML-based approaches. Section 3 introduces the I-TROPHYTS concept and components of the framework. Section 4 details the current design and development phase of the framework, focusing on the IoT sensing and edge processing layers. Section 5 describes experiments and performance results. Section 6 includes a discussion on ongoing work on data modeling and integration with robotic actuation. Section 7 concludes the paper.