# A Smart Motor Rehabilitation System Based on the Internet of Things and Humanoid Robotics
## 2 Related Works  
Many studies make use of wearables for early detection and diagnosis, to improve elderly care and reduce physiotherapy visits. In a recent work [6], researchers explored the value of wearable sensor data for timely prediction in stroke recovery patients, employing IMU sensors to collect movement and balance data. Participants were divided into ambulatory and non-ambulatory groups. By applying supervised ML classifiers, three models were trained to combine patient information with clinical and sensor data to predict outcomes. According to a review [7] of stroke rehabilitation research from 2009 to 2023, accelerometers (the most common) and gyroscopes paired with ML can improve stroke rehabilitation and remote monitoring. The study explores several ML techniques, including reinforcement learning, supervised, and semi-supervised learning. [PERSON] et al. [9] presented a sensor-based 3D motion capture method that employs four IMU sensors placed on the trunk, pelvis, upper leg, and lower leg, to measure joint movement during a single-leg squat, demonstrating good to excellent concurrent validity with a conventional 3D motion capture system. Similarly, [PERSON] et al. [8] used two IMU sensors, placed on the wrist and upper arm, to measure the range of motion of the shoulder. Additionally, a Raspberry Pi served as a gateway, managing data collection, synchronization, and processing through dedicated software. [PERSON] et al. [19] proposes a belt with IMU sensors and a High-Definition camera to highlight the need for posture monitoring to prevent spinal issues and musculoskeletal discomfort. Sensors are placed in the thoracic and thoracolumbar corners to monitor the flexion angle of the neck and upper back. The collected data are transmitted to a cloud server, and users can view and track their posture via a mobile application, enabling therapists to provide accurate follow-up and guidance. The solution described in [10] captures the therapist's movements, gathers data using RGB-Depth cameras, and allows therapists to record specific rehabilitation movements via a gesture-based Natural User Interface (NUI). These motions are then replicated by an exoskeleton robot on a patient. Future developments aim to integrate ML for automatic error detection during exercises and potentially provide feedback. [PERSON] et al. [20] propose a robot-assisted wrist physical rehabilitation system that uses accelerometer and magnetometer sensors embedded in a 3D-printed wearable band to monitor wrist movements such as flexion, extension, abduction, and adduction in stroke patients. By synchronizing the robot's actions with sensor feedback, it ensures precise execution of exercises, and can operate independently. Data are uploaded to a cloud server, enabling clinicians to track progress and adjust treatment plans based on feedback. The system supports both active rehabilitation and therapist-assisted use. The study presented in [11] aims to help paralyzed patients navigate smart cities with automatic limb control, suggesting a lightweight, intelligent exoskeleton system that employs data from multiple sensors and cameras to detect parameters like distance, obstacles, orientation, speed and acceleration. The system uses Artificial Neural Networks (ANNs) for data classification and AI-powered navigation for real-time motion control and prediction. While some data are processed locally to enable real-time corrective movements and prevent falls, cloud-based feedback is provided for remote monitoring and assistance. In this context, the proposed work aims to develop an IoT system that facilitates synchronized motor rehabilitation. I-TROPHYTS jointly monitors patient movements and vital signs via non-invasive IoT devices to conduct physiotherapy sessions independently at home, while receiving real-time semi-autonomous supervision of their rehabilitation activities. Table 1 summarizes our literature review by comparing our solution with the state of the art.