This project is granted by the research initiative 2020 de «Proyectos de I+D+i» de los Programas Estatales de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i y de I+D+i Orientada a los Retos de la Sociedad.
Robot-assisted rehabilitation therapy has been proven to effectively improve the patients motor function and it is demand is increasing annually. However, one of its major limitations are their complexity in operation, robustness, and difficulty of therapies personalization that sometimes explain the concerns of clinicians to use this technology in their daily practice. Automating the rehabilitation cycle, by introducing robotic assistance will help to and homogenize protocols, performing automatic recording of outcomes and rationale processes to increase sample size in research studies. Moreover, a common problem remains if perform the therapies in an open-loop manner without getting patients in the control loop and considering them an homogenous entity. If there is no adjustment of the system to each particular individuals , then patients are forced to adapt themselves to the system capabilities. In contrast, in the same way that a skilled physiotherapist does intuitively, a smart robotic assistance should modulate their behaviour according to the user's intention, action, state, as well as emotions, providing feedback to perform a bidirectional adaptation. An automatic administration of robotic therapies will need this this smart skill to successfully deal with complex situations such as relieve pain, reduce load on painful joints and
muscles, or detect patient demotivation.
The main objective of this project is the development of smart robotic assistance systems for efficient assessment and personalized rehabilitation using innovative control strategy with bidirectional multisensorial feedback for both robot and patient. The system will use collaborative robot IIWA with hybrid position/force control that will act as passive element during the assessment phase and as active one during the rehabilitation. At the same time, the patient will be equipped with the multisensorial system of two types: a) embodied easy to wear sensors that measure his/her medical parameters during rehabilitation such as IMU+EMG signals, O2 saturation, hearth pulse, and arms/trunk poses, and b) external cameras that analyse the patient's face expression, compensatory movements and postures, to infer during execution of exercises pain, placidity, fatigue, and later the acceptance and adherence of the therapy.
The physical patient-robot interaction will be dynamically measured and adjusted based on robot sensors feedback and adjustment of predefined musculoskeletal model of patients upper limb features, according the evolution of the therapy (velocity of the upper limb motion and applying 3D forces). It mixes in bidirectional way information provided by the robot sensors to adapt the patients exercise and, viceversa, use the patients data to adjust the robot motion. It also mix objective (sensor measurements) and subjective (deduced by observation) data during the therapy. This will be implemented by AI techniques by learning from previous measurements/observations and the development of a descriptors set for each pathology. This paradigm is based on a multidisciplinary approach applied learning to robot assisted therapy that exploits automatic assessment, machine learning and gamification technologies capabilities to dynamically adapt the rehabilitation execution and prescription during the therapy, helping therapists to be more precise, efficient and cost effective at resource use and increase patient adherence to the therapies.