Biofeedback Based System to Enhance Robotic Assisted Gait Training in Children With Cerebral Palsy

The AID2GAIT project aims to develop a biofeedback system with the aim of improving the outcomes of robot-assisted gait training (RAGT) in pediatric patients with cerebral palsy. The physiological signals of children during RAGT therapy sessions, acquired through non-invasive technologies, will be analyzed. These technologies specifically are: * wearable technology (smartwatch), from which the HRV (Heart Rate Variability) signal will be measured; * infrared thermography, from which the temperature in salient facial regions will be obtained; * fNIRS (functional near-infrared spectroscopy), from which information on brain activity and its changes over time will be obtained. Information on the kinematics of the exoskeleton used during RAGT will be extracted. The RAGT will be performed using the Lokomat orthosis (Hocoma), the most widely used exoskeleton in rehabilitation that facilitates a bilaterally symmetrical gait, as the individual actively tries to advance each limb during walking, combined with a patented dynamic body weight support system.

The entire RAGT treatment will consist of 12 sessions of robotic gait rehabilitation administered over 4 weeks. The protocol consists of a block paradigm: children will be asked to actively move during the robotic training for the first 30 seconds, followed by a rest period of 30 seconds. The paradigm will be provided in 10 blocks for a total duration of 30 minutes. During each single session, infrared (IR) thermal video, smartwatch signals (HRV- heart rate variability) and the kinematic output of the robot will be recorded. During the first (T1) and the last session of the RAGT (T12), fNIRS will also be recorded together with the assessment of motor skills, revealed by clinical scales. The assessment of fNIRS and clinical scales will be useful to understand the global efficacy of the treatment. Before the start of the experimental trials, parents will be extensively informed about the purpose and protocol of the study and will sign an informed consent form. For each session, the assessment of the psychophysiological state of the patients will be based on the estimation of the state of physiological parameters, such as heart rate variability (HRV), recorded by a smartwatch and on the assessment of the emotional state of the child by means of an infrared thermal imaging system. The information on the state of the child and the robot will be assessed in real time and will constitute the input data for a machine learning-based model capable of classifying the level of patient engagement. Based on this information, the physiotherapist, who assists the child during the training sessions, will be able to intervene and modify the parameters of the exoskeleton (e.g. push force, body weight support, treadmill speed, range of motion and hip and knee offset). Furthermore, the effectiveness of the entire treatment will be assessed through the administration of clinical tools commonly used in clinical practice and by the assessment of brain activation by means of a non-invasive and portable neuroimaging technique, the fNIRS. These assessments will be conducted by comparing the first and the last training session.