NEURAL NETWORK TO IDENTIFY KEY POINTS OF THE HUMAN POSE IN PHYSIOTHERAPY REHABILITATION OF GAIT

This work proposes a digital system with cloud neural network with video acquisition to obtain key points of the human pose in patients undergoing physiotherapy rehabilitation of lower limbs in gait mobility, allowing agility in the records, minimizing measurement errors, analysis clinical condition, and the exchange of information between professionals in the field, resulting in a shared knowledge base. A digital camera, 1080p and 60fps, used parallel to the ground, with height adjustment (0.6 to 2.4m), 1.5m from the analysis field (3x3m to 3x10m) is used for measurement, rehabilitation or walking procedures. Values manually entered by the physiotherapist are applied for calibration and definition of limits for comparison with the data obtained by the system. In relation to the current ones, the advantage is given by digitally obtaining anatomical points and body segments while in manuals they allow greater error and too much time in this collection. The system is an auxiliary tool for the physiotherapist to supply the manual limitation and sharing, with agility among professionals, historical data and qualitative analyzes that accurately generate a patient profile and allow the adaptation of the procedures applied with agility, since it provides an immediate graphic analysis.