Human Pose Estimation-Based Real-Time Gait Analysis Using Convolutional Neural Network

Gait analysis is widely used in clinical practice to help in understanding the gait abnormalities and its association with a certain underlying medical condition for better diagnosis and prognosis. Several technologies embedded in the specialized devices such as computer-interfaced video cameras to measure patient motion, electrodes placed on the surface of the skin to appreciate muscle activity, force platforms embedded in a walkway to monitor the forces and torques produced between the ambulatory patient and the ground, Inertial Measurement Unit (IMU) sensors, and wearable devices are being used for this purpose. All of these technologies require an expert to translate the data recorded by the said embedded specialized devices, which is typically done by a medical expert but with the recent improvements in the field of Artificial Intelligence (AI), especially in deep learning, it is possible now to create a mechanism where the translation of the data can be performed by a deep learning tool such as Convolutional Neural Network (CNN). Therefore, this work presents an approach where human pose estimation is combined with a CNN for classification between normal and abnormal gait of a human with an ability to provide information about the detected abnormalities form an extracted skeletal image in real-time.