My research group focuses on enhancing walking performance in healthy and disabled populations with impaired gait. The core of this work is to develop personalized task-oriented wearable gait training systems that guide the user in modifying their gait biomechanics towards improved walking performance. To personalize these training devices this work aims to develop a method based on a human-in-loop AI data-driven optimization model to optimize the training parameter of the system based on the multi-objective function criterion for each individual separately. In addition, to make training accessible to everyone this work aims to develop wearable robot-assisted and user-guided biofeedback training systems that provide beyond-laboratory gait training solutions for home-based training for the people from remote areas having limited accessibility to the rehabilitation clinics in cities. Overall, this work provides a complete solution to enhance walking performance by maximizing the gait training outcome in terms of its speed, effectiveness, and accessibility. I am also interested in developing wearable gait biomechanical sensing devices using off-the-shelf sensors and signal processing pipelines for gait monitoring/assessment and to quantify the effect of rehabilitation efforts. I am also interested in developing gait assistive devices such as orthosis, exoskeleton, and prosthesis and machine learning methods for gait activity classification for assistive device control applications. I am also interested in understanding human motor adaptation mechanisms during goal-oriented gait training using controlled human subject experiments.