1. Introduction PART I: Neuroscience perspective on motor recovery 2. Clinical perspective on functional gait disorders 3. Motor learning - what constitutes, enables, and improves outcomes in neuro-impaired individuals PART II: Opinion pieces on the main technology 4. Closing the loop between wearable technology and human biology 5. Crunching through data - how machine learning is transforming human movement analysis 6. Challenges in making neuromusculoskeletal models clinically useful PART III: The role of human biomechanics in motor recovery 7. The outcomes and lessons from a constrained walking study 8.

Motion and joint function in human gait 9. The role of muscle synergies in maximizing motor recovery 10. Optimality in human gait - the role of symmetry in motor learning 11. Error augmentation and haptic interventions during motor learning PART IV: Technology-assisted motor function recovery 12. An overview of technology-assisted gait rehabilitation 13. Predictive simulations for better understanding neuromechanics of gait 15. Portable gait lab - taking mocap into clinical and community environments 16. Analyzing human gait using machine learning and explainable artificial intelligence 17.

Concluding remarks.