The goal of this project is to develop an automated assistive device capable of restoring walking and standing functions in persons with motor impairments. Although research on assistive devices, such as active and passive orthoses and exoskeletons, has been ongoing for several decades, the improvements in mobility have been modest due to a number of limitations. One major challenge has been the limited ability to sense and interpret the state of the human, including volitional motor intent and fatigue. The proposed device will consist of powered electric motors, as well as the power generated by the person's own muscles. This work proposes to develop novel sensors to monitor muscle function, and, muscle fatigue is identified, the system will switch to the electric motors until the muscles recover. Through research on methods of seamless automated control of a hybrid assistive device while minimizing muscle fatigue, this study addresses significant limitations of prior work. The proposed project has the long-term potential to significantly improve walking and quality of life of individuals with spinal cord injuries and stroke. The proposed work will also contribute to new science of cyber-physical systems by integrating wearable image-based biosensing with physical exoskeleton systems through computational algorithms. This project will provide immersive interdisciplinary training for graduate and undergraduate students to integrate computational methods with imaging, robotics, human functional activity and artificial devices for solving challenging public health problems. A strong emphasis will be placed on involving undergraduate students in research as part of structured programs at our institutions. Additionally, students with disabilities will be involved in this research activities by leveraging an ongoing NSF-funded project. This project includes the development of wearable ultrasound imaging sensors and real-time image analysis algorithms that can provide direct measurement of the function and status of the underlying muscles. This will allow development of dynamic control allocation algorithms that utilize this information to distribute control between actuation and stimulation. This approach for closed-loop control based on muscle-specific feedback represents a paradigm shift from conventional lower extremity exoskeletons that rely only on joint kinematics for feedback. As a testbed for this new approach, the team will utilize a hybrid exoskeleton that combines active joint actuators with functional electrical stimulation of a person's own muscles. Repetitive electrical stimulation leads to the rapid onset of muscle fatigue that limits the utility of these hybrid systems and potentially increases risk of injury. The goals of the project are: develop novel ultrasound sensing technology and image analysis algorithms for real-time sensing of muscle function and fatigue; investigate closed-loop control allocation algorithms utilizing measured muscle contraction rates to minimize fatigue; integrate sensing and control methods into a closed loop hybrid exoskeleton system and evaluate on patients with spinal cord injury. The proposed approach will lead to innovative CPS science by (1) integrating a human-in-the-loop physical exoskeleton system with novel image-based real-time robust sensing of complex time-varying physical phenomena, such as dynamic neuromuscular activity and fatigue, and (2) developing novel computational models to interpret such phenomena and effectively adapt control strategies. This research will enable practical wearable image-based biosensing, with broader applications in healthcare. This framework can be widely applicable in a number of medical CPS problems that involve a human in the loop, including upper and lower extremity prostheses and exoskeletons, rehabilitation and surgical robots. The new control allocation algorithms relying on sensor measurements could have broader applicability in fault-tolerant and redundant actuator systems, and reliable fault-tolerant control of unmanned aerial vehicles.