The problem of controlling biomechatronic systems, such as multiarticulating prosthetic hands, involves unique challenges in the science and engineering of Cyber Physical Systems (CPS), requiring integration between computational systems for recognizing human functional activity and intent and controlling prosthetic devices to interact with the physical world. Research on this problem has been limited by the difficulties in noninvasively acquiring robust biosignals that allow intuitive and reliable control of multiple degrees of freedom (DoF). The objective of this research is to investigate a new sensing paradigm based on ultrasonic imaging of dynamic muscle activity. The synergistic research plan will integrate novel imaging technologies, new computational methods for activity recognition and learning, and high-performance embedded computing to enable robust and intuitive control of dexterous prosthetic hands with multiple DoF. The interdisciplinary research team involves collaboration between biomedical engineers, electrical engineers and computer scientists. The specific aims are to: (1) research and develop spatio-temporal image analysis and pattern recognition algorithms to learn and predict different dexterous tasks based on sonographic patterns of muscle activity (2) develop a wearable image-based biosignal sensing system by integrating multiple ultrasound imaging sensors with a low-power heterogeneous multicore embedded processor and (3) perform experiments to evaluate the real-time control of a prosthetic hand. The proposed research methods are broadly applicable to assistive technologies where physical systems, computational frameworks and low-power embedded computing serve to augment human activities or to replace lost functionality. The research will advance CPS science and engineering through integration of portable sensors for image-based sensing of complex adaptive physical phenomena such as dynamic neuromuscular activity, and real-time sophisticated image understanding algorithms to interpret such phenomena running on low-power high performance embedded systems. The technological advances would enable practical wearable image-based biosensing, with applications in healthcare, and the computational methods would be broadly applicable to problems involving activity recognition from spatiotemporal image data, such as surveillance. This research will have societal impacts as well as train students in interdisciplinary methods relevant to CPS. About 1.6 million Americans live with amputations that significantly affect activities of daily living. The proposed project has the long-term potential to significantly improve functionality of upper extremity prostheses, improve quality of life of amputees, and increase the acceptance of prosthetic limbs. This research could also facilitate intelligent assistive devices for more targeted neurorehabilitation of stroke victims. This project will provide immersive interdisciplinary CPS-relevant training for graduate and undergraduate students to integrate computational methods with imaging, processor architectures, 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 institution. The research team will involve students with disabilities in research activities by leveraging an ongoing NSF-funded project. Bioengineering training activities will be part of a newly developed undergraduate curriculum and a graduate curriculum under development. The synergistic research plan has been designed to advance CPS science and engineering through the development of new computational methods for dynamic activity recognition and learning from image sequences, development of novel wearable imaging technologies including high-performance embedded computing, and real-time control of a physical system. The specific aims are to: (1) Research and develop spatio-temporal image analysis and pattern recognition algorithms to learn and predict different dexterous tasks based on sonographic patterns of muscle activity. The first aim has three subtasks designed to collect, analyze and understand image sequences associated with functional tasks. (2) Develop a wearable image-based biosignal sensing system by integrating multiple ultrasound imaging sensors with a low-power heterogeneous multicore embedded processor. The second aim has two subtasks designed to integrate wearable imaging sensors with a real-time computational platform. (3) Perform experiments to evaluate the real-time control of a prosthetic hand. The third aim will integrate the wearable image acquisition system developed in Aim 2, and the image understanding algorithms developed in Aim 1, for real-time evaluation of the control of a prosthetic hand interacting with a virtual reality environment. Successful completion of these aims will result in a real-time system that acquires image data from complex neuromuscular activity, decodes activity intent from spatiotemporal image data using computational algorithms, and controls a prosthetic limb in a virtual reality environment in real time. Once developed and validated, this system can be the starting point for developing a new class of sophisticated control algorithms for intuitive control of advanced prosthetic limbs, new assistive technologies for neurorehabilitation, and wearable real-time imaging systems for smart health applications.