Systems for control and evaluation of advanced assistive technologies

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Rehabilitation technology aims to restore independence and quality of life to individuals with disabilities. After cervical spinal cord injury (SCI), arm and hand function are the top priority for recovery. To address this need, progress is being made in technologies that can restore a variety of complex movements, including implanted functional electrical stimulation systems and wearable exoskeletons. However, considerable improvements are needed in human-machine interfaces that will enable users to effectively control the complex movement patterns that these new assistive technologies can produce. Interfaces with the nervous system are the most appealing avenue, as they provide a mechanism for intuitive and naturalistic control. The goal of the proposed research program is to advance human-machine interface technology to enable effective communication with complex assistive technologies. We will address the following objectives: Objective 1 - Interface: Develop methods to selectively record information from multiple sources within peripheral nerves, in order to extract command signals from the nervous system. Building on recent our success in acute animal experiments, we will use chronic animal studies and computational modeling to develop adaptive signal processing methods that can compensate for changes over time. This work will result in stable and accurate neural interfaces that will support the next generation of assistive technologies. Objective 2 - Control: Optimize the information transfer rates (ITRs) achievable through partially impaired neural pathways. Using electromyographic recordings from upper limb muscles of individuals with SCI as an experimental model, we will develop methods to predict personalized control parameters (number of classes, rate of commands) that optimize ITR for each person, based on the residual strength of the muscles used. Objective 3 - Evaluate: Develop wearable systems to characterize the impact of new technology on upper limb function in the daily lives of individuals with SCI. Our group has pioneered the use of wearable cameras (egocentric video) to assess hand function at home. Over the granting period, we will develop computer vision and deep learning methods for postural tracking of the hands in sequences of egocentric video, thus providing an unprecedented level of detail about hand function. This work will catalyze a new generation of assistive technology in rehabilitation. Specific contributions to the natural sciences and engineering will include novel chronic neural interfaces, optimal control schemes for these interfaces, and wearable systems that directly evaluate the true impact of assistive technology. We will train highly qualified personnel in neural engineering, signal processing, computer vision, deep learning, modeling, and neuroscience. This research will lead to improved quality of life for individuals with disabilities, and reduce the economic impact on the health care system.