Targeted transcranial direct current stimulation combined with bimanual training for children with cerebral palsy

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PROJECT SUMMARY The overall objective for the parent grant is to determine how to optimally target transcranial direct current stimulation (tDCS) to enhance the efficacy of upper extremity (UE) training in children with unilateral cerebral palsy (UCP). A key tool to determine whether this intervention leads to improves UE function is the use of clinical assessments. However, even the best assessments do not have the capacity to identify kinematic features of a child’s hand movement. Most assessments use time to complete a task as a corollary of hand function or subjective ratings of movement quality and function. By not capturing movement patterns using kinematics, there is a vital loss of information that could help optimize interventions. Existing methods of UE kinematics acquisition are not easily accessible because of their size, cost, and operational complexities. In this supplement proposal, we aim to use Deep Learning (DL) pose estimation models along with 3D depth sensing cameras to develop a cost effective, easy to use, and compact Deep Learning based markerless kinematic data acquisition (DL-KDA) system that can be applied to children with UCP. In order to achieve this overall goal, we must establish the accuracy and validity of the kinematic data obtained from the system. We will begin by developing a modular software framework for building and testing DL-KDA systems against a very precise marker-based motion capture gold standard (VICON). We will study the effects of 3D camera and DL parameters/architecture on the accuracy of the resulting kinematic data from healthy adult participants during BBT. Kinematic data from both, the DL-KDA and gold standard systems along with the respective DL-KDA parameter data will be transformed into an AI/ML ready HDF5 file. This will be published in public DL and NIH data repositories to encourage further development of ML applications using kinematic data. Once a validated and optimal DL-KDA configuration is identified, we will investigate this system’s suitability for applications to children with UCP. Since existing training datasets used for most DL pose estimation models are not inclusive of children and/or adults with movement disorders, potential ethical and scientific biases may arise if applied to an underrepresented group. To address this, we will use our large database of UCP assessment videos over the last 9 years to generate ~12,000 pose images of children with UCP. The Images will be transformed into ML/AI ready HDF5 datasets and published in public DL and NIH repositories. These datasets will be available for other researchers to consider when using or building DL pose estimation models for applications in UCP clinical research. We will use this dataset to perform transfer learning and retrain the optimal DL model previously identified. The performance of the retrained DL model will be statistically compared to the original DL model to verify if bias was indeed present. Finally, we will apply the optimal DL- KDA using the retrained model to ~20 children with UCP during the BBT. Validated kinematics for the UCP population, as well, will be uploaded to public DL and NIH repositories for use in future UCP research.