Patients with progressive neurological disorders such as Parkinson's disease, Huntington's disease, and Amyotrophic Lateral Sclerosis (ALS) suffer both chronic and episodic difficulties with locomotion. Real-time assessment and visualization of sensor data can be valuable to physicians monitoring the progression of these conditions. We present a system that utilizes the attention based bi-directional recurrent neural network (RNN) presented in [2] to evaluate foot pressure sensor data streamed directly from a pair of sensors attached to a patient. The demonstration also supports indirect streaming from recorded sessions, such as those stored in a FHIR [1] enabled electronic medical records repository, for post-hoc evaluation and comparison of a patient's gait over time. The system evaluates and visualizes the streamed gait in a real time web interface to provide a personalized normality rating that highlights the strengths and weaknesses of a patient's gait.

Engineering drawings, scientific data, and governmental document repositories rely on degraded two-dimensional images to represent physical three-dimensional objects. The collection of two-dimensional multiview images are generated from a set of known camera positions that are aimed directly at the target object. These images provide a convenient method for representing the original physical object but significantly degrades the interpretability of the object. The multiview images from the document repositories may be integrated to reconstruct an approximation of the original physical object as a point cloud. We show that retrieval methods for documents are improved by directly sampling point clouds from the multiview image set to reconstruct the original physical object. We compare the retrieval results from direct image retrieval, multiview convolutional neural networks (MVCNN), and point clouds reconstructed from sampled images. To evaluate these models, we trained them on line drawings generated from models in the ShapeNet Core data set. We show retrieval of the reconstructed object is more accurate than single image retrieval or the multiview image set retrieval.

A common representation of a three dimensional object is a multi-view collection of two dimensional images showing the object from multiple angles. This technique is often used with document repositories such as collections of engineering drawings and governmental repositories of design patents and 3D trademarks. It is rare for the original physical artifact to be available. When the original physical artifact is modeled as a set of images, the resulting multi-view collection of images may be indexed and retrieved using traditional image retrieval techniques. Consequently, massive repositories of multi-view collections exist. While these repositories are in use and easy to construct, the conversion of a physical object into multi-view images results in a degraded representation of both the original three dimensional artifact and the resulting document repository. We propose an alternative approach where the archived multi-view representation of the physical artifact is used to reconstruct the 3D model, and the reconstructed model is used for retrieval against a database of 3D models. We demonstrate that document retrieval using the reconstructed 3D model achieves higher accuracy than document retrieval using a document image against a collection of degraded multi-view images. The Princeton Shape Benchmark 3D model database and the ShapeNet Core 3D model database are used as ground truth for the 3D image collection. Traditional indexing and retrieval is simulated using the multi-view images generated from the 3D models. A more accurate 3D model search is then considered using a reconstruction of the original 3D models from the multi-view archive, and this model is searched against the 3D model database.