VidSole: A Multimodal Dataset for Joint Kinetics Quantification and Disease Detection with Deep Learning

Understanding internal joint loading is critical for diagnosing gait-related diseases such as knee osteoarthritis; however, current methods of measuring joint risk factors are time-consuming, expensive, and restricted to lab settings. In this paper, we enable the large-scale, cost-effective biomechanical analysis of joint loading via three key contributions: the development and deployment of novel instrumented insoles, the creation of a large multimodal biomechanics dataset (VidSole), and a baseline deep learning pipeline to predict internal joint loading factors. Our novel instrumented insole measures the tri-axial forces and moments across five high-pressure points under the foot. VidSole consists of the forces and moments measured by these insoles along with corresponding RGB video from two viewpoints, 3D body motion capture, and force plate data for over 2,600 trials of 52 diverse participants performing four fundamental activities of daily living (sit-to-stand, stand-to-sit, walking, and running). We feed the insole data and kinematic parameters extractable from video (i.e., pose, knee angle) into a deep learning pipeline consisting of an ensemble Gated Recurrent Unit (GRU) activity classifier followed by activity-specific Long Short Term Memory (LSTM) regression networks to estimate knee adduction moment (KAM), a biomechanical risk factor for knee osteoarthritis. The successful classification of activities at an accuracy of 99.02 percent and KAM estimation with mean absolute error (MAE) less than 0.5 percent*body weight*height, the current threshold for accurately detecting knee osteoarthritis with KAM, illustrates the usefulness of our dataset for future research and clinical settings.

Indoor Wireless Signal Modeling with Smooth Surface Diffraction Effects

We present a novel algorithm that enhances the accuracy of electromagnetic field simulations in indoor environments by incorporating the Uniform Geometrical Theory of Diffraction (UTD) for surface diffraction. This additional diffraction phenomenology is important for the design of modern wireless systems and allows us to capture the effects of more complex scene geometries. Central to our methodology is the Dynamic Coherence-Based EM Ray Tracing Simulator (DCEM), and we augment that formulation with smooth surface UTD and present techniques to efficiently compute the ray paths. We validate our additions by comparing them to analytical solutions of a sphere, method of moments solutions from FEKO, and ray-traced indoor scenes from WinProp. Our algorithm improves shadow region predicted powers by about 5dB compared to our previous work, and captures nuanced field effects beyond shadow boundaries. We highlight the performance on different indoor scenes and observe 60% faster computation time over WinProp.