Abstract: A multimodal, contactless vital sign monitoring system is configured to perform the following operations. Images are received from a video capture device. An image of a subject is identified within the images. The image of the subject is segmented into a plurality of segments. A first analysis is performed on the plurality of segments to identify a color feature. A second analysis is performed of the plurality of segments to identify a motion feature. Using a combination of the color feature and the motion feature a plurality of vital signs for the subject are determined. The first analyzing and the second analyzing are performed in parallel. The plurality of vital signs include one or more of heart rate, respiration rate, oxygen saturation, heart rate variability, and atrial fibrillation.

Abstract: Limb motion tracking using a single sensor can include capturing acceleration data. The acceleration data can be captured in real time by an IMU sensor of a wearable device worn on a limb of a user. Orientation data can be captured in real time by the IMU sensor concurrently with the acceleration data. Estimated positions of joints of the limb can be determined based on the acceleration data and the orientation data, the joints positions estimated using a machine learning model and relative to a coordinate system. Motion of the limb can be tracked based on the estimated positions determined at different times as the user moves the limb.

Abstract: Systems and methods for virtual training are provided. The systems and methods resolves user gestures in view of network and user latencies. Subsequences in the user responsive gesture data are aligned with subsequences in the avatar video data. Correction data can be generated in real time to send through the network for use by the display device.