Abstract: A respiration rate prediction system and method can include: emitting light into tissue with a light emitter; detecting the light from the tissue with a photodetector as a hemodynamic signal; determining a respiration rate estimate and a signal condition feature from the hemodynamic signal; determining a signal quality indicator from the signal condition feature; deciding whether to determine a respiration rate prediction with a machine learning algorithm based on the signal quality indicator; determining the respiration rate prediction and a confidence score for the respiration rate prediction, the confidence score based on the respiration rate estimate and the signal condition feature being inputs into the machine learning algorithm; and outputting the respiration rate prediction from the machine learning algorithm based on the confidence score.

Abstract: One embodiment is a method comprising collecting photoplethysmography (“PPG”) data associated with a user using a biometric monitoring device; extracting a signal quality metric (“SQM”) from the collected PPG data; classifying the PPG data signal based on the extracted SQM into one of a plurality of signal quality levels; and determining based on the classification whether to increase, decrease, or maintain a power level of an LED of the biometric monitoring device.

Abstract: Accuracy of vital signs monitoring systems depend on the quality of the measurements by the sensors. Crude techniques are applied to discard measurements which have a low signal-to-noise ratio or are saturated. A more accurate and flexible technique enables more measurements to be kept, more meaningful signal quality information to be extracted, more accurate vital signs extraction and more systems to readily embed signal quality assessment in the signal processing pipeline. Improvements include preprocessing of the signal that is independent of variations of underlying hardware, use of features with low computational complexity and high predictive power, cross-channel feature extraction, application of a trained machine learning model, and flexible translation of signal quality classification information into a continuous metric for signal quality.

Abstract: Embodiments of the present disclosure describe mechanisms for a radar-based indoor localization and tracking system. One example can include monitoring unit that includes a radar source, a camera unit, and one or more processors coupled to the radar element and the camera unit. The monitoring unit is configured to generate point cloud data associated with an object; execute Point Cloud Library (PCL) preprocessing based, at least, on the point cloud data; execute Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering; execute multi-object tracking on the object; and execute an image PCL overlay based on the point cloud data to generate real-time data associated with the object.

Abstract: System and apparatus for pulse oximetry. Systems, methods, and devices are presented for placement of the hardware on a body part where detracting signals are strong. In one example, detracting signals include signals from respiration. In various examples, the body part the oximeter is placed on is a wrist, chest, or arm. A computationally efficient pipeline is presented that allows for reliable and robust SpO2 estimation.

Abstract: Embodiments of the present disclosure describe mechanisms for a radar-based indoor localization and tracking system. One example can include monitoring unit that includes a radar source, a camera unit, and one or more processors coupled to the radar element and the camera unit. The monitoring unit is configured to generate point cloud data associated with an object; execute Point Cloud Library (PCL) preprocessing based, at least, on the point cloud data; execute Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering; execute multi-object tracking on the object; and execute an image PCL overlay based on the point cloud data to generate real-time data associated with the object.

Abstract: One embodiment is a method comprising collecting photoplethysmography (“PPG”) data associated with a user using a biometric monitoring device; extracting a signal quality metric (“SQM”) from the collected PPG data; classifying the PPG data signal based on the extracted SQM into one of a plurality of signal quality levels; and determining based on the classification whether to increase, decrease, or maintain a power level of an LED of the biometric monitoring device.

Abstract: Systems and methods for ultrasound imaging capable of achieving spatial resolutions that can resolve objects smaller than 300 ?m are described. Ultrasound is transmitted to and steered over a volume-of-interest that contains a microbubble contrast agent to individually excite microbubbles. Signal data is acquired in response to the transmitted ultrasound, and a plurality of images are reconstructed by beamforming the acquired signal data. The spatial resolution of the beamformed images can be further increased using techniques that determine the position of the microbubble within each image to a greater level of accuracy than the point spread function (“PSF”) of the ultrasound system. The images can also be combined to produce a single high resolution image of the volume-of-interest using, for instance, a maximum pixel projection technique.

Abstract: Systems and methods for medical imaging, specifically ultrasound imaging capable of achieving spatial resolutions that can resolve point objects smaller than 100 ?m irrespective of them to be well-resolved, using the principles of compressive sensing and sparse recovery are described. Ultrasound system uses the transmit transducers sequentially to sonicate the medium and the data is acquired over the receive transducers. The acquired signals are then sampled by the low-dimensional acquisition system. The signals are recovered using an optimization method before a frequency domain beamforming technique is applied. The time reversal focused frequency matrix is formed to focus the energy of different frequency bands into a single frequency.

Abstract: Systems and methods for ultrasound imaging capable of achieving spatial resolutions that can resolve objects smaller than 300 ?m are described. Ultrasound is transmitted to and steered over a volume-of-interest that contains a microbubble contrast agent to individually excite microbubbles. Signal data is acquired in response to the transmitted ultrasound, and a plurality of images are reconstructed by beamforming the acquired signal data. The spatial resolution of the beamformed images can be further increased using techniques that determine the position of the microbubble within each image to a greater level of accuracy than the point spread function (“PSF”) of the ultrasound system. The images can also be combined to produce a single high resolution image of the volume-of-interest using, for instance, a maximum pixel projection technique.