Abstract: Peritonitis sensors, including peritonitis sensors for automated peritoneal dialysis (APD) systems, and associated systems, devices, and methods are disclosed herein. In one embodiment, an APD system includes a disposable set, a portion of which is at least partially aligned with a peritonitis sensor. The peritonitis sensor can be configured to capture one or more peritonitis measurements from the solution in the disposable set. The APD system can determine whether one or more peritonitis measurements indicate the presence of peritonitis in a patient from which the solution is drained. In some embodiments, if the APD system determines that one or more peritonitis measurements indicate peritonitis, the APD system can alert a user of the system to the presence of peritonitis in the patient.

Abstract: Transfer sets with filters, including transfer sets with filters for peritoneal dialysis (PD) systems, and associated systems, devices, and methods are disclosed herein. In one embodiment, a transfer set includes a first connector configured to be coupled to a disposable set of a PD system, a second connector configured to be coupled to a catheter of the PD system, and a fluid channel extending between the first connector and the second connector. The transfer set further includes a filter positioned within the fluid channel and configured to filter contaminants from solution flowing within the fluid channel between the first connector and the second connector. In some embodiments, the transfer set further includes a one-way valve positioned between the filter and the second connector and configured to prevent fluid from flowing through the one-way valve in a direction toward the filter.

Abstract: Identification systems, including anti-counterfeit and anti-reuse identification systems for automated peritoneal dialysis (APD) systems, and associated systems, devices, and methods are disclosed herein. In one embodiment, an APD system includes an identification system having at least one identification sensor configured to read device identifiers associated with disposable components of the APD system. The disposable components can include cassettes or source bags containing dialysate solution. In some embodiments, the identification system can (a) compare a device identifier of a disposable component to a whitelist of valid device identifiers, (b) determine whether the device identifier is valid, unused, and/or non-expired, and/or (c) based on the determination, determine whether to use the disposable component to execute an exchange treatment.

Abstract: Damping devices, including damping devices for automated peritoneal dialysis (APD) systems, and associated systems, devices, and methods are disclosed herein. In one embodiment, a damping device includes a body portion, a first membrane, and a second membrane. The body portion can include a first side, a second side opposite the first side, an inlet, and a cavity fluidly coupled to the inlet. The cavity can be defined at least in part by a lumen in the body portion extending from the first side to the second side. The first membrane can be affixed to the first side of the body portion such that the first membrane hermetically seals the cavity at the first side. The second membrane can be affixed to the second side of the body portion such that the second membrane hermetically seals the cavity at the second side.

Abstract: Biometric systems capture and combine biometric information from more than one modality, employing digital processing algorithms to process and evaluate captured images having data for a biometric characteristic. Such digital algorithms may include a pupil segmentation algorithm for determining a pupil image in the captured image, an iris segmentation algorithm for determining an iris image in the captured image, an eyelid/eyelash segmentation algorithm for determining an eyelid/eyelash image in the captured image, and an algorithm for measuring the focus on the iris. Some embodiments employ an auto-capture process which employs such algorithms, in part; to evaluate captured images and obtain the best possible images for biometric identification.

Abstract: A system for multimodal biometric identification has a first imaging system that detects one or more subjects in a first field of view, including a targeted subject having a first biometric characteristic and a second biometric characteristic; a second imaging system that captures a first image of the first biometric characteristic according to first photons, where the first biometric characteristic is positioned in a second field of view smaller than the first field of view, and the first image includes first data for biometric identification; a third imaging system that captures a second image of the second biometric characteristic according to second photons, where the second biometric characteristic is positioned in a third field of view which is smaller than the first and second fields of view, and the second image includes second data for biometric identification. At least one active illumination source emits the second photons.

Abstract: A system for multimodal biometric identification has a first imaging system that detects one or more subjects in a first field of view, including a targeted subject having a first biometric characteristic and a second biometric characteristic; a second imaging system that captures a first image of the first biometric characteristic according to first photons, where the first biometric characteristic is positioned in a second field of view smaller than the first field of view, and the first image includes first data for biometric identification; a third imaging system that captures a second image of the second biometric characteristic according to second photons, where the second biometric characteristic is positioned in a third field of view which is smaller than the first and second fields of view, and the second image includes second data for biometric identification. At least one active illumination source emits the second photons.

Abstract: Biometric systems capture and combine biometric information from more than one modality, employing digital processing algorithms to process and evaluate captured images having data for a biometric characteristic. Such digital algorithms may include a pupil segmentation algorithm for determining a pupil image in the captured image, an iris segmentation algorithm for determining an iris image in the captured image, an eyelid/eyelash segmentation algorithm for determining an eyelid/eyelash image in the captured image, and an algorithm for measuring the focus on the iris. Some embodiments employ an auto-capture process which employs such algorithms, in part; to evaluate captured images and obtain the best possible images for biometric identification.

Abstract: A system for multimodal biometric identification has a first imaging system that detects one or more subjects in a first field of view, including a targeted subject having a first biometric characteristic and a second biometric characteristic; a second imaging system that captures a first image of the first biometric characteristic according to first photons, where the first biometric characteristic is positioned in a second field of view smaller than the first field of view, and the first image includes first data for biometric identification; a third imaging system that captures a second image of the second biometric characteristic according to second photons, where the second biometric characteristic is positioned in a third field of view which is smaller than the first and second fields of view, and the second image includes second data for biometric identification. At least one active illumination source emits the second photons.

Abstract: Biometric systems capture and combine biometric information from more than one modality, employing digital processing algorithms to process and evaluate captured images having data for a biometric characteristic. Such digital algorithms may include a pupil segmentation algorithm for determining a pupil image in the captured image, an iris segmentation algorithm for determining an iris image in the captured image, an eyelid/eyelash segmentation algorithm for determining an eyelid/eyelash image in the captured image, and an algorithm for measuring the focus on the iris. Some embodiments employ an auto-capture process which employs such algorithms, in part, to evaluate captured images and obtain the best possible images for biometric identification.

Abstract: A system for multimodal biometric identification has a first imaging system that detects one or more subjects in a first field of view, including a targeted subject having a first biometric characteristic and a second biometric characteristic; a second imaging system that captures a first image of the first biometric characteristic according to first photons, where the first biometric characteristic is positioned in a second field of view smaller than the first field of view, and the first image includes first data for biometric identification; a third imaging system that captures a second image of the second biometric characteristic according to second photons, where the second biometric characteristic is positioned in a third field of view which is smaller than the first and second fields of view, and the second image includes second data for biometric identification. At least one active illumination source emits the second photons.

Abstract: A system for multimodal biometric identification has a first imaging system that detects one or more subjects in a first field of view, including a targeted subject having a first biometric characteristic and a second biometric characteristic; a second imaging system that captures a first image of the first biometric characteristic according to first photons, where the first biometric characteristic is positioned in a second field of view smaller than the first field of view, and the first image includes first data for biometric identification; a third imaging system that captures a second image of the second biometric characteristic according to second photons, where the second biometric characteristic is positioned in a third field of view which is smaller than the first and second fields of view, and the second image includes second data for biometric identification. At least one active illumination source emits the second photons.

Abstract: Biometric systems capture and combine biometric information from more than one modality, employing digital processing algorithms to process and evaluate captured images having data for a biometric characteristic. Such digital algorithms may include a pupil segmentation algorithm for determining a pupil image in the captured image, an iris segmentation algorithm for determining an iris image in the captured image, an eyelid/eyelash segmentation algorithm for determining an eyelid/eyelash image in the captured image, and an algorithm for measuring the focus on the iris. Some embodiments employ an auto-capture process which employs such algorithms, in part, to evaluate captured images and obtain the best possible images for biometric identification.

Abstract: A system for multimodal biometric identification has a first imaging system that detects one or more subjects in a first field of view, including a targeted subject having a first biometric characteristic and a second biometric characteristic; a second imaging system that captures a first image of the first biometric characteristic according to first photons, where the first biometric characteristic is positioned in a second field of view smaller than the first field of view, and the first image includes first data for biometric identification; a third imaging system that captures a second image of the second biometric characteristic according to second photons, where the second biometric characteristic is positioned in a third field of view which is smaller than the first and second fields of view, and the second image includes second data for biometric identification. At least one active illumination source emits the second photons.

Abstract: A method summarizes a video including a sequence of frames. The video is partitioned into segments of frames, and faces are detected in the frames of the segments. Features of the frames including the faces are extracted. For each segment including the faces, a representative frame based on the features is selected. For each possible pair of representative frames, distances are determined based on the faces. The distances are arranged in a matrix. Spectral clustering is applied to the matrix to determine an optimal number of clusters. Then, the video can be summarized according to the optimal number of clusters.