Abstract: A computing system for decimating video data includes: a processor; a persistent storage system coupled to the processor; and memory storing instructions that, when executed by the processor, cause the processor to decimate a batch of frames of video data by: receiving the batch of frames of video data; mapping, by a feature extractor, the frames of the batch to corresponding feature vectors in a feature space, each of the feature vectors having a lower dimension than a corresponding one of the frames of the batch; selecting a set of dissimilar frames from the plurality of frames of video data based on dissimilarities between corresponding ones of the feature vectors; and storing the selected set of dissimilar frames in the persistent storage system, the size of the selected set of dissimilar frames being smaller than the number of frames in the batch of frames of video data.

Abstract: A computing system for decimating video data includes: a processor; a persistent storage system coupled to the processor; and memory storing instructions that, when executed by the processor, cause the processor to decimate a batch of frames of video data by: receiving the batch of frames of video data; mapping, by a feature extractor, the frames of the batch to corresponding feature vectors in a feature space, each of the feature vectors having a lower dimension than a corresponding one of the frames of the batch; selecting a set of dissimilar frames from the plurality of frames of video data based on dissimilarities between corresponding ones of the feature vectors; and storing the selected set of dissimilar frames in the persistent storage system, the size of the selected set of dissimilar frames being smaller than the number of frames in the batch of frames of video data.

Abstract: A computing system for decimating video data includes: a processor; a persistent storage system coupled to the processor; and memory storing instructions that, when executed by the processor, cause the processor to decimate a batch of frames of video data by: receiving the batch of frames of video data; mapping, by a feature extractor, the frames of the batch to corresponding feature vectors in a feature space, each of the feature vectors having a lower dimension than a corresponding one of the frames of the batch; selecting a set of dissimilar frames from the plurality of frames of video data based on dissimilarities between corresponding ones of the feature vectors; and storing the selected set of dissimilar frames in the persistent storage system, the size of the selected set of dissimilar frames being smaller than the number of frames in the batch of frames of video data.

Abstract: A calibration system includes a processing circuit configured to receive data corresponding to a first image where the data includes information corresponding to a 2D BEV of a scene, and to receive data corresponding to a second image where the data includes information corresponding to a 2D image of the scene. The processing circuit may determine a salient feature in the first image or the second image, and project, based on a first calibration setting, the salient feature from the first image to the second image or vice versa. The processing circuit may select a region corresponding to the projected salient feature in the first image or the second image, determine an identity of the salient feature, and determine a second calibration setting based on the identity of the salient feature and the selected region corresponding to the salient feature in the 2D BEV and the 2D image.

Abstract: A clip configured to be coupled to an electrocardiogram sensor unit. The clip includes an electrically conductive material having a first surface and a second surface opposite the first surface. The clip also includes electrical insulation coupled to the second surface of the electrically conductive material, and an electrode defined by an exposed portion of the first surface of the electrically conductive material. The clip may be flexible or substantially rigid.

Abstract: An apparatus includes a sensor module, a data processing module, a quality assessment module and an event prediction module. The sensor module provides biosignal data samples and motion data samples. The data processing module processes the biosignal data samples to remove baseline and processes the motion data samples to generate a motion significant measure. The quality assessment module generates a signal quality indicator based on the processed biosignal data sample segments and the corresponding motion significance measure using a first deep learning model. The event prediction module generates an event prediction result based on the processed biosignal data sample segments associated with a desired signal quality indicator using a second deep learning model.

Abstract: A calibration system includes a processing circuit configured to receive data corresponding to a first image where the data includes information corresponding to a 2D BEV of a scene, and to receive data corresponding to a second image where the data includes information corresponding to a 2D image of the scene. The processing circuit may determine a salient feature in the first image or the second image, and project, based on a first calibration setting, the salient feature from the first image to the second image or vice versa. The processing circuit may select a region corresponding to the projected salient feature in the first image or the second image, determine an identity of the salient feature, and determine a second calibration setting based on the identity of the salient feature and the selected region corresponding to the salient feature in the 2D BEV and the 2D image.

Abstract: A computing system for decimating video data includes: a processor; a persistent storage system coupled to the processor; and memory storing instructions that, when executed by the processor, cause the processor to decimate a batch of frames of video data by: receiving the batch of frames of video data; mapping, by a feature extractor, the frames of the batch to corresponding feature vectors in a feature space, each of the feature vectors having a lower dimension than a corresponding one of the frames of the batch; selecting a set of dissimilar frames from the plurality of frames of video data based on dissimilarities between corresponding ones of the feature vectors; and storing the selected set of dissimilar frames in the persistent storage system, the size of the selected set of dissimilar frames being smaller than the number of frames in the batch of frames of video data.

Abstract: A wearable device includes: a wristband; at least one PPG sensor mounted on the wristband and configured to output a measured PPG signal corresponding to vital signals of a wearer of the wristband; a neural network based controller configured to receive the measured PPG signal from the at least one PPG sensor and to generate commands in real time based on a quality of the PPG signal to adjust at least one selected from a position, a tension, and an orientation of the wristband to achieve a desired PPG signal quality.

Abstract: A clip configured to be coupled to an electrocardiogram sensor unit. The clip includes an electrically conductive material having a first surface and a second surface opposite the first surface. The clip also includes electrical insulation coupled to the second surface of the electrically conductive material, and an electrode defined by an exposed portion of the first surface of the electrically conductive material. The clip may be flexible or substantially rigid.

Abstract: A bioresponsive virtual reality system includes: a head-mounted display including a display device, the head-mounted display being configured to display a three-dimensional virtual reality environment on the display device; a plurality of bioresponsive sensors; and a processor connected to the head-mounted display and the bioresponsive sensors. The processor is configured to: receive signals indicative of a user's arousal and valance levels from the bioresponsive sensors; calibrate a neural network to correlate the user's arousal and valance values to a calculated affective state; calculate the user's affective state based on the signals; and vary the virtual reality environment displayed on the head-mounted display in response to the user's calculated affective state to induce a target affective state.

Abstract: A wearable device includes: a wristband; at least one PPG sensor mounted on the wristband and configured to output a measured PPG signal corresponding to vital signals of a wearer of the wristband; a neural network based controller configured to receive the measured PPG signal from the at least one PPG sensor and to generate commands in real time based on a quality of the PPG signal to adjust at least one selected from a position, a tension, and an orientation of the wristband to achieve a desired PPG signal quality.

Abstract: In a method for controlling a sensing device, the method includes: transmitting, by a processor, a signal to a wearable device to initiate vital signal sensing during a first time period; receiving, by the processor from the wearable device, vital signal data from the wearable device; adjusting, by the processor, a schedule for initiating the vital signal sensing based on the vital signal data; and transmitting, by the processor, a signal to the wearable device to initiate the vital signal sensing during a second time period according to the schedule for initiating the vital signal sensing.

Abstract: An apparatus includes a sensor module, a data processing module, a quality assessment module and an event prediction module. The sensor module provides biosignal data samples and motion data samples. The data processing module processes the biosignal data samples to remove baseline and processes the motion data samples to generate a motion significant measure. The quality assessment module generates a signal quality indicator based on the processed biosignal data sample segments and the corresponding motion significance measure using a first deep learning model. The event prediction module generates an event prediction result based on the processed biosignal data sample segments associated with a desired signal quality indicator using a second deep learning model.

Abstract: Provided is a method and system for helping a user reach a health goal. Various embodiments of the disclosure disclose receiving various data that relate to the user and comparing the received data to monitored user health data to provide recommendations on steps to take to achieve the health goal. The various embodiments also determine when the user has achieved the user's desired health goal.