Abstract: Certain aspects of the present disclosure relate to a method for quantizing signals and reconstructing signals, and/or encoding or decoding data for storage or transmission. Points of a signal may be determined as local extrema or points where an absolute rise of the signal is greater than a threshold. The tread and value of the points may be quantized, and certain of the quantizations may be discarded before the quantizations are transmitted. After being received, the signal may be reconstructed from the quantizations using an iterative process.

Abstract: A repeated integral images method filters image data in only two passes, e.g., the first pass filters horizontal rows of pixels and a second pass filters vertical columns of pixels, or in a single pass. The filter performs at least one infinite impulse response (IIR) filter and at least one finite impulse response (FIR) filter on the image data. A plurality of IIR filters and FIR filters maybe performed to approximate a Gaussian filter. By minimizing the number of passes, the data flow between the processing unit and the storage unit is greatly reduced compared to conventional repeated integral images method thereby improving computation time.

Abstract: Certain aspects of the present disclosure relate to a method for compressed sensing (CS). The CS is a signal processing concept wherein significantly fewer sensor measurements than that suggested by Shannon/Nyquist sampling theorem can be used to recover signals with arbitrarily fine resolution. In this disclosure, the CS framework is applied for sensor signal processing in order to support low power robust sensors and reliable communication in Body Area Networks (BANs) for healthcare and fitness applications.

Abstract: Example methods, apparatuses, or articles of manufacture are disclosed herein that may be utilized, in whole or in part, to facilitate or support one or more operations or techniques for machine learning of known or unknown motion states with sensor fusion.

Abstract: Certain aspects of the present disclosure relate to a technique for mitigating artifacts of biophysical signals in a body area network. Information from multiple sensors (including motion information of the body) can be employed in mitigating the artifacts. The biophysical signals in the body area network can be compressively sensed.

Abstract: Methods and apparatuses are provided that may be implemented in a mobile device to establish an orientation invariant reference frame based, at least in part, on measurement values from a three-dimensional accelerometer fixed to the mobile device; transform subsequent inertial sensor measurements to the reference frame; and classify a motion state of the mobile device relative to the reference frame based, at least in part, on the transformed inertial sensor measurements.

Abstract: Example methods, apparatuses, or articles of manufacture are disclosed herein that may be utilized, in whole or in part, to facilitate or support one or more operations or techniques for machine learning of known or unknown motion states with sensor fusion.

Abstract: A repeated integral images method filters image data in only two passes, e.g., the first pass filters horizontal rows of pixels and a second pass filters vertical columns of pixels, or in a single pass. The filter performs at least one infinite impulse response (IIR) filter and at least one finite impulse response (FIR) filter on the image data. A plurality of IIR filters and FIR filters maybe performed to approximate a Gaussian filter. By minimizing the number of passes, the data flow between the processing unit and the storage unit is greatly reduced compared to conventional repeated integral images method thereby improving computation time.

Abstract: Certain aspects of the present disclosure relate to techniques for measuring body impedance based on baseband signal detection in analog domain. Proposed methods and apparatus are able to measure an impedance of human body based on sub-Nyquist sampling of signals. The proposed techniques can be particularly beneficial for reducing overall sensor power when an actuation signal generates electrical signals corresponding to vital signs in humans.

Abstract: A database for object recognition is generated by performing at least one of intra-object pruning and inter-object pruning, as well as keypoint clustering and selection. Intra-object pruning removes similar and redundant keypoints within an object and different views of the same object, and may be used to generate and associate a significance value, such as a weight, with respect to remaining keypoint descriptors. Inter-object pruning retains the most informative set of descriptors across different objects, by characterizing the discriminability of the keypoint descriptors for all of the objects and removing keypoint descriptors with a discriminability that is less than a threshold. Additionally, a mobile platform may download a geographically relevant portion of the database and perform object recognition by extracting features from the query image and using determined confidence levels for each query feature during outlier removal.

Abstract: A database for object recognition is modified based on feedback information received from a mobile platform. The feedback information includes information with respect to an image of an object captured by the mobile platform. The feedback information, for example, may include the image, features extracted from the image, a confidence level for the features, posterior probabilities of the features belonging to an object in the database, GPS information, and heading orientation information. The feedback information may be used to improve the database pruning, add content to the database or update the database compression efficiency. The information feedback to the server by the mobile platform may be determined based on a search of a portion of the database performed by the mobile platform using features extracted from a captured query image.

Abstract: Certain aspects of the present disclosure relate to a method for quantizing signals and reconstructing signals, and/or encoding or decoding data for storage or transmission. Points of a signal may be determined as local extrema or points where an absolute rise of the signal is greater than a threshold. The tread and value of the points may be quantized, and certain of the quantizations may be discarded before the quantizations are transmitted. After being received, the signal may be reconstructed from the quantizations using an iterative process.

Abstract: Certain aspects of the present disclosure relate to a technique for mitigating artifacts of biophysical signals in a body area network. Information from multiple sensors (including motion information of the body) can be employed in mitigating the artifacts. The biophysical signals in the body area network can be compressively sensed.

Abstract: Certain aspects of the present disclosure relate to a method for estimating a blood pressure using both a pulse arrival time (PAT) and an instantaneous heart rate (HR). The PAT can be measured as the delay between QRS peaks in an electrocardiogram (ECG) signal and corresponding points in a photoplethysmogram (PPG) waveform. Parameters of the estimation model can be determined through an initial training. Then, the model parameters can be recalibrated in constant intervals using the recursive least square (RLS) approach combined with a smooth bias fixing. The proposed estimation algorithm is applied on a multi-parameter intelligent monitoring for intensive care (MIMIC) database, and the results are compared with estimation methods that use PAT only or HR only. The proposed estimation algorithm meets, on average, the Association for the Advancement of Medical Instrumentation (AAMI) requirements and outperforms other methods from the prior art.