Abstract: A security gateway for touch-free infectious disease screening includes a contactless sensor, a memory and a processor. The contactless sensor is configured to obtain health readings of a subject. The memory stores instructions. The processor executes the instructions. When executed by the processor, the instructions cause the security gateway to measure vital signs based on the health readings of the subject, and determine whether to allow access to a premise based on the vital signs.

Abstract: A method for managing movement includes determining a current location of a subject in a monitoring area, comparing the current location to a risk area location in the monitoring area, determining a likelihood of injury based on a result of the comparison, and generating control information based on the likelihood of injury to the subject. The control information may control activation of a haptic effect in a device worn or carried by the subject. The haptic effect may correspond to at least one stimulus that notifies the subject of the potential risk area.

Abstract: There is provided an apparatus (100) for monitoring swallowing in a subject. The apparatus (100) comprises a processor (102) configured to acquire, from a motion sensor (104), motion signals obtained from a feeding tube, when placed in the esophagus of the subject. The motion signals are indicative of swallowing motions transmitted along the feeding tube. The processor (102) is also configured to process the acquired motion signals to classify the acquired motion signals as indicative of the subject swallowing healthily or unhealthily.

Abstract: A system and method are provided for performing physical activity monitoring and fall detection of a subject using a wearable sensor including at least one audio sensor and at least one of an accelerometer, a gyroscope and a magnetometer. The method includes monitoring activities of the subject using the at least one of the accelerometer, the gyroscope and the magnetometer, and identifying a characteristic motion pattern based on the monitored activities; detecting an apparent fall experienced by the subject based on the identified at least one characteristic motion pattern; monitoring sounds provided by the at least one audio sensor following the detected apparent fall; determining whether the detected apparent fall is an actual fall experienced by the subject based on the monitored sounds; and communicating an indication of the actual fall to a monitoring system, enabling commencement of responsive action.

Abstract: A CDS system including a controller extracting EHRs of an IHG and selecting SMs to form a target patient database, capturing a time interval between each subsequent record for each of the measurements defined by the SMs, computing hospital level features including statistics on the distribution of the captured time intervals for each of the measurements, clustering the plurality of hospitals of the IHG in accordance with the computed hospital level features, determining whether an other hospital is within a threshold distance to a closest one of the plurality of clusters; placing the other hospital in the closest cluster when it is determined that the other hospital is within the threshold distance, and placing the other hospital in a new cluster when it is determined that the other hospital is not within the threshold distance.

Abstract: Described herein are methods, devices and systems for predicting ventilator associated pneumonia (VAP). In an example, data of a patient including one or more features useful in predicting VAPs are accessed and the features are extracted. A model is activated based on a time parameter associated with mechanical ventilation or length of stay in ICU. The extracted features are evaluated automatically by the activated model to determine a probability that a VAP will occur. If it is determined, based on the evaluating, that a VAP has a probability of occurring that exceeds a threshold, an indication that the VAP is predicted to occur may be provided. Other examples are disclosed and claimed.

Abstract: A method of evaluating a subject using a wearable sensor includes collecting raw data at the sensor indicating movement and/or characteristics, determining a physical location of the sensor on the body of the subject, and determining whether the physical location matches a primary location on the subject corresponding to a location at which training data are collected for training pre-trained models. When the physical location matches the primary location, the physical activity and posture of the subject are determined using the raw data collected at the sensor, in accordance with a selected model. When the physical location does not match the primary location, the raw data is mapped from the physical location to the primary location using a machine-learning based algorithm to provide mapped data, and the physical activity and posture of the subject are determined using the mapped data, in accordance with the selected model.

Abstract: Described herein are methods, devices and systems for predicting ventilator associated events (VAEs). In an example, data of a patient including one or more features useful in predicting VAEs is accessed and the features are extracted. The extracted features are evaluated automatically to determine a probability that a VAE will occur. If it is determined, based on the evaluating, that a VAE has a probability of occurring that exceeds a threshold, an indication that the VAE is predicted to occur is provided. Other examples are disclosed and claimed.

Abstract: A controller includes a memory that stores instructions and a processor that executes the instructions. The instructions cause the controller to execute a process that includes receiving sensor data from a first sensor and a second sensor. The sensor data includes a time-series observation representing a first activity and a second activity. The controller generates models for each activity involving progressions through states indicated by the sensor data from each sensor. The controller receives from each sensor additional sensor data including a time-series observation representing the first activity and the second activity. The controller determines likelihoods that the models generated a portion of the additional sensor data and calculates a pair-wise distance between each sensor-specific determined likelihood to obtain calculated distances.

Abstract: A controller includes a memory that stores instructions and a processor that executes the instructions. The instructions cause the controller to execute a process that includes receiving sensor data from a first sensor and a second sensor. The sensor data includes a time-series observation representing a first activity and a second activity. The controller generates models for each activity involving progressions through states indicated by the sensor data from each sensor. The controller receives from each sensor additional sensor data including a time-series observation representing the first activity and the second activity. The controller determines likelihoods that the models generated a portion of the additional sensor data and calculates a pair-wise distance between each sensor-specific determined likelihood to obtain calculated distances.

Abstract: A controller includes a memory that stores instructions and a processor that executes the instructions. The instructions cause the controller to execute a process that includes receiving sensor data from a first sensor and a second sensor. The sensor data includes a time-series observation representing a first activity and a second activity. The controller generates models for each activity involving progressions through states indicated by the sensor data from each sensor. The controller receives from each sensor additional sensor data including a time-series observation representing the first activity and the second activity. The controller determines likelihoods that the models generated a portion of the additional sensor data and calculates a pair-wise distance between each sensor-specific determined likelihood to obtain calculated distances.

Abstract: There is provided an apparatus (100) for monitoring swallowing in a subject. The apparatus (100) comprises a processor (102) configured to acquire, from a motion sensor (104), motion signals obtained from a feeding tube, when placed in the esophagus of the subject. The motion signals are indicative of swallowing motions transmitted along the feeding tube. The processor (102) is also configured to process the acquired motion signals to classify the acquired motion signals as indicative of the subject swallowing healthily or unhealthily.

Abstract: A method for managing movement includes determining a current location of a subject in a monitoring area, comparing the current location to a risk area location in the monitoring area, determining a likelihood of injury based on a result of the comparison, and generating control information based on the likelihood of injury to the subject. The control information may control activation of a haptic effect in a device worn or carried by the subject. The haptic effect may correspond to at least one stimulus that notifies the subject of the potential risk area.

Abstract: Proposed are concepts for monitoring abnormal respiratory events of a subject which leverage data from a sensor system. Proposed concepts may employ data acquisition from the sensor system at two different frequencies. For instance, a first, lower frequency may be used to acquire data from which an abnormal respiratory event (such as a cough or wheeze) of the subject may be detected. In response to detecting an abnormal respiratory event, a second, higher frequency may be used to acquire data to facilitate more detailed analysis and/or monitoring of the subject's respiration. In this way, low frequency data acquisition, which may be less accurate but consume less power, may be used to firstly detect an abnormal respiratory event. Once an abnormal respiratory event detected, data acquisition may be switched to a higher frequency, so as to obtain more detailed (e.g. higher resolution) information about the respiration.

Abstract: Techniques herein relate to personalized assistance for subjects with impairment(s). In various embodiments, a subject's state may be determined (402) from various signal(s). Based on the subject's state, a first computing device may be selected (404) from one or more computing devices available to the subject. Based on the subject's state and a policy associated with the subject, task(s) may be determined (406) that are performable by the subject with the aid of the first computing device. Task-selection input from the subject may be received (408) via the first computing device to initiate a triggered task. Task-engagement input may be received (412) via the first computing device from the subject that indicate completion of step(s) of the triggered task. The policy may be updated (414), e.g., using reinforcement learning, based on attribute(s) of the task-engagement inputs.

Abstract: A system and method are provided for performing physical activity monitoring and fall detection of a subject using a wearable sensor including at least one audio sensor and at least one of an accelerometer, a gyroscope and a magnetometer. The method includes monitoring activities of the subject using the at least one of the accelerometer, the gyroscope and the magnetometer, and identifying a characteristic motion pattern based on the monitored activities; detecting an apparent fall experienced by the subject based on the identified at least one characteristic motion pattern; monitoring sounds provided by the at least one audio sensor following the detected apparent fall; determining whether the detected apparent fall is an actual fall experienced by the subject based on the monitored sounds; and communicating an indication of the actual fall to a monitoring system, enabling commencement of responsive action.

Abstract: A system is proposed for detecting incontinence of a subject. The system comprises an electronic textile sensing device arranged in an ambient environment of the subject comprising an incontinence detection sensor and a pressure detection sensor. The sensing device is configured to detect at least one incontinence-related parameter and at least one subject-specific parameter. The system comprises a processor connectively coupled to the electronic textile sensing device. The processor may process the at least one incontinence-related parameter and the at least one subject-specific parameter.

Abstract: A controller includes a memory that stores instructions and a processor that executes the instructions. The instructions cause the controller to execute a process that includes receiving sensor data from a first sensor and a second sensor. The sensor data includes a time-series observation representing a first activity and a second activity. The controller generates models for each activity involving progressions through states indicated by the sensor data from each sensor. The controller receives from each sensor additional sensor data including a time-series observation representing the first activity and the second activity. The controller determines likelihoods that the models generated a portion of the additional sensor data and calculates a pair-wise distance between each sensor-specific determined likelihood to obtain calculated distances.

Abstract: A controller includes a memory that stores instructions and a processor that executes the instructions. The instructions cause the controller to execute a process that includes receiving sensor data from a first sensor and a second sensor. The sensor data includes a time-series observation representing a first activity and a second activity. The controller generates models for each activity involving progressions through states indicated by the sensor data from each sensor. The controller receives from each sensor additional sensor data including a time-series observation representing the first activity and the second activity. The controller determines likelihoods that the models generated a portion of the additional sensor data and calculates a pair-wise distance between each sensor-specific determined likelihood to obtain calculated distances.