Koninklijke Philips Corporation N.v.

Saman Parvaneh

Ali Akbar Ahmad Samadani

Saeed Babaeizadeh

Bryan Conroy

Asif Rahman

Jonathan Rubin

Mladen Milosevic

Various embodiments relate to a system for activities of daily living ('ADL') and instrumental activities of daily living (“IADL”) monitoring through electric current profile sensing, the system including an electric current profile sensing module configured to sense electricity usage and capture a current profile of a plurality of devices in a home using a plurality of current clamps, a device usage detection module configured to match the current profile for each of the plurality of devices to a referenced profile for each of the plurality of devices to determine a detected usage for each of the plurality of devices and an ADL and IADL module configured to translate the detected usage for each of the plurality of devices into ADLs and IADLs; wherein the ADL and IADL detection module determines whether a detected usage is a hazardous situation based on ADLs and IADLs.

System for emergency safety warning and risk mitigation

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.

System and method of detecting falls of a subject using a wearable sensor

A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor () to perform an atrial fibrillation (AF) detection method (). The method includes: generating a time-frequency representation of an electrocardiogram (ECG) signal acquired over a time interval; processing the time-frequency representation using a neural network (NN) () to output probabilities for rhythms of a set of rhythms including at least atrial fibrillation; assigning a rhythm for the ECG signal based on the probabilities for the rhythms of the set of rhythms output by the neural network; and controlling a display device () to display the rhythm assigned to the ECG signal.

Detecting atrial fibrillation using short single-lead ECG recordings

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. The calculated distances for each sensor are grouped, and a relevance of each sensor to each activity is determined by executing a regression model using the grouped calculated distances.

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Malden, MA, United States of America

Saman Parvaneh