Samsung Electronics Co., Ltd.

Tousif Ahmed

Jun Gao

Jilong Kuang

Md Mahbubur Rahman

Ebrahim Nematihosseinabadi

Viswam Nathan

Sean Bornheimer

Korosh Vatanparvar

Nathan Robert Folkman

Anh Minh Dinh

Mohsin Ahmed

Shibo Zhang

Bashima Islam

Pulmonary assessment based on speech can include identifying one or more audio features and speech patterns of a user's speech. A cognitive burden associated with the user's speech can be determined. A pulmonary condition of the user can be determined based on predetermined correlations between the one or more audio features and speech patterns of the user's speech, the cognitive burden, and a respiratory airway condition.

Speech-based pulmonary assessment

Detecting and identifying a predetermined health event can include detecting a potential occurrence of the predetermined health event for a user by processing in real-time motion signals corresponding to motion of the user. A likelihood that the potential occurrence is an actual occurrence of the predetermined health event can be determined based on template matching of the motion signals. In response to determining that the likelihood exceeds a predetermined threshold, audio signals coinciding in time with the motion of the user can be processed using one or more layers of a multilayered audio event classifier.

Multilayered determination of health events using resource-constrained platforms

Detecting an adverse health event and responding to an emergency event can include updating a current context profile of a user based on signals generated by one or more sensors of a device, the signals generated in response to one or more physically measurable phenomena associated with the user. A context-aware baseline profile having a same context as the current context profile can be selected, the context-aware baseline profile selected from a plurality of context-aware baseline profiles having different contexts. Features of the current context profile can be compared with corresponding features of the context-aware baseline profile of the user having the same context as the current context profile. A remedial action can be initiated in response to recognizing, based on the comparing, an occurrence of a possible adverse health event affecting the user.

Timely detection and response to context-specific health events

Passively monitoring a user's breathing with a device can include identifying breathing modes of the user's breathing and responsive to detecting a trigger mode based on the identifying, generating an instruction adapted to the trigger mode. The instruction can be conveyed to the user via the device. The monitoring can include determining phases of the user's breathing with the device. Determining the phases can include receiving acoustic signals generated by an acoustic sensor in response to a user's breathing and generating acoustic data comprising features extracted from the acoustic signals. Phases of the user's breathing can be determined by classifying the acoustic data using a machine learning model trained based on signal processing of motion signals generated by a motion sensor in response to human breathing motions. Though trained using signal processing of motion signals, the machine learning model is trained to classify acoustic data.

Breathing measurement and management using an electronic device

A method includes obtaining, by an electronic device, an audio segment comprising one or more audio events of a target subject. The method also includes extracting, by the electronic device, audio embeddings from the one or more audio events using an embedding model, the embedding model comprising a trained machine learning model. The method further includes comparing, by the electronic device, the extracted audio embeddings with a match profile of the target subject, the match profile generated during an enrollment stage. The method also includes generating, by the electronic device, a label for the audio segment based on whether or not the extracted audio embeddings match the match profile, wherein the label enables correlation of the audio segment with the target subject for monitoring a health condition of the target subject.

System and method for passive subject specific monitoring

Adaptive respiratory condition assessment can include capturing signals generated by sensors of a portable device positioned to sense a user's body. The sensors can generate the signals in response to sensor-detected movements of the user's body measured along multiple axes and corresponding to lung activity in the user's body during respiratory cycles. A preferred axis of measurement can be selected using signal processing performed by a signal processor embedded in the portable device. Waveforms generated from signals corresponding to signals generated in response to movements of the user's body measured along the preferred axis can be filtered for extracting biomarkers from the waveforms using the signal processor. The one or more biomarkers extracted correspond to the lung activity. Based on the biomarkers, a respiratory condition of the user can be determined. A notification based on the respiratory condition as determined can be generated and conveyed with the portable device.

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San Jose, CA, United States of America

Tousif Ahmed