A system and method for automatically requesting assistance for a user experiencing a medical emergency is disclosed. The system includes a user device with a sensor to detect sensed information. Based on the sensed information the user device can determine if the user is experiencing a medical emergency. The user device can communicate with nearby devices over one or more networks and send information about the medical emergency to the nearby devices. The information can include the type of medical emergency, the location of the user experiencing the medical emergency, and any instructions for providing the user with assistance.

Embodiments relate to using an active reflected wave detector to classify the state of a person in an environment and optionally respond accordingly. In one embodiment there is provided a computer implemented method of determining a state of a person comprising: receiving an output of an active reflected wave detector; classifying a state of the person as being in a safe supported state based on the output using measurements of reflections associated with the person, wherein said classifying is based at least on: a height metric associated with at least one reflection from the person conveyed in the output of the active reflected wave detector; and a plurality of velocity magnitude measurements of the person corresponding to different times, each of said velocity magnitude measurements determined using the reflections associated with the person conveyed in the output of the active reflected wave detector.

A system to search in a low visibility environment comprising: an infrared (IR) sensor and a radar unit to receive measurements in a first field of view; a microphone to receive audio measurements in a second field of view; processing circuitry to determine presence of an animal and/or human in the first field of view using a first machine learning engine trained based upon the IR image measurements and/or using a second machine learning engine trained based upon the radar image measurements; determine presence of an animal and/or human in the second field of view using a third machine learning engine trained based upon the audio measurements; providing a first digital transmission indicating whether a human and/or animal is detected within the first field of view; and providing a second digital signal indicating whether a human and/or animal is detected within the second field of view.

Various embodiments concern a system for monitoring a patient in a bed, the system comprising a camera, a user interface comprising a screen, and a computing system. The computing system can be configured to monitor, with the camera, for motion within each of a plurality of zones, the plurality of zones comprising a one or more inner zones extending adjacent along the bed, one or more outer zones extending adjacent along the one or more inner zones, and one or more end zones. Motion within the inner zones can trigger a fall alert while motion within the outer or end zones can suspend one or more functions to prevent the fall alert from being issued. The end zones can comprise a bottom zone and one or more top zones.

A method and system for detecting a person falling, confirming a potential fall, and taking action to mitigate the effects of a fall.

A laser monitoring system designed to prevent accidental falls of people who are at high risk of falling when they try to get up from a chair, commode, or bed. The system includes a first unit providing a circuit board, a laser emitting device, an alignment indicator, a control button, a communicator, an alarm speaker, an electrical port, and an electrical housing that retains all of the above. The system also includes a second unit providing a circuit board, a laser receiving device, an alignment indicator, and receiver which are all retained inside of an electrical housing. The first unit and second unit form a laser net alarm system that when interrupted provides an audible alarm requesting that the patient wait for medical attention, while at the same time may notify the care giver that their patient is trying to get up unassisted by sending an electronic message to the care giver's electronic device.

A disclosed method includes receiving at least an indication of audio data generated based on sound captured by a microphone of a monitoring device. A set of audio event labels are generated by processing the received audio data with a model that includes audio recognition patterns. Each audio event label is generated by matching an audio pattern in the received audio data with an audio pattern of the audio recognition patterns. The method further includes identifying a hazard event type occurring within a time window of the time period by processing the set of audio event labels with a hazard detection model. In response to detecting the hazard event type, a hazard alert is generated and at least an indication of the hazard alert is communicated to an electronic device other than the monitoring device, which is authorized to receive communications from the monitoring device.

An embodiment in accordance with the present invention includes a technology to continuously measure patient mobility automatically, using sensors that capture color and depth images along with algorithms that process the data and analyze the activities of the patients and providers to assess the highest level of mobility of the patient. An algorithm according to the present invention employs the following five steps: 1) analyze individual images to locate the regions containing every person in the scene (Person Localization), 2) for each person region, assign an identity to distinguish ‘patient’ vs. ‘not patient’ (Patient Identification), 3) determine the pose of the patient, with the help of contextual information (Patient Pose Classification and Context Detection), 4) measure the degree of motion of the patient (Motion Analysis), and 5) infer the highest mobility level of the patient using the combination of pose and motion characteristics (Mobility Classification).

An embodiment in accordance with the present invention includes a technology to continuously measure patient mobility automatically, using sensors that capture color and depth images along with algorithms that process the data and analyze the activities of the patients and providers to assess the highest level of mobility of the patient. An algorithm according to the present invention employs the following five steps: 1) analyze individual images to locate the regions containing every person in the scene (Person Localization), 2) for each person region, assign an identity to distinguish ‘patient’ vs. ‘not patient’ (Patient Identification), 3) determine the pose of the patient, with the help of contextual information (Patient Pose Classification and Context Detection), 4) measure the degree of motion of the patient (Motion Analysis), and 5) infer the highest mobility level of the patient using the combination of pose and motion characteristics (Mobility Classification).

Aspects herein are directed to an apparel thermo-regulatory system that actively heats or cools a wearer. The apparel thermo-regulatory system comprises an apparel item, a dimensionally stable frame comprising at least one aperture that is affixed to an outer-facing surface of the apparel item at a predetermined location, an absorbent material applied to an exposed face of the dimensionally stable frame, and at least one thermoelectric module that is releasably positioned within the aperture of the dimensionally stable frame.