A remote distress monitor includes a steerable microphone array, a memory, and a control system. The steerable microphone array is configured to detect audio data and generate sound data. The memory stores machine-readable instructions. The control system includes one or more processors configured to execute the machine-readable instructions. The generated sound data from the steerable microphone array is analyzed. Based at least in part on the analysis, a token associated with the audio data detected by the steerable microphone array is generated. The audio data is representative of one or more sounds associated with a distress event. The generated token is transmitted, via a communications network, to an electronic device to cause a distress response action to occur. The distress response action is associated with the distress event.

In one embodiment, a method (110) that determines conditions including that a user is located in a monitored area and not wearing a wearable device (112), and provides an alert based on the determinations and an input pattern from one or plural sensors (114).

Various embodiments concern video patient monitoring with detection zones. Various embodiments can comprise a camera, a user interface, and a computing system. The computing system can be configured to perform various steps based on reception of a frame from the camera, including: calculate a background luminance of the frame; monitor for a luminance change of a zone as compared to one or more previous frames, the luminance change indicative of patient motion in the zone; and compare the background luminance to an aggregate background luminance, the aggregate background luminance based on the plurality of frames. If the background luminance changed by more than a predetermined amount, then the aggregate background luminance can be set to the background luminance, luminance information of the previous frames can be disregarded, and motion detection can be disregarded.

An electronic monitoring system implements an evaluation strategy to distinguish between low-interest detected events and high-interest detected events and to send lower or standard alerts to a user device if a detected event is a low-interest event and a heightened alert or warning if the detected event is a high-interest event. The system may utilize patterns of information to establish baseline event characteristics for the particular monitored environment. The baseline event characteristics are used to evaluate triggering events for determining whether those events are best categorized as low-interest or high-interest.

A method and system for detecting a fall risk condition, the system comprising a surveillance camera configured to generate a plurality of frames showing an area in which a patient at risk of falling is being monitored, and a computer system comprising memory and logic circuitry configured to store motion feature patterns that are extracted from video recordings, the motion feature patterns are representative of motion associated with real alarm cases and false-alarm cases of fall events, receive a fall alert from a classifier, determine motion features of one or more frames from the plurality of frames that correspond to the fall alert; compare the motion features of the one or more frames with the motion feature patterns, and determine whether to confirm the fall alert based on the comparison.

Provided is a system used in various facilities, which enables every specific event as a sign of an accident to be detected without missing to ensure transmission of an alert message at a proper time, thereby preventing accidents from occurring. The system includes cameras for capturing images of a monitoring area, and a monitoring server for controlling transmission of an alert message based on the images, wherein the monitoring server is configured to: set a sensing area around an entrance to a risky point (escalator entrance) and a notifying area closer to the risky point than the sensing area; sense a person in the sensing area and detect a specific event associated with the person based on images captured by each camera; and, when the sensed person enters the notifying area, transmits an alert message corresponding to the specific event associated with the person.

A system and method for automatically screening anyone arriving to a facility outside of predetermined entry and exit times is provided. The automated screening system denies entry and detains anyone determined to be a threat. An operator may then take the person denied entry into custody or manually override the system to allow the person access to the facility.

A system includes video cameras arranged to monitor a vulnerable person, and a processor system that receives video frames from the video cameras, the processor system comprising a processor and a non-transitory, computer-readable storage medium having machine instructions executed by the processor. The processor detects and identifies objects in a current received video frame, classifies an identified object as the person by applying a facial recognition algorithm that identifies the person, determines a posture of the person by identifying joints, limbs, and body parts, and their respective orientations to each other and to a plane, and immediately discards the current video frame. The processor then determines a change in motion, of the person, between the current received video frame and one or more prior received video frames, and, based on the determined posture and the change in motion, determines that the person has experienced a defined event. An example method for monitoring health and well-being of persons includes a processor integral to a video camera capturing video frames containing raw image data of a scene containing person objects and non-person objects. The processor, for a first video frame, executes video frame operations, including detecting the person objects in the first video frame, detecting and identifying the non-person objects in the first video frame, identifying and classifying a person object as a person for metric analysis, and determining a posture of the person. The method further includes the processor repeating the video frame operations for second and subsequent video frames; identifying changes in posture of the person from the second and subsequent video frames; using the changes in posture, determining motion of the person; providing person privacy, comprising automatically and immediately following each video frame operation on a video frame, deleting all raw image data of the video frame; and, based on the posture changes and the motion, generating a private, personalized metric analysis for the person. In an aspect, the private, personalized metric analysis includes outcome measures of interest selected from a group consisting of steps taken/distance walked/changes in gait by elderly persons, positional changes for bedridden persons, and developmental milestones in child persons; and the processor conducts the metric analysis over time to identify trends and corresponding changes in a health condition of the person, comprising progress in rehabilitation, deterioration from a degenerative disease, and physical and mental development.

A system and method for patient movement detection and fall monitoring to address the need to proactively monitor patients to detect abnormal movements, in-room activity, and other movements associated with providing in-room care. The system comprises an environmental model which can be used to track the position and movement of a patient and a classifier network configured to receive movement data and classify a patient's movement as normal or abnormal movement. In addition to monitoring in-room activity, the system and method create safe zones within the room to ensure patients are proactively monitor in the event of a seizure, fall, or other unintended activity. The system will record and store in-room video in a secure environment. Videos and notifications are automatically sent to designated staff as events occur.

Systems and methods for improved operations of ski lifts increase skier safety at on-boarding and off-boarding locations by providing an always-on, always-alert system that “watches” these locations, identifies developing problem situations, and initiates mitigation actions. One or more video cameras feed live video to a video processing module. The video processing module feeds resulting sequences of images to an artificial intelligence (Al) engine. The AI engine makes an inference regarding existence of a potential problem situation based on the sequence of images. This inference is fed to an inference processing module, which determines if the inference processing module should send an alert or interact with the lift motor controller to slow or stop the lift.