Systems and methods for aiding, supplementing, and/or increasing the monitoring capabilities of responsible persons over a supervised person are disclosed herein. In some implementations, the system includes one or more controlling persons, one or more supervising persons, a supervised person, and a cloud server. Each of the controlling persons and the supervising persons can have a personal electronic device. The supervised person can have a wearable electronic device. The electronic devices allow the controlling persons and the supervising persons to actively communicate with the cloud server to upload data related to the supervised person that is accessibly stored in the cloud server. In turn, the wearable device can include one or more sensors to measure and communicate various bioindicators to the cloud server. The system allows the controlling persons to monitor the health of, development of, and/or current control over the supervised person.

The present invention relates to: a device and method for assessing the risk of lonely death using a door open sensor; and a LED system for assessing the risk of lonely death. More specifically, the device for assessing the risk of lonely death using a door open sensor, according to one embodiment of the present invention, comprises: a door open recognition unit which recognizes that a door has been opened; an outside activity time calculation unit which, when the door is recognized to have been opened, and activity of a human body is not detected from the time point when the door is recognized to have been opened to a predetermined time, determines an outside activity state and calculates an outside activity duration which is the time during which the outside activity state is maintained; an inside activity time calculation unit which, when activity of the human body is detected at a first time point, and activity of the human body is detected again at a second time point which includes an idle detection period after the first time point, determines an inside activity state and calculates an inside activity duration which is the time during which the inside activity state is maintained; and a risk assessment unit which assesses the risk of lonely death by generating a probability distribution model on the basis of the outside activity durations and inside activity durations calculated during a first period, and applying, to the probability distribution model, the outside activity duration and inside activity duration calculated after the first period.

It is provided a method for controlling camera-based supervision of a physical space. The method is performed in a supervision controller and comprises the steps of: determining that a person enters the physical space; deactivating camera-based supervision of the physical space based on the person entering the physical space, by turning off a video feed of the camera-based supervision; determining that a person exits the physical space; and activating camera-based supervision of the physical space based on the person exiting the physical space.

A fall prevention system includes an image capturing unit that captures an image of a space to be monitored; a skeleton model generating unit that generates a skeleton model representing a person in the image captured by the image capturing unit; a determination unit that determines a state of a person corresponding to the skeleton model generated by the skeleton model generating unit, by distinguishing between the person standing up and the person sitting down, based on the skeleton model; and an operation processing unit capable of executing a fall prevention process that is a process according to a determination result by the determination unit, and that prevents the person from falling over, based on the determination result.

The purpose of the present invention is to provide a fall risk evaluation system whereby risk of falling of an elderly person or other person to be managed can be easily evaluated on the basis of a captured image of daily life, instead of by a physical therapist, etc. To achieve this purpose, the present invention is a fall risk evaluation system comprising a stereo camera and a fall risk evaluation device, the fall risk evaluation device being provided with: a person authentication unit for authenticating a person to be managed who has been imaged by the stereo camera; a person tracking unit for tracking the person to be managed who is authenticated by the person authentication unit; an action extraction unit for extracting walking by the person to be managed; a feature value calculation unit for calculating a feature value of the walking extracted by the action extraction unit; an integration unit for generating integrated data obtained by integrating the outputs of the person authentication unit, the person tracking unit, the action extraction unit, and the feature value calculation unit; a fall index calculation unit for calculating a fall index value of the person to be managed, on the basis of a plurality of integrated data generated by the integration unit; and a fall risk evaluation unit for comparing the fall index value calculated by the fall index calculation unit and a threshold value to evaluate the risk of falling of the person to be managed.

Various embodiments of a jobsite keep out zone system are provided. The system includes one or more detectors configured to determine when an intruder is within and/or is approaching a protected area. In response to the determination, one or more alarms and/or notifications are generated.

Methods, apparatus and systems for periodic or transient motion detection, e.g. fall event detection, based on wireless signals are described. In one example, a described system comprises: a transmitter configured for transmitting a first wireless signal through a wireless multipath channel of a venue; a receiver configured for receiving a second wireless signal through the wireless multipath channel; and a processor. The second wireless signal differs from the first wireless signal due to the wireless multipath channel that is impacted by a target motion of an object in the venue. The processor is configured for: obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the second wireless signal, computing a time series of spatial-temporal information (STI) of the object based on the TSCI, and detecting the target motion of the object based on the time series of STI (TSSTI).

The present invention relates to a multi-sensing-based vulnerable social group danger recognition detection method of sensing a person being monitored and residence states in real time in a vulnerable social group residence, and, if an analysis result based on the sensed information corresponds to a dangerous situation, transmitting information thereabout to a guardian or related organizations so as to quickly respond thereto. In addition, the present invention is configured to include operations S100 to S800 of receiving sensing information from a sensing means (100) comprised of one or more sensors, analyzing the sensing information, and transmitting, to a central server (300), a dangerous-situation analysis information result of a person being monitored and an alarm signal according thereto.

The systems and methods may use machine learning models to process device data of user devices and determine device usage behaviors for the users of the user devices based on the device data. The systems and methods may provide relatable insights for the device usage behaviors in a user-friendly manner. The systems and methods may provide actional recommendations that users may take in response to the insights provided to promote healthy device usage behaviors or to prevent or reduce the device usage behavior. The systems and methods may also provide recommendations with access to information or other content related to the device usage behavior.

An apparatus for calibration of a bio-information estimation model includes a sensor configured to obtain a bio-signal from an object in a reference interval; a feature extractor, implemented by at least one processor, configure to extract a reference feature value from the bio-signal; and a calibrator, implemented by the at least one processor, configured to determine whether a condition is satisfied based on at least one of the reference feature value and an offset value, and based on determining that the condition is satisfied, configured to calibrate the bio-information estimation model based on the reference feature value and the offset value.